CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Application No. 62/384,783, which was filed on 08-September-2016, and the entire disclosure of which is incorporated herein by reference. This application also is related to U.S. Patent Application No. 14/854,828, which was filed on 15-September-2015, and the entire disclosure of which is incorporated herein by reference.

BACKGROUND

[0002] Pipelines carry pressurized fluids, such as hydrocarbon gases and oils, slurries, water, and/or the like, for many times tens of kilometers between pumping stations. The pipeline may be exposed to extreme weather that includes a corrosive atmosphere, exposed to alkaline or acidic content in the soil, exposed to external forces from the soil or other sources, manufacturing defects, or the like. Additionally, the contents carried within the pipeline may not be corrosive and/or abrasive.

[0003] Pipeline operators are responsible for monitoring pipelines and detecting leaks so that pipelines can be shut down before leaks cause harm to safety and/or the environment. Pipeline operators use a variety of leak detection systems (LDS) to monitor and detect leaks of the pipeline such as a computational pipeline monitoring (CPM) mass balance; CPM real-time transient monitoring; fiber optic sensing of acoustic, temperature, and strain; or the like. Conventional leak detection systems have a plurality of issues. Conventional LDSs have limited sensitivity, may miss some leaks and emit false alerts. The pipeline operator utilizes his or her working knowledge of the pipeline, along with LDSs to make assessments and decisions about responses to leak alerts.

[0004] Conventionally, the pipelines may include multiple LDS that each are independent from each other using different sensing modalities coupled with specialized analytics that can provide conflicting and/or non-definitive information. Each of the LDS are used independently. However, the different LDS may issue alerts and/or severity values that are not comparable with each other. With multiple LDSs, the pipeline operator is faced with making sense of and reconciling alerts and other data from each LDS. Since the different LDS operate in very different ways this can be challenging. Further, operators may have varying levels of expertise with each LDS. Additionally, conventional methods to train, test and/or perform information fusion on the various LDSs require data under both "no leak" and "leak" conditions. The "leak" conditions might be created by a real historical leak, or by simulation of leak conditions. Leaks in pipelines are very rare, and leak simulations are expensive and/or unrealistic.

BRIEF DESCRIPTION

[0005] In one embodiment, a system (e.g., monitoring system) is provided. The system includes a communication circuit configured to obtain monitoring measurements measured by a plurality of leak detection systems (LDS) along corresponding communication links. The system includes a controller circuit communicatively coupled to the communication circuit. The controller circuit is configured to calculate a statistical model for each of the LDS based on a series of monitoring measurements received for a period of time, determine a severity frequency indicator (SFI) based on the statistical model and first monitoring measurements acquired by the plurality of LDS, and a display configured to display the SFI.

[0006] In one embodiment, a method (e.g., for monitoring a pipeline) is provided. The method includes obtaining monitoring measurements measured by a plurality of leak detection systems, calculating a statistical model for each of the LDS based on a series of monitoring measurements received for a period of time, determining a severity frequency indicator (SFI) based on the statistical model and first monitoring measurements acquired by the plurality of LDS, and displaying the SFI on a display. [0007] In one embodiment, a system (e.g., monitoring system) is provided. The system includes a communication circuit configured to obtain monitoring measurements measured from a first and second leak detection systems (LDS) along corresponding communication links. The system includes a controller circuit communicatively coupled to the communication circuit. The controller circuit is configured to calculate a first statistical model for the first LDS and a second statistical model for the second LDS. The first statistical model and the second statistical model are based on a series of monitoring measurements received for a period of time. The controller circuit is configured to determine a first severity frequency indicator (SFI) based on the first statistical model and a first monitoring measurement acquired by the first LDS and a second SFI based on the second statistical model and a second monitoring measurement acquired by the second LDS. The system includes a display configured to display the first and second SFI.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is an illustration of a pipeline network of an embodiment.

[0009] Figure 2 is a schematic illustration of a monitoring system, in accordance with various embodiments.

[0010] Figure 3 is a functional block diagram of an embodiment for determining a severity frequency indicator from a leak detection system monitoring a segment of a pipeline.

[0011] Figure 4 is a graphical illustration of an embodiment of a statistical model.

[0012] Figure 5 is an illustration of a gauge of a severity frequency indicator of an embodiment.

[0013] Figure 6 is a functional block diagram of an embodiment for determining a severity frequency indicator having more than one statistical model. [0014] Figure 7 is a functional block diagram of an embodiment for determining a fusion severity frequency indicator from multiple leak detection systems monitoring a segment of a pipeline.

[0015] Figure 8 is a functional block diagram of an embodiment for determining a fusion severity frequency indicator from multiple leak detection systems monitoring different segments of a pipeline.

[0016] Figure 9 is a functional block diagram of an embodiment for determining a fusion severity frequency indicator from multiple correlated leak detection systems monitoring a segment of a pipeline.

[0017] Figure 10 is a functional block diagram of an embodiment.

[0018] Figures 11A-B are illustrations of gauges of severity frequency indicators of an embodiment.

DETAILED DESCRIPTION

[0019] Various embodiments will be better understood when read in conjunction with the appended drawings. To the extent that the figures illustrate diagrams of the functional blocks of various embodiments, the functional blocks are not necessarily indicative of the division between hardware circuitry. Thus, for example, one or more of the functional blocks (e.g., processors, controllers or memories) may be implemented in a single piece of hardware (e.g., a general-purpose signal processor or random access memory, hard disk, or the like) or multiple pieces of hardware. Similarly, any programs may be stand-alone programs, may be incorporated as subroutines in an operating system, may be functions in an installed software package, and the like. It should be understood that the various embodiments are not limited to the arrangements and instrumentality shown in the drawings. [0020] As used herein, the terms "system," "unit," or "module" may include a hardware and/or software system that operates to perform one or more functions. For example, a module, unit, or system may include a computer processor, controller, or other logic-based device that performs operations based on instructions stored on a tangible and non- transitory computer readable storage medium, such as a computer memory. Alternatively, a module, unit, or system may include a hard-wired device that performs operations based on hard-wired logic of the device. The modules or units shown in the attached figures may represent the hardware that operates based on software or hardwired instructions, the software that directs hardware to perform the operations, or a combination thereof. The hardware may include electronic circuits that include and/or are connected to one or more logic-based devices, such as microprocessors, processors, controllers, or the like. These devices may be off-the-shelf devices that are appropriately programmed or instructed to perform operations described herein from the instructions described above. Additionally or alternatively, one or more of these devices may be hard-wired with logic circuits to perform these operations.

[0021] As used herein, an element or step recited in the singular and proceeded with the word "a" or "an" should be understood as not excluding plural of said elements or steps, unless such exclusion is explicitly stated. Furthermore, references to "one embodiment" are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Moreover, unless explicitly stated to the contrary, embodiments "comprising" or "having" an element or a plurality of elements having a particular property may include additional such elements not having that property.

[0022] Generally, various embodiments provide methods and systems for monitoring a pipeline network for the real-time analysis, fusion, and presentation of information obtained from a plurality of leak detection systems. A monitoring system, in accordance with various embodiments, is configured to determine one or more severity frequency indicators (SFI) based on monitoring measurements received from the plurality of LDSs. The SFIs represent a severity of the output of individual LDSs and/or combinations of LDSs. The SFI is configured to provide a unified presentation and understanding of the severity, relevance, and importance of information from each LDS, even for LDSs using completely different sensing modalities. The SFI works for individual LDSs, groups of different LDSs, LDSs with multiple different algorithms that provide corresponding outputs, and LDSs that simultaneously monitor multiple pipeline segments and provide corresponding outputs.

[0023] For each LDS (or LDS sub-component), the monitoring system is configured to calculate a statistical model (SM) that represents a distribution of the LDS measurement values based on historical LDS measurement data. Since actual leaks in pipelines are quite rare, the SM represents the distribution of the historical output value under generally no- leak conditions. When the LDS generates a measurement value, the SM is used to determine a tail probability of the measurement value. The tail probability is the probability that the LDS historically outputs a value at least as large as the current output value. Based on the tail probability, the monitoring system is configured to determine the SFI. The SFI represents an expected rate over time at which the LDS outputs a value at least as great as the current output value.

[0024] For example, the SFI for a select LDS output value is "2.5 per month," which means the LDS typically generates a measurement value at least as large at frequency of 2.5 times per month, on average. The monitoring system is configured to display the SFI as a gauge (e.g., dial), graphical icon, text, a numerical value, on a plot, and/or the like. Additionally or alternatively, each SFI can generate an alert responsive to the severity frequency exceeding a predetermined threshold. The SFI provides pipeline operators with an on-line real-time unified gauge and scale for each individual LDS and for groups of LDSs. The SFI provides a unified understanding of the severity or risk indicated by individual LDSs and by pairs or groups of LDSs taken collectively. Pipeline operators can then evaluate the information from LDSs more quickly and effectively. The SFI approach requires no manual parameter setting for its underlying analytics. The SFI approach makes no assumptions about the statistics or behavior of an LDS. This makes the approach robust and reduces or minimizes the possibility of producing an unexpected result. The statistical models that drive the SFIs can be adaptive to individual LDSs in specific applications and automatically tune themselves. A statistical model can be trained on historical data from an LDS. A statistical model can be trained on data from an LDS as the data continuously arrives. A statistical model can adapt to permanent or long-term pipeline operation changes by using a forgetting factor. The SFI approach requires no "leak data." That is, it does not require LDS output data from periods of actual or simulated pipeline leaks. SFI gives operators a direct mechanism by which to control LDS false alert rates. For example, if the desired false alert rate for a specific LDS system is twice per week, then twice per week is used as a threshold for the SFI. Any SFI value at or beyond twice per week then results in an alert.

[0025] At least one technical effect of various embodiments includes a single consistent representation of the importance of each LDS and the fusion of the collective LDSs as a SFI. At least one technical effect of various embodiments includes calculating a single SFI for a variety of simple and complex combinations of LDSs. At least one technical effect of various embodiments includes providing a unified indicator (e.g., SFI) of a severity and/or risk indicated by individual LDSs and by pairs or groups of LDSs taken collectively. At least one technical effect of various embodiments includes a faster and/or more effective method to inform a pipeline operator and/or user of a structural and/or operational status of the pipeline. At least one technical effect of various embodiments includes calculating statistical models adaptive to individual LDSs in specific applications and automatically tune themselves. At least one technical effect of various embodiments includes a unified indicator (e.g., SFI) that does not require LDS monitoring measurements and/or simulated data from periods of actual or simulated pipeline leaks

[0026] Figure 1 is an illustration of a pipeline network 100 of an embodiment. The pipeline network 100 includes a pipeline 102, a monitoring system 150, and a plurality of leak detection systems (LDS) 110-112 communicatively coupled to one or more sensors 120- 122 positioned along and/or within the pipeline 102. The sensors 120-122 may be configured to acquire pressure data (e.g., piezoresistive strain gauge, capacitive sensor, resonant sensor, optical sensor, and/or the like), temperature data (e.g., thermistor, thermocouple, and/or the like), flow data (e.g., piston meter, turbine flow meter, current meter, optical flow meter, and/or the like), vibration data (e.g., accelerometer, vibration sensor, and/or the like), acoustic data (e.g., capacitive sensor, microphone, and/or the like), and/or the like. The pipeline 102 is configured to contain a pressurized fluid (e.g., hydrocarbon gases and oils, slurries, water, and/or the like) that is propelled within the pipeline 102 for a distance such as tens to hundreds of kilometers. A structure of the pipeline 102 may be cylindrical and comprised of an electrically conductive material such as aluminum, steel, copper, and/or the like. The pipeline 102 may be monitored by the plurality of LDS 110-112. The LDS 110-112 are configured to utilize the sensors 120-122 and process sensor data acquired by the sensors 120-122 and received by the LDS 110-112 to detect leaks and/or generate monitoring measurements of the pipeline 102.

[0027] Each of the LDSs 110-112 may be configured to generate an output representing monitoring measurements. The LDS 110-112 may be a computational pipeline monitoring (CPM) mass balance, a CPM real-time transient monitoring, fiber optic sensing (e.g., for sensing acoustic characteristics, temperature, strain), and/or the like. An example of the LDS 110-112 is illustrated and described as a system 100 in U.S. Patent Application No. 14/854,828.

[0028] The LDS 110-112 utilize different sensing modalities, which are processed with specialized analytics. Each of the LDSs 110-112 is configured to monitor the pipeline 102 and detect leaks. For example, the LDS 110 may acquire sensor measurements related to pressure, flow, and/or temperature to estimate a hydraulic behavior of the pressurized fluid traversing within the pipeline 102. When the LDS 110 estimates the hydraulic behavior, the LDS 110 may determine if an anomaly is present representing a leak. When a leak is detected, one or more of the LDSs 110-112 may issue an alert. It may be noted that each of the LDSs 110-112 may not be compatible with each other. For example, the LDSs 110- 112 may each issue alerts and severity values that are not comparable with each other. [0029] The monitoring measurements of the LDSs 110-112 are generated utilizing internal computation and/or processing of the LDSs 110-112. The monitoring measurements may correspond to a test statistic representing a characteristic of the pressurized fluid traversing within the pipeline 102, the pipeline 102, one or more physical quantities, and/or the like based on measurements by one or more sensors of the LDSs 110-112. For example, the monitoring measurements is typically a single floating point number that is produced at a sampling rate (e.g., time interval), such as once per minute. The sampling rate represents an amount of time the LDS 110-112 requires to acquire the monitoring measurement. The monitoring measurement may include a mass balance integration (e.g., from a CPM mass balance system), a leak probability, a severity level as a percentage, and/or the like. Optionally, if the value of the monitoring measurements is above an alert threshold, an alert is issued by the LDS 110-112. Additionally or alternatively, the LDS 110-112 may have multiple thresholds for different alert levels, such as a warning level, caution level, or high level.

[0030] It may be noted, the monitoring measurements generated by one or more of the LDS 110-112 may be a discrete value (e.g., binary value). For example, the monitoring measurement may be a fixed set of values, such as low, medium, and/or high. In another example, the monitoring measurement may be a binary value representing no leak and/or leak. The monitoring system 150 may be configured to translate the discrete values into numerical values. For example, a controller circuit (e.g., a controller circuit 202) of the monitoring system 150 may convert the discrete values such that a value of the numerical value represents a severity, likelihood, and/or risk of a leak. In another example, the controller circuit of the monitoring system 150 may utilize a Bernoulli model and/or Bernoulli distribution to translate the discrete value to determine the SFI of the LDS 110- 112.

[0031] An LDS may generate multiple output values that are discrete and continuous. For example, an LDS may issue one output that is a binary alert ("no leak," or "leak") and a second output that is a floating-point severity value, but is issued only if the binary output is "leak." The SFI system can work in this situation as well. The binary and floating point output values are combined into a single mixed discrete/floating point value. The mixed output is set to a very low floating point value if the binary output is "no leak," and is set to the given floating point severity output value is the binary output value is "leak." The low floating point value used for "no leak" should be lower than all possible floating point severity values if possible. The resulting mixed discrete/floating point output value may be modeled by an SM that uses an empirical distribution function.

[0032] While the SFI system works for discrete as well as floating point output values, performance of any monitoring and fusion system is improved when floating point output values are available from LDSs.

[0033] In connection with Figure 2, the monitoring system 150 may receive the monitoring measurements from the plurality of the LDS 110-112 along corresponding communication links formed by a communication circuit 210.

[0034] Figure 2 is a schematic illustration of the monitoring system 150 of an embodiment. The monitoring system 150 includes a controller circuit 202, a communication circuit 210, a display 208, a memory 204, and a user interface 206. These components may communicate with each other via wired and/or wireless connections. Additionally or alternatively, the monitoring system 150 may include one or more components in addition to the listed components and/or one or more of the listed components may be included on a remote system that is communicatively coupled to the monitoring system 150.

[0035] The communication circuit 210 may include a transceiver, a transmitter and receiver, and/or the like. The communication circuit 210 may be electrically coupled to an antenna 212, for example, the communication circuit 210 is configured to wirelessly communicate, bi-directionally and/or uni-directional, with off-board locations, such as the LDS 110-112, a remote system (e.g., pumping station), and/or the like along one or more communication links. For example, the communication circuit 210 may obtain and/or receive the monitoring measurements measured by the plurality of LDS 110-112 along corresponding communication links. Additionally or alternatively, the communication circuit 210 may be communicatively coupled to the LDS 110-112, the remote system, and/or the like along a wired communication link.

[0036] The user interface 206 is configured to receive input information from one or more users. The user interface 206 may include a keyboard, a mouse, a hand-held device (e.g., cell phone, tablet, PDA, etc.), touchscreen, and/or a graphical user interface of the display 208. The user interface 206 may be communicatively coupled to the controller circuit 202, which receives the input information from the user interface 206 for processing.

[0037] The display 208 may be an LCD (liquid crystal display), plasma display, CRT monitor, and/or the like. Optionally, the display 208 may include a touch sensitive surface (e.g., sensor or set of sensors that accepts input from a user based on haptic and/or tactile contact) which may be used as a part of the user interface 206. For example, the display 208 may display a graphical user interface which is interfaced by the user by interacting with the touch sensitive surface of the display 208.

[0038] The controller circuit 202 controls the operation of the monitoring system 150. The controller circuit 202 may be embodied in hardware, such as one or more processors, controller, or other logic-based device, that performs functions or operations based on one or more sets of instructions (e.g., software). The instructions on which the hardware operates may be stored on a tangible and non-transitory (e.g., not a transient signal) computer readable storage medium, such as the memory 204. The memory 204 may include one or more computer hard drives, flash drives, RAM, ROM, EEPROM and/or the like. Alternatively, one or more of the sets of instructions that direct operations of the hardware may be hard-wired into the logic of the hardware.

[0039] The controller circuit 202 may calculate a statistical model for each of the LDS 110-112 based on the series of monitoring measurements received for the period of time. Additionally or alternatively, the controller circuit 202 may calculate a statistical model representing more than one LDS 110-112. The statistical model may represent a statistical distribution function of the series of the monitoring measurements acquired by the LDS 110-112. For example, the statistical model may be an empirical distribution function, an empirical distribution function with Pareto tails, an exponentially weighted moving average of Gaussian distribution statistics, and/or the like. The statistical model may be configured by the controller circuit 202 to have a fixed distribution set using priori information stored in the memory 204. For example, the distribution may be set to be Gaussian with a known mean and variance. Optionally, the statistical model may learn the distribution using historical training data and/or machine learning. For example, the controller circuit 202 may adapt the statistical model utilizing a machine learning algorithm (e.g., cluster analysis) to refine the distribution incrementally, as new monitoring measurements are received by the LDS 110-112.

[0040] Based on the series of monitoring measurements of the LDS 110-112, the controller circuit 202 may define and/or train the statistical model for each LDS 110-112 as the values are received by the monitoring system 150. It may be noted a selection of the monitoring measurements of the series utilized by the controller circuit 202 for defining and/or training the statistical model affects the distribution. For example, the controller circuit 202 may define the statistical model utilizing a series of historical monitoring measurements received by the LDS 110-112 over a period of time. The period of time may be based on a number of monitoring measurements received from the LDS 110-112. Optionally, the period of time may be based on the sampling rate of the LDS 110-112. Additionally or alternatively, the period of time may be temporally based such as a number of days, months, and/or the like. Optionally, one or more of the monitoring measurements utilized within the series by the controller circuit 202 to define the statistical model may be discarded and/or expired (e.g., forgetting factor) after a predetermined expiration threshold.

[0041] For example, the controller circuit 202 may define a number of monitoring measurements within a series based on the predetermined expiration threshold. When the number of monitoring measurements of the series reaches the predetermined expiration threshold, the controller circuit 202 may replace and/or discard monitoring measurements (e.g., oldest in time) in the series utilized to define the statistical model with a newly obtained subsequent monitoring measurement from the LDS 110-112 acquired by the monitoring system 150. The controller circuit 202 may include the newly obtained subsequent monitoring measurement in the series and retrain and/or redefine the statistical model.

[0042] Additionally or alternatively, the predetermined expiration threshold may represent a period of time of what monitoring measurements are included within the series. For example, the controller circuit 202 may only include monitoring measurements acquired during the period of time obtained by the LDS 110-112 and/or the monitoring system 150 are used within the series for defining and/or training the statistical model. Optionally, the controller circuit 202 may be configured to define and/or train the statistical model based on a maximum or other upper limit of a monitoring measurement value over a fixed-size non-overlapping blocks of monitoring measurement values over time.

[0043] Figure 3 is a functional block diagram 300 of an embodiment for determining an SFI from the LDS 110 a segment of the pipeline 102. For example, the LDS 110 may continually transmit monitoring measurements along a communication link 306 to the monitoring system 150 based on the sampling rate of the LDS 110, which is received by the controller circuit 202 via the communication circuit 210. Based on a series of the monitoring measurements received by the controller circuit 202, the controller circuit 202 may calculate a statistical model 302 as shown in Figure 4.

[0044] Figure 4 is a graphical representation 400 of an embodiment of the statistical model 302. The statistical model 302 is shown as a Gaussian curve with an amplitude along a vertical axis 406 representing a statistical probability of a value of the monitoring measurement based on the sampling rate (e.g., timing interval) of the LSD 110. The statistical model 302 is plotted along a horizontal axis 404 representing corresponding values of the monitoring measurements. [0045] Returning to Figure 3, the controller circuit 202 may determine an SFI 304 based on the statistical model 302. For example, the controller circuit 202 may determine the SFI 304 based on a tail probability 410 of a received value 408 (Figure 4) of the monitoring measurement received from the LSD 110. For example, the statistical model 302 may be defined and/or trained by the controller circuit 202 based on a series monitoring measurements obtained by the LDS 110. The controller circuit 202 may obtain the monitoring measurement of the value 408 from the LDS 110. The controller circuit 202 may determine the tail probability 410 based on the value 408 with respect to the statistical model 302. The tail probability 410 representing a probability value of the monitoring system 150 obtaining and/or receiving a monitoring measurement from the LDS 110 at the value 408 and/or a greater value (e.g., in a direction of the arrow 412). For example, the tail probability 410 may be a sum of the probabilities of the statistical model 302 from the value 408 along the statistical model 302 in the direction of the arrow 412.

[0046] The controller circuit 202 may utilize the tail probability 410 based on the value 408 of the monitoring measurement to determine the SFI 304. The SFI 304 represents a severity frequency, which is a frequency with which the LDS 110, or fused set of LDS 110- 112, has a monitoring measurement as severe as the value 408. The SFI 304 may include a temporal component representing how frequently the LDS 110 acquires the value 408 and/or greater of the monitoring measurement based on the sampling rate (e.g., time interval) of the LDS 110. For example, the SFI 304 may be five per hour, 1.2 per week, 2.5 per month, 5.1 per year, and/or the like. The SFI 304 is configured to expresses a severity of the monitoring measurement by being configured to convey to a user a frequency and/or likelihood the value of the monitoring measurement acquired by the LSD 110 occurs. For example, the controller circuit 202 may determine the SFI 304 is twice per month, which indicates the value 408 of the monitoring measurement represented by the tail probability 410 can occur twice a month.

[0047] Additionally or alternatively, the controller circuit 202 may be configured to calculate a confidence value of the tail probability 410, such as a confidence interval. For example, the confidence value may be based on a confidence interval of the statistical model 302 utilizing a binomial proportion parameter estimation, including the Wilson approximation, and/or the like. The controller circuit 202 may determine the confidence value based on a number of monitoring measurements within the series of monitoring measurements utilized to define the statistical model 302, a standard deviation of the statistical model, a mean of the series of monitoring measurements, and/or the like. For example, the controller circuit 202 may determine the tail probability 410 has a value 0.072 having a confidence interval +/- 0.021 with a 95% confidence. Optionally, the confidence value may include a lower and/or upper confidence bounds configured to indicate an accuracy of the tail probability 410. In another example, the controller circuit 202 may determine the tail probability 410 is 0.052 with 95% confidence having a confidence interval with a lower confidence bound of 0.027 and an upper confidence bound of 0.061. Additionally or alternatively, the confidence value may represent a standard deviation. For example, the controller circuit 202 may determine the tail probability 410 of 0.072 with a standard deviation of 0.019. It may be noted in connection with Figures 7-9, the confidence values of the tail probability uncertainty from individual LDS 110-112 may be carried through fusion operations executed by the controller circuit 202.

[0048] The controller circuit 202 may be configured to calculate a confidence value of the SFI 304. The confidence value of the SFI 304 may be based on the confidence interval of the tail probability 410. Optionally, the confidence value may include a lower and/or upper confidence bounds configured to indicate an accuracy of the SFI 304. Additionally or alternatively, the confidence value may represent a range values with the SFI 304.

[0049] It may be noted, the monitoring measurements generated by one or more of the LDS 110-112 may be a discrete value (e.g., binary value). For example, the monitoring measurement may be a fixed set of values, such as low, medium, and/or high. In another example, the monitoring measurement may be a binary value representing no leak and/or leak. The monitoring system 150 may be configured to translate the discrete values into numerical values. For example, a controller circuit (e.g., a controller circuit 202) of the monitoring system 150 may convert the discrete values such that a value of the numerical value represents a severity, likelihood, and/or risk of a leak. In another example, the controller circuit of the monitoring system 150 may utilize a Bernoulli model and/or Bernoulli distribution to translate the discrete value to determine the SFI of the LDS 110- 112.

[0050] A LDS may generate multiple output values that are discrete or continuous. For example, an LDS may issue one output that is a binary alert ("no leak," or "leak") and a second output that is a floating point severity value, but is issued only if the binary output is "leak." The SFI system can work in this situation as well. The binary and floating point output values are combined into a single mixed discrete/floating point value. The mixed output is set to a very low floating point value if the binary output is "no leak," and is set to the given floating point severity output value is the binary output value is "leak." The low floating point value used for "no leak" should be lower than all possible floating point severity values if possible. The resulting mixed discrete/floating point output value may be modeled by an SM that uses an empirical distribution function.

[0051] The SFI 304 can be displayed on the display 208 as text, a graphical icon, a numerical value, a graph (e.g., utilizing a linear scale or log scale), and/or the like. Additionally or alternatively, in connection with Figure 5, the SFI 304 may be represented as a gauge 502 shown on the display 208.

[0052] Figure 5 is an illustration of the gauge 502 of the SFI 304 of an embodiment. The gauge 502 includes a needle and/or graphical indicator 506 indicating a value of the SFI 304 relative to a frequency scale 504. The frequency scale 504 may represent a range of possible values of the SFI 304. The gauge 502 may include a threshold indicator 508. The threshold indicator 508 may represent a portion of the frequency scale 504 that is above a predetermined threshold stored in the memory 204. Additionally or alternatively, the display 208 may include LDS information 510 based on the monitoring measurements of the LDS 110. For example, the LDS information 510 may display the monitoring measurement and/or other outputs of the LDS 110 shown concurrently with the gauge 502. Additionally or alternatively, the controller circuit 202 may display other metrics within the LDS information 510, such as a leak confidence, probability of a leak, risk of a leak, and/or the like based on statistical models known in the art. The metrics may be displayed numerically, using percentage, a range or units, and/or the like. Optionally, the metrics may also be displayed using discrete categories, instead of numerically, such as low, medium, high and/or the like.

[0053] Optionally, the gauge may display the confidence value of the SFI 304 and/or the tail probability. For example, the controller circuit 202 may generate a shaded region proximate to the graphical indicator 506 representing a lower and/or upper bound of the SFI 304 corresponding to the confidence value. In another example, a thickness of the graphical indicator 506 may include range of the SFI 304 that includes the confidence value.

[0054] Additionally or alternatively, in connection with Figure 6, the controller circuit 202 may determine a plurality of statistical models 602-604 for an LDS (e.g., the LDS 110). For example, the monitoring measurements of the LDS 110 may depend on operating conditions of the pipeline 102.

[0055] Figure 6 is a functional block diagram 600 of an embodiment for determining the SFI 304 having a plurality of statistical models 602-604. For example, the LDS 110 may be a CPM mass balance LDS the operation of which depends on the operation of pipeline pumps and/or valves of the pipeline 102. The operation of the pumps and/or valves, for example, may cause transient fluid flow conditions within the pipeline 102, that may cause a behavior and/or monitoring measurement of the CPM mass balance LDS to be unreliable. Based on the unreliability of the CPM mass balance LDS during operation of the pumps and/or valves, the CPM mass balance LDS may utilize dynamic thresholding. The dynamic thresholding of the CPM mass balance LDS is configured to adjust an alert threshold of the CPM mass balance LDS up and/or down depending on the pump and/or valve actions of the pipeline 102 that define an operating condition class of the CPM mass balance LDS for an operation time interval. The dynamic threshold of the operating condition classes may be stored in a look-up table stored in the CPM mass balance LDS and/or the memory 204. It may be noted that alternative LDSs (e.g., the LDS 111-112) may have operating condition classes based on different parameters of an operation and/or environment of the pipeline 102. For example, at least one of the LDS 111-112 may utilize fiber optic sensing that has different operating condition classes with dynamic thresholding based on weather conditions, roadway traffic conditions, the operation of equipment proximate to the pipeline 102, and/or the like.

[0056] Based on the operating condition classes of the LDS 110, the controller circuit 202 may define different statistical models 602-604 representing an operating condition class. For example, the LDS 110 has three defined operating condition classes. The controller circuit 202 may define and/or train the statistical models 602-604 for each operating condition class of the LDS 110. The controller circuit 202 may select different series of monitoring measurements of the LDS 110 based on the operating condition class of the LDS 110. For example, during a first operating condition class of the LDS 110 the controller circuit 202 may define the statistical model 602. In another example, during a second operating condition class of the LDS 110 the controller circuit 202 may define the statistical model 603. For example, during a third operating condition class of the LDS 110 the controller circuit 202 may define the statistical model 604. When the controller circuit 202 determines the SFI 304 (Figure 6), the controller circuit 202 may utilize the statistical model 602-604 corresponding to the operating condition class of the LDS 110.

[0057] Additionally or alternatively, in connection with Figures 7-9, the controller circuit 202 may determine a combined and/or fusion SFI 722, 804, 906 based on a plurality of LDS 110-112.

[0058] Figure 7 is a functional block diagram 700 of an embodiment for determining a fusion SFI 722 from multiple LDS 110-112 monitoring a segment of a pipeline. For example, the LDS 110-112 may continually transmit monitoring measurements along corresponding communication links 750-752 to the monitoring system 150 based on the respective sampling rates of the LDS 1 10-112. The monitoring measurements of each LDS 110-112 are received by the controller circuit 202 via the communication circuit 210 of the monitoring system 150. The controller circuit 202 selects a series of the monitoring measurements for each LDS 110-112 to calculate corresponding statistical models 702-704 for each of the LDS 110-112. The controller circuit 202 determines corresponding SFI 710-712 of the LDS 110-112 based on the statistical models 702-704 and a select monitoring measurement of the LDS 110-112, for example, similar to and/or the same as the SFI 304.

[0059] The controller circuit 202 may determine a combined and/or fusion SFI 722 based on of more than one LDS 110-112. The LDS 110-112 may be independent from each other while monitoring the same segment of the pipeline 102. For example, the LDS 110 may be a CPM mass balance LDS, the LDS 111 may be a fiber optic acoustic system and the LDS 112 may be a CPM Real-Time Transient Monitoring. The fusion SFI 722 may represent an independent fusion of the SFI for the LDS 110-112 collectively. For example, the fusion SFI 722 is configured to indicate a frequency (e.g., average interval) with which the values of the select monitoring measurements acquired by a collective of the LDS 110- 112 are at least as severe as the present given values. The controller circuit 202 may calculate the fusion SFI 722 utilizing a tail probability fusion based on the tail probability (e.g., the tail probability 410) of each of the statistical models 702-704 of the LDS 110- 112. For example, the controller circuit 202 may calculate the tail probabilities for each of the LDS 110-112 based on the statistical models 702-704 and the select monitoring measurement. The controller circuit 202 may be configured to combine the tail probabilities to determine the fusion SFI 722. For example, the controller circuit 202 may combine the tail probabilities by utilizing the Fisher's method (e.g., Fisher's combined probability test), based on a set of combination rules stored in the memory 204, tail probability multiplication, based on characteristics of the LDS 110-112, and/or the like. Based on the combined tail probability, the controller circuit 202 may determine the combined SFI 722. The controller circuit 202 can implement one or more responsive actions based on the combined or fused SFI. For example, the controller circuit 202 can automatically (e.g., without operator intervention) shut off a valve or otherwise stop the flow of a fluid through the pipeline responsive to the combined or fused SFI indicating that the fluid is leaking from the pipeline. Optionally, the controller circuit 202 can automatically direct a robotic or manually operated repair system to travel to a location of a leak to repair the pipeline responsive to the combined or fused SFI indicating that the fluid is leaking from the pipeline.

[0060] Figure 8 is a functional block diagram 800 of an embodiment for determining a fusion SFI 804 from multiple LDS 110-112 monitoring different segments of the pipeline 102. For example, the LDS 110-112 may represent fiber optic acoustic systems. Each LDS 110-112 is positioned at different 10-meter segments of the pipeline 102. The controller circuit 202 may be configured to calculate an aggregate fusion based on the statistical models 702-704 to determine a fusion SFI 804. Similar to the fusion SFI 772, the fusion SFI 804 is configured to indicate a frequency based on a select value of the monitoring measurements acquired by the LDS 110-112. The fusion SFI 804 may represent a lowest SFI 710-712. For example, the fusion SFI 804 may have a value equal to or greater than the lowest frequency of the SFI 710-712 of the collection of LDS 110- 112.

[0061] The controller circuit 202 may determine the fusion SFI 804 based on tail probabilities calculated from the statistical models 702-704 of the LDS 110-112. For example, the controller circuit 202 may calculate the tail probabilities for each of the LDS 110-112 based on the statistical models 702-704 and the select monitoring measurement. The controller circuit 202 may be configured to combine the tail probabilities to determine the fusion SFI 804. For example, the controller circuit 202 may combine the tail probabilities by utilizing Equation 1 shown below to form a fusion tail probability 802 (e.g., the variable pj). The variable p represents the lowest tail probability of the value of LDS 110-112 based on the statistical models 702-704. The variable n represents a number of LDS 110-112 corresponding to the fusion SFI 804. Based on the fusion tail probability 802, the controller circuit 202 may determine the combined SFI 804. p _f = 1 - (l - p) * n Equation (1)

[0062] Figure 9 is a functional block diagram of an embodiment for determining a fusion SFI 906 from multiple LDS 1 10-1 12 monitoring a segment of the pipeline 102. The multiple LDS 1 10-1 12 may be multiple CPM monitoring LSD (e.g., compensated mass balance, CPM Real-Time Transient Monitoring) that are affected by exogenous factors that may adjust the monitoring measurement values acquired by the LDS 1 10-1 12. The controller circuit 202 may be configured to determine a correlation between the monitoring measurement values of the LDS 1 10-1 12. For example, the controller circuit 202 may determine a joint statistical model 902 based on the monitoring measurements obtained by the LDS 1 10-1 12. Additionally or alternatively, the controller circuit 202 may utilize the statistical models 702-704 to determine the correlation between the monitoring measurement values of the LDS 1 10-1 12. Optionally, the controller circuit 202 may include the joint statistical model 902 to determine the correlation. The statistical models 702-704 model the distribution of the monitoring measurements acquired by the LDS 1 10- 1 12. The controller circuit 202 may execute a copula model stored in the memory 204 configured to determine the correlation of the monitoring measurement values between the LDS 1 10-1 12 based on the distribution provided in the statistical models 702-704 to generate a dependent fusion 904 of the statistical models 702-704. Based on the dependent fusion 904, the controller circuit 202 may determine the fusion SFI 906 of the LDS 1 10- 1 12. It may be noted that the controller circuit may utilize other models to determine a correlation of the monitoring measurements of the LDS 1 10-1 12 such as a Simes combination rule, and/or the like.

[0063] Additionally or alternatively, in connection with Figure 10, the controller circuit 202 based on a received user selection from the user interface 206 may display SFI representing multiple LDS, for a select type of LDS, position of the LDS with respect to the pipeline and/or the like. [0064] Figure 10 is a functional block diagram 1000 of an embodiment. The functional block diagram includes a first and second series of LDS 1002, 1003. Each series of LDS 1002, 1003, may represent a type of LDS (e.g., CPM mass balance, CPM Real-Time Transient Monitoring, fiber optic sensing, and/or the like). For example, the first series of LDS 1002 may represent a CPM mass balance LDS configured to monitor a pipeline segment, such as a 10 km pipeline segment, that is integrated over six different integration periods representing six different LDS (e.g., LDS A-l, LDS A-2, LDS A-6) of the first series of LDS 1002. Each LDS of the first series of LDS 1002 may include three operating conditions utilizing dynamic thresholds, for example similar to the functional block diagram 600 shown in Figure 6. The second series of LDS 1003 may represent a fiber optic acoustic LDS configured to monitor the pipeline segment. For example, the fiber optic acoustic LDS of the second series of LDS 1003 may subdivide the pipeline segment into 10 meter channels to form 1,000 channels distributed along the 10 km pipeline segment. The second series of LDS 1003 may utilize two distinct detection algorithms to define the monitoring measurements of the second series of LDS 1003. For example, the second series of LDS 1003 may output two monitoring measurements for each channel, or 2,000 monitoring measurements for each time interval of the LDS. The monitoring system 150 may be configured to manage monitoring and fusion of all information (e.g., monitoring measurements) obtained by the first and second series of LDS 1002, 1003.

[0065] For example, for each of the six integration periods of the first series of LDS 1002, the controller circuit 202 may determine three statistical models 1004 (e.g., SMI, SM2, SM3) for each operating condition class. The first series of LDS 1002 may generate six monitoring measurement values that are received by the controller circuit 202 along corresponding communication links. Based on the operating condition of the six integration periods, the controller circuit 202 may determine SFIs 1020 for each LDS of the first series of LDS 1002 utilizing the corresponding statistical models 1004.

[0066] Additionally or alternatively, the controller circuit 202 may utilize a dependent fusion 1008 of the monitoring measurements of the first series of LDS 1002 to determine a fusion SFI 1010 of the first series of LDS 1002. For example, the controller circuit 202 may be configured to determine a correlation between the monitoring measurement values of the first series of LDS 1002 similar to and/or the same as the described in connection with the functional block diagram 900 shown in Figure 9. The controller circuit 202 may utilize the statistical models 1004 to determine the correlation between the monitoring measurement values of the first series of LDS 1002. The controller circuit 202 may execute a copula model stored in the memory 204 configured to determine the correlation of the monitoring measurement values between the LDS of the first series of LDS 1002 based on the distribution provided in the statistical models 1004 to generate a dependent fusion 1008 of the statistical models 1004. Based on the dependent fusion 1008, the controller circuit 202 may determine the fusion SFI 1010 of the first series of LDS 1002.

[0067] In another example, the controller circuit 202 may determine 2000 statistical models 1005 corresponding to each pair of monitoring measurements defined by the two distinct detection algorithms obtained by each of the LDS (e.g., LDS B-l, LDS B-2, LDS B-M) of the second series of LDS 1003. The controller circuit 202 may utilize the statistical models 1005 each time the second series of LDS 1003 generate the monitoring measurements. For example, the second series of LDS 1003 may generate 2,000 pairs of monitoring measurements, which are obtained by the controller circuit 202. The controller circuit 202 is configured to determine a pair of tail probabilities for each LDS utilizing the statistical models 1005. The controller circuit 202 may fuse the tail probabilities to form the fusion tail probabilities 1022, for example utilizing aggregate fusion as described in relation to the functional block diagram 800 in Figure 8 and/or dependent fusion as described in relation to the functional block diagram 900 in Figure 9, and determine an SFI for each LDS of the second series of LDS 1003 based on the fused trail probabilities.

[0068] Additionally or alternatively, the controller circuit 202 may fuse the SFI of the LDS of the second series of LDS 1003 to determine a fusion SFI 1014 for the entire second series of LDS 1003. For example, the controller circuit 202 may be configured to calculate an aggregate fusion based on the fusion tail probabilities 1022 similar to and/or the same as the described in connection with the functional block diagram 800 shown in Figure 8. The controller circuit 202 may combine the fusion tail probabilities 1022 based on the Equation 1 shown above to determine an aggregate fusion 1012 of the fuse tail probabilities of the second series LSD 1003. Based on the aggregate fusion 1012, the controller circuit 202 may determine the fusion SFI 1014 of the second series of LDS 1003.

[0069] In another example, the controller circuit 202 may be configured to fuse the SFI of the first and second series of LSD 1002, 1003 to determine the fusion SFI 1018 representing the SFI of the entire pipeline considering all LDS of the first and second series of LSD

1002, 1003. The controller circuit 202 may determine the fusion SFI 1018 by determining a series of independent fusions 1006 that function similar to and/or the same as described in connection with the functional block diagram 700 shown in Figure 7. The controller circuit 202 may combine each of the tail probabilities of the first series of LDS 1002 with each and/or the corresponding fusion tail probabilities 1022 of the second series of LDS 1003 to determine a fusion tail probabilities of the first and second series of LDS 1002,

1003. Optionally, the controller circuit 202 may produce an SFI for each of the fusion tail probabilities of the first series and the second series of LDS 1002, 1003. The controller circuit 202 may combine the fusion tail probabilities determined based on the 1022 based on the Equation 1 shown above to determine an aggregate fusion 1016 of the fusion tail probabilities of the first and second series of LDS 1002, 1003. Based on the aggregate fusion 1016, the controller circuit 202 may determine the fusion SFI 1018 representing the SFI of the pipeline.

[0070] It may be noted that the controller circuit 202 may determine a plurality of SFI for each LDS of the first and second series of LDS 1002, 1003, fusion SFI (e.g., the SFIs 1020, the fusion SFI 1010, the fusion SFI 1014, the fusion SFI 1018) representing more than one LDS, and/or the like. Optionally, the controller circuit 202 may display every SFI calculated by the controller circuit 202 on the display 208 and/or a subset of the SFI. For example, in connection with Figures 11A-B, the user may select different SFIs (e.g., the SFIs 1020, the fusion SFI 1010, the fusion SFI 1014, the fusion SFI 1018) determined by the controller circuit 202 utilizing the user interface 206, which are displayed on the display 208. Additionally or alternatively, the controller circuit 202 may be configured to display other SFIs on demand (e.g., based on user inputs received from the user interface 206), automatically when the LDS is a contributing to an SFI above the predetermined threshold from a fusion SFI.

[0071] Figures 11A and 11B are illustrations of gauges 1102, 1152, 1153 of SFI of an embodiment. For example, the gauge 1 102 (Figure 11 A) may indicate the fusion SFI 1018 of the pipeline associated with Figure 10. The gauge 1102 includes a needle and/or graphical indicator 1106 indicating a value of the fusion SFI 1018 relative to a frequency scale 1010. The frequency scale 1010 includes a threshold indicator 1108 representing a predetermined threshold stored in the memory 204. The graphical indicator 1106 indicates that the fusion SFI 1018 is above the predetermined threshold by being within the threshold indicator 1108. Optionally, the controller circuit 202 is configured to generate a notification alert when the fusion SFI 1018 is above the predetermined threshold. For example, the notification alert may be an auditory alert, a graphical alert shown on the display, a transmission to a remote system, a transmission to one or more users, and/or the like. Optionally, the controller circuit 202 may generate additional textual information 1104 on the display 208 shown concurrently with the gauge 1102. For example, the textual information 1104 may display a numerical value representing the predetermined threshold, details of the notification alert (e.g., length of time for the notification alert), LDS of the first and/or second series of LDS 1002, 1003, above the predetermined threshold, and/or the like.

[0072] Additionally or alternatively, the controller circuit 202 may display a subset of the SFI based on a user selection. For example, the controller circuit 202 may receive a user input indicative to display the fusion SFI 1010, 1014 of the first series of LDS 1002 and the second series of LDS 1003, respectively. In connection with Figure 11B, the fusion SFI 1010 may be shown on the gauge 1152, and the fusion SFI 1014 may be shown on the gauge 1153. The gauges 1152, 1153 include graphical indicators 1064, 1066 indicating values of the fusion SFI 1010, 1014 relative to the frequency scale 1010. The controller circuit 202 may generate the LDS information 1154, 1155 concurrently with the gauges 1152, 1153 based on the monitoring measurements of the first and second series of LDS 1002, 1003. The LDS information 1154, 1155 may display the monitoring measurement, amount of time the fusion SFI 1010, 1014 is above the predetermined threshold, indicating the LDS having an SFI above the predetermined threshold, and/or the like. For example, the gauge 1153 indicates that the fusion SFI 1014 is above the predetermined threshold by the graphical indicator 1066 being positioned within the threshold indicator 1108. The LDS information 1155 may indicate which of the LDS of the second series of LDS 1003 have an SFI above the predetermined threshold. Additionally or alternatively, the user interface 206 may be configured to receive a user selection indicative of one of the LDS of the second series LDS 1003. For example, the controller circuit 202 may receive a user selection indicative of the LDS having an SFI above the predetermined threshold. The controller circuit 202 may generate a gauge of the SFI based on the monitoring measurements corresponding to the selected LDS of the second series LDS.

[0073] It should be noted that the particular arrangement of components (e.g., the number, types, placement, or the like) of the illustrated embodiments may be modified in various alternate embodiments. For example, in various embodiments, different numbers of a given module or unit may be employed, a different type or types of a given module or unit may be employed, a number of modules or units (or aspects thereof) may be combined, a given module or unit may be divided into plural modules (or sub-modules) or units (or sub- units), one or more aspects of one or more modules may be shared between modules, a given module or unit may be added, or a given module or unit may be omitted.

[0074] As used herein, a structure, limitation, or element that is "configured to" perform a task or operation is particularly structurally formed, constructed, or adapted in a manner corresponding to the task or operation. For purposes of clarity and the avoidance of doubt, an object that is merely capable of being modified to perform the task or operation is not "configured to" perform the task or operation as used herein. Instead, the use of "configured to" as used herein denotes structural adaptations or characteristics, and denotes structural requirements of any structure, limitation, or element that is described as being "configured to" perform the task or operation. For example, a processing unit, processor, or computer that is "configured to" perform a task or operation may be understood as being particularly structured to perform the task or operation (e.g., having one or more programs or instructions stored thereon or used in conjunction therewith tailored or intended to perform the task or operation, and/or having an arrangement of processing circuitry tailored or intended to perform the task or operation). For the purposes of clarity and the avoidance of doubt, a general-purpose computer (which may become "configured to" perform the task or operation if appropriately programmed) is not "configured to" perform a task or operation unless or until specifically programmed or structurally modified to perform the task or operation.

[0075] It should be noted that the various embodiments may be implemented in hardware, software or a combination thereof. The various embodiments and/or components, for example, the modules, or components and controllers therein, also may be implemented as part of one or more computers or processors. The computer or processor may include a computing device, an input device, a display unit and an interface, for example, for accessing the Internet. The computer or processor may include a microprocessor. The microprocessor may be connected to a communication bus. The computer or processor may also include a memory. The memory may include Random Access Memory (RAM) and Read Only Memory (ROM). The computer or processor further may include a storage device, which may be a hard disk drive or a removable storage drive such as a solid state drive, optic drive, and the like. The storage device may also be other similar means for loading computer programs or other instructions into the computer or processor.

[0076] As used herein, the term "computer," "controller," and "module" may each include any processor-based or microprocessor-based system including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), logic circuits, GPUs, FPGAs, and any other circuit or processor capable of executing the functions described herein. The above examples are exemplary only, and are thus not intended to limit in any way the definition and/or meaning of the term "module" or "computer."

[0077] The computer, module, or processor executes a set of instructions that are stored in one or more storage elements, to process input data. The storage elements may also store data or other information as desired or needed. The storage element may be in the form of an information source or a physical memory element within a processing machine.

[0078] The set of instructions may include various commands that instruct the computer, module, or processor as a processing machine to perform specific operations such as the methods and processes of the various embodiments described and/or illustrated herein. The set of instructions may be in the form of a software program. The software may be in various forms such as system software or application software and which may be embodied as a tangible and non-transitory computer readable medium. Further, the software may be in the form of a collection of separate programs or modules, a program module within a larger program or a portion of a program module. The software also may include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to operator commands, or in response to results of previous processing, or in response to a request made by another processing machine.

[0079] As used herein, the terms "software" and "firmware" are interchangeable, and include any computer program stored in memory for execution by a computer, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are exemplary only, and are thus not limiting as to the types of memory usable for storage of a computer program. The individual components of the various embodiments may be virtualized and hosted by a cloud type computational environment, for example to allow for dynamic allocation of computational power, without requiring the user concerning the location, configuration, and/or specific hardware of the computer system. [0080] It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments (and/or aspects thereof) may be used in combination with each other. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from its scope. Dimensions, types of materials, orientations of the various components, and the number and positions of the various components described herein are intended to define parameters of certain embodiments, and are by no means limiting and are merely exemplary embodiments. Many other embodiments and modifications within the spirit and scope of the claims will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms "including" and "in which" are used as the plain-English equivalents of the respective terms "comprising" and "wherein." Moreover, in the following claims, the terms "first," "second," and "third," etc. are used merely as labels, and are not intended to impose numerical requirements on their objects. Further, the limitations of the following claims are not written in means-plus-function format and are not intended to be interpreted based on 35 U.S.C. § 112(f) unless and until such claim limitations expressly use the phrase "means for" followed by a statement of function void of further structure.

[0081] This written description uses examples to disclose the various embodiments, and also to enable a person having ordinary skill in the art to practice the various embodiments, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the various embodiments is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if the examples have structural elements that do not differ from the literal language of the claims, or the examples include equivalent structural elements with insubstantial differences from the literal language of the claims.