CROSS-REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to and the benefit of United States Provisional Application No. 63/305,241 filed January 31, 2022, the contents of which are hereby incorporated by reference in their entirety.

TECHNICAL FIELD

[0002] The present disclosure relates generally to collecting and capturing process data from process equipment using a process controller in communication with a computing device.

BACKGROUND

[0003] As automation becomes more complex, the amount of detailed process data received by controllers monitoring and controlling performance of production systems have dramatically increased. Collecting the detailed process data from these controllers, including, for example, input data from each station and/or operation of a production system, may require large data structures and substantial memory capacity for storage of the detailed process data and/or for analy sis of the detailed data to identify improvement opportunities.

SUMMARY

[0004] A system and method are provided for dynamically scaling of data collection, data processing, and data visualization of a process including a plurality of stations comprising a production line of a facility , each station repeatedly performing a sequence of process steps or operations, each repetition of the sequence defining a cycle performed by the station. In an illustrative example, the method described herein is implemented in two stages. In Stage 1, the system, via a data collector in communication with process controllers controlling the process stations, collects all station data as the stations repeatedly perform their respective process cycles, the station data including summary and detailed data, to establish baseline operating conditions and to determine correlations among process outputs and process inputs. During Stage 1, the collected station data can be used to identify and implement actions to improve and/or stabilize the process outputs of each respective process station. At Stage 2, the system or a user of the system will identify each of the stations as either a critical station or a non-critical station, based on the station data collected during Stage 1. At Stage 2, the system and/or the user, via a user device in communication with the system will turn off, e g., cease collection of the detailed station data while continuing to collect the summary data for non-critical operations, and continue to monitor the stability of the non- critical production operations using the summary data. At Stage 2, collection of the detailed data and summary data is maintained and/or activated for all stations which have been identified as critical stations, to provide focused and pertinent data from the critical stations to the production management to take actions to improve the process outputs of tire critical stations, and/or to stabilize or return process outputs of the critical stations to an acceptable, e.g., normal, range. In illustrative examples, station data, including various types of summary data and detailed data, arc provided.

[0005] An exemplary system and method for capturing process data from a plurality of process stations is described herein. The system includes a plurality of process stations, each respective process station of the plurality of process stations configured to repeatedly perform a process cycle and generate station data defined by each performance of the process cycle. The station data generated by the process station includes detailed data and summary data, as further described herein. The system further includes a controller configured to receive the station data from the plurality of process stations, a data collector configured to collect the station data from the controller, and a server in communication with the data collector and a database. In an illustrative example, the server is configured to receive the station data from the data collector, store the station data in the database, associate the station data received from each respective process station with the respective process station in the database, determine a station status of the respective process station as one of critical or non-critical, using the station data; associate the station status with the respective process station in the database, and instruct the data collector to cease collection of the detailed data from each process station of the plurality of process stations having a non-critical station status.

[0006] In an illustrative example, the server is configured to associate, in the database, critical station criteria with each respective process station, perform a comparison of the station data and the critical station criteria, and determine the station status of the respective process station using the comparison. In an illustrative example, the critical station criteria includes at least one of an acceptable threshold, an acceptable limit, or an acceptable range of the station data, and the server is configured to compare the station data to the at least one of the acceptable threshold, the acceptable limit, and the acceptable range to determine the station status. In an example, the server is configmed to analyze the station data to determine the critical station criteria. In an example, the server is configmed to analyze the station data to determine a baseline of the station data, and the critical station criteria is defined by a deviation of the station data from the baseline.

[0007] In one example, the server is configured to continuously receive the station data from the data collector, continuously determine in real time, using the station data, a current station status of the respective process station as a critical or non-critical, and continuously associate in real time, in the database, the cmrent station status with the respective process station. In the present example, the server instructs the data collector to: when the cmrent station status of the respective process station changes from critical to non-critical, cease collection of the detailed data from the respective process station, and when the cmrent station status of the respective process station changes from non-critical to critical, reinstate collection of the detailed data from the respective process station.

[0008] In one example, the process cycle includes a sequence of process steps performed by the respective process station, where the detailed data is defined by performance of at least one of the process steps of the sequence of process steps, and the summary data is defined by performance of the process cycle.

[0009] The benefits and advantages of the system for dynamic scaling of data collection includes reducing the loading and cost for the factory network and IT infrastructures by reducing data collection from non-critical stations, providing pertinent and focused data to improve critical station performance and to improve and/or sustain overall production performance, and thus, optimize the users’ operating cost of the facility (factory), gain efficiency in data collection from non-critical stations while monitoring non-critical stations for condition changes, and focus improvement resources on critical stations.

[0010] The above noted and other features and advantages of the present disclosure are readily apparent from the following detailed description when taken in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The description herein makes reference to the accompanying drawings wherein like reference numerals refer to like or similar components throughout the several views, and wherein: [0012] FIG. 1 is a schematic illustration of a system for dynamically scaling of data collection, data processing, and data visualization of a process;

[0013] FIG. 2 is a schematic illustration of data collected from a process, including examples of Detailed Data and Summary Data related to production cycle performance and product characteristics; [0014] FIG. 3 is a schematic illustration of data collected from a process, including examples of Detailed Data and Summary Data related to process Output Data and process Input Data;

[0015] FIG. 4 is a schematic illustration of a flowchart showing Stage 1 of a method for dynamic scaling of data collection, data processing, and data visualization of a process, Stage 1 including collecting and set up baselines and correlations between process outputs and process inputs;

[0016] FIG. 5 is a schematic illustration of the system of FIG. 1, showing data collection during Stage 1 of the method shown in FIG. 4;

[0017] FIG. 6 is a schematic illustration to show Stage 2 of the method of FIG. 4, Stage 2 including Dynamic Scaling of data collection; and

[0018] FIG. 7 is a schematic illustration of the system of FIG. 1, showing data collection during Stage 2 of the method shown in FIG. 6.

DETAILED DESCRIPTION

[0019] In the following description, numerous details of the embodiments of the present disclosure, which should be deemed merely as exemplary, are set forth with reference to accompanying drawings to provide thorough understanding of the embodiments of the present disclosure. Therefore, those skilled in the art will appreciate that various modifications and replacements may be made in the described embodiments without departing from the protection scope and the spirit of the present disclosure. Further, for clarity and conciseness, descriptions of known functions and structures are omitted hereinafter.

[0020] Referring to the drawings, wherein like reference numbers correspond to like or similar components throughout the several figures, there is shown in FIG. 1 a data capture system generally indicated at 100 for capturing data from a production facility generally indicated at 10, and a method 200 (see FIGS. 4 and 6) for capturing automation data from the facility 10 using the system 100 are described herein. The system 100 and method 200 are configured for dynamically scaling data collection, data processing, and data visualization of a process, production line, etc., such as a manufacturing, assembly or other production process which can include one or more stations, machines, sub-processes, production lines, etc., configured to perform coordinated operations, which can include automated, partially automated, and/or non-automated operations.

[0021] Referring to FIG. 1, in a non-limiting example, the system 100 includes a facility 10 having one or more production lines 15 including one or more stations 20 controlled by one or more controllers 35. The controllers 35 are in communication with a data collector 45. The data collector 45 is configured for receiving data from the controllers 35 and, in accordance with the method 200 described herein, and for selectively transmitting data to a system server 50 for storage to a database 55. In an illustrative example, the data collector 45 receives instructions from the system server 50 and selectively transmits data received from the controllers 35 to the system server 50 in response to the instructions. The instructions can include selectively ceasing the transmission of detailed data from non-critical stations 20, where the non-critical status of the stations 20 is determined by the server 50. The database 55 and/or server 50, in a non-limiting example, can be configured as cloud computing resources. The system 100 further includes at least one user device 60 in communication with the server 50. The user device 60 includes a user interface 65 for displaying data and other process information collected and generated by the system 100, and for receiving inputs, including instructions, from a user of the user device 60.

[0022] Each of the stations 20, controllers 20, data collector 45, server 50, and user devices 60 are in communication via a network 40. Examples of the network 40 include but are not limited to the internet, intranet, local area network, mobile communication network, and combinations thereof. Each of the controllers 20, data collector 45, server 50, and user devices 60 can include a memory for receiving, storing, generating and/or providing the station data 70 and data derived therefrom including detailed data 75 including process input data, summary data 80 including process output data, etc. within the system 100, and further include a central processing unit (CPU) for executing applications and/or algorithms as required to perform the method 200 described herein. The memory, at least some of which is tangible and non-transitory, may include, by way of example, ROM, RAM, EEPROM, etc., of a size and speed sufficient, for example, for executing the applications and algorithms required to perform the method 200, receiving, storing, generating, and/or collecting the station data 70, storing the data to the database 55, and/or communicating with other devices via the network 40.

[0023] Referring again to FIG. 1, each controller 35 is configured to control one or more stations 20 to perform coordinated operations, including processing steps and/or a sequence of operations. A station 20 is configured to repeatedly perfonn one or more processing steps, one or more operations, and/or a sequence of operations as defined (instructed) by the controller 35. It would be understood that the station 20 would, in operation, repeatedly perform the sequence of operations comprising ordered steps as an operating cycle, under control of the controller 35, such that the station data 70 generated by the station 20 would include detailed data 75 from each of the repeated operating cycles performed by the station 20. A station 20 can include one or more station elements 25, such as devices, tools, fixtures, etc. for performing the various steps and/or operations as instructed and/or controlled by the controller 35. A station 20 can include one or more sensing devices 30, also referred to herein as sensors 30, for sensing one or more parameters or characteristics of the station 20, station elements 25, product being processed by the station 20, the station environment, etc., and generating a sensor signal corresponding to the sensed parameter and/or characteristic. The sensor 30 is in communication with the controller 35, via the station 30 and/or the network 40, such that during operation of a production cycle, the sensor 30 is outputting sensor signals which are received by the controller 35 for processing according to the data collection method 200 described further herein. In one example, the production line 15 can be configured as a machine, and/or can include a plurality of machines, such that each station 20 can include one or more machines. Accordingly, the terminology production line, sub-line, main assembly line, station, machine, etc. is not intended to limit the implementation as described and/or claimed herein.

[0024] The station data 70 collected and/or generated by the controller 35 and/or data collector 45 can include condition state data for a station 20, where a state, which may be referred to as a condition state or as a condition, as used herein, refers to a state of the station 20 and/or a station element 25, a state of an object (such as a product or a workpiece) being operated on by the station 20, a condition, a status, a parameter, a position, or other property of the station, operation, object, product or workpiece being monitored, measured and/or sensed. Non-limiting examples of condition states including cycle start time, cycle stop time, element start time, element travel, element stop time, position of an element or object, a dimensional measurement or parameter of an object which can include a dimensional measurement of a feature of the object, or a station element 25, a feature of a station 20, a feature of a workpiece to which an operation is being performed by a station 20 and/or station element 25, a condition of one or more of a station element 25, a station 20, a production line 15 or workpiece, or a condition of the environment within the facility 10. A condition state could further include for example, operating conditions of a station 20 or station element 25, such as on, off, open, closed, auto, manual, stalled, blocked, starved, traveling, stopped, faulted, OK, good, bad, in tolerance, out of tolerance, present, not present, extended, retracted, high, low, etc., and can include for example, a measure of a physical property such as chemistry, temperature, pressure, color, shape, position, dimensional conditions such as size, surface finish, thread form, a functional parameter such as voltage, current, torque, pressure, force, etc., such that it would be understood that the terms state, condition, condition state and/or parameter as describing station data 70, detailed data 75, and summary data 80, are intended to be defined broadly.

[0025] Referring to the non-limiting example illustrated by FIG. 1, shown is an example facility 10 including a production line 15 consisting of a main line 15B including stations 20 identified in FIG. 1 as Mainl, Main2, Main3, Main4, Main5, Main6, and Main7. The production line 15 further includes a sub-assembly line 15A including sub-assembly stations 20 identified in FIG. 1 as Subl, Sub2, and Sub3. Output from the sub-assembly line 15 A, for example, sub-assemblies, products, or materials processed on the sub-assembly line 15 A, joins (enters or is introduced into) the main production line 15B at the station 20 identified as Main4. Each station 20 is either fully automated, manual, or mixed manual and automated. All stations 20 are connected to one or a plurality of controllers 35 via the facility network 40. The controllers 35 are responsible for controlling the automation, e.g., controlling the elements 25 of the stations 20, and receiving/collecting station data 70, including, for example, signals from sensors 30, signals of manual operation feedback, production parameters (process inputs), and production outcome measurement (process outputs).

[0026] The controllers 5 are connected, through the factory network 40, to a data collector 45 that collects the station data 70 from the controllers 35. The data collector 45 conducts processing of the station data 70, then selectively sends all or a portion of the station data 70 to the factory server 50, which may be a cloud resource, to process, store to database 55, and/or display, for example, via a user interface 65 of a user device 60, all or a portion of the station data 70 according to method 200 as described further herein.

[0027] Referring to FIGS. 2 and 3, illustrative examples of station data 70 which can be collected from the production lines 15 and stations 20 are shown. As illustrated in the figures, the station data 70 from the production line 15 and stations 20 can be categorized as detailed data 75 and summary data 80. The summary data 80 of a station 20 provide a high-level summary of the process results of the station 20. The detailed data 75 are, by way of non-limiting example, a data which aggregate to or are in a mathematical relationship with the summary data 80, and/or which are related to the summary data 80 by a formula or algorithm, as illustrated in FIGS. 2 and 3. Thus the detailed data 75 can provide detailed information to explain, diagnose, and/or determine which part of the process element(s) 25 and/or processing steps performed by the station 20 are causing the summary data 80 for that station 20 to trend over an expected cycle time and/or be outside of a normal range (specification or baseline) for the station 20.

[0028] Referring to FIG. 2, Example 1 of station data 70 includes production cycle data of a station 20, received by a controller 35 controlling that station 20. In Example 1, the detailed data 75 include start and finish timestamps tl , t2, t3,... of each of the process steps, which can be, for example, timestamped by the controller 35. This group of detailed data 75 consisting of timestamps tl, t2, t3,... corresponding to each step of the cycle performed by the station 20 provide the breakdown details of the overall Cycle time, e.g., the summation of tl, t2, t3... . for completing all steps in the cycle performed by station 20. In Example 1, another group of detailed data 75 includes the machine fault codes (c 1 , c2, ... ) and their start and end timestamps such that a faulted time associated with each fault code can be determined. This group of detailed data 75 comes with the Faulted time and provide the detail data 75 and additional information (clues) to investigate and/or determine why the station 20 and/or one or more machines and/or elements 25 of the station 20 are faulted.

[0029] Continuing with Example 1, at the station level the summary data 80 generated for station 20 include Cycle time, Starved time, Blocked time, Faulted time, and Cycle counts. In the illustrative example, the Cycle time is the time from a production cycle start to end. A station is Starved when the station is empty and waiting for upstream station to finish and send the next part over. A station is Blocked when the processes are completed in the station and waiting to send the part out to downstream station. A station is Faulted when one or more assets in the station are malfunctioned and stop cycling. The Cycle counts are the number of cycles completed by the station 20 during a specified period, for example, during a production shift.

[0030] Example 2 of FIG. 2 illustrates station level data 70 comprising dimensional data of a product assembly which is assembled in a station 20. The summary data 80, in the present example, are the overall dimension of Length, Width, and Height of the product assembly. The detailed data are the length (11, 12,...), width (wl, w2,...), and height (hl, h2,...) of the components (Component 1, Component 2, Component ... ) which are processed and/or assembled in the station 20 to produce the product assembly. If one of the overall dimensions of Length, Width, and Height of the product assembly (the summary data 80) is out of normal (acceptable or specified) range, the component dimensions (1, w, h) (the detailed data 75) can provide the additional information (clues) to determine which component(s) caused the out of range condition.

[0031] Referring to FIG. 3, additional non-limiting examples are shown illustrating station data 70 comprised of summary data 80 and detailed data 75 consisting, respectively, of process outputs and process inputs, where the process output is related to the process inputs by an algorithm or relationship, which may be a formulaic relationship as shown in FIG. 3. The process outputs (summary data 80) are the expected or intended outcomes of the production process, and can include quality measurements that are important to the product. The process output data is measured and collected after the completion of related production (manufacturing) processes. Process inputs (detailed data 75) include the factors or controlled parameters applied during the manufacturing process in order to generate the expected outcomes - the process outputs (summary data 80). As distinguished from Examples 1 and 2 shown in FIG. 2, in Examples 3 and 4 the Process outputs (summary data 80) and Process inputs (detailed data 75) are often collected from different stations 20 and can be collected from more than one controller 35. In the example shown, the relationship between process output and process input can be expressed as Y = f (xl, x2, x3,...), where Y is the process output and xl , x2, x3 , ... are the process inputs, which are related to the process output in a cause-effect relationship.

[0032] Example 3 shows a non-limiting example of station data 70 from a welding station 20, where the summary data 80 consists of welding quality data (Y3, welding quality check). The process output (summary data 80) here is the weld quality, usually measured after the welding process, for example, using non-destructive inspection or on a sample basis using destructive testing, and rated as Good or Reject. The process inputs (detailed data 75) identified as parameters affecting weld quality, are parameters which are applied when assembly (welding) process occurs. In the present example, tire process inputs (detailed data 75) include electric current (xl), welding gun squeezing force (x2), and duration (c3), ...etc. These are the contributing factors affecting the process output of ‘"weld quality.”

[0033] Example 4 shows a non-limiting example of station data 70 from a paint station 20, where the process output (summary data 80) is paint film thickness build-up, which is one of the critical quality indicators for paint appearance. As shown in the example, the process output, paint thickness build-up, is affected by the process inputs (detailed data 75) of: pressure (x4), the paint flow rate (x5), the voltage difference betw een the paint bells and the vehicle body (x6), the temperature (x7), and the humidity (x8) of the paint booth.

[0034] Referring now to FIGS. 4-7, the method 200 including a first stage 205 (Stage 1) and a second stage (210) is illustrated, with FIGS. 4-5 illustrating Stage 1 and FIGS. 6-7 illustrating Stage 2 of the method 200. As shown in FIG. 4, Stage 1, indicated generally at 205, begins with step 105 by generating detailed station level data 75 from the stations 20, which is received by the controllers 35 and transmitted to the data collector 45. Concurrently at step 105, the summary data 80 generated from the stations 20 and/or produced using the detailed data 75 is transmitted to the data collector 45, with the data collector 45 collecting all station data 70, including summary data 80 and detailed data 75 generated by the stations 20. At step 110, the summary data 80 and detailed data 75, including the process outputs 80 and process inputs 75 are analyzed to establish baselines for each station 20, for example, for each of the summary data elements and detailed data elements, and to determine the correlations among process outputs 80 and process inputs 75 (see FIG. 3). At step 115, the station data 70 including the detailed data 75 and summary data 80 can be used to identify improvement and stabilization actions on the production line 15 including the stations 20, which are then implemented at step 120 of Stage 1. Additionally, at step 115, critical station criteria are established for each station 20. In an illustrative example, the critical station criteria for a station 20 can include critical thresholds, limits, or ranges established for one or more of the data elements of the station data 70, e.g., for one or more data elements of the detailed data 75 and/or the summary data 80, such that when a data element exceeds the critical threshold or is operating outside the critical limits or critical range established for that data element, the station 20 producing that data element is determined to be a critical station 85. Likewise, when the data element is in an acceptable condition, e.g., below the critical threshold or is operating within acceptable (non-critical) limits or within an acceptable (non- critical) range established for that data element, the station 20 producing that data element is determined to be a non-critical station 90. The examples are illustrative and non-limiting, such that other criteria can be used to establish the criticality of a certain station 20, such as the nature of the operation being performed or the nature of the product output from the station 20.

[0035] Stage 1, c.g., the first stage 205 of the method 200, is further illustrated in FIG. 5, showing the system 100 set up to collect all station data 70 (step 105), both summary data 80 and detailed data 75 from each station 20 of each production line 15, to set up baselines and establish the correlation relationship between process outputs and inputs (step 110). The station data 70 is received by controllers 35 (see FIG. 1) and collected by the data collector 45, to be processed and/or analyzed by the system server 50 and stored to the database 55. In a non-limiting example, the system server 50, data collector 45, and/or the controller 35 can be collectively configured to associate, in the database 55, the station data 70 with the station 20 from which the station data 70 was generated, and other information, such as the timestamp of the data, the product being produced, etc. In a nonlimiting example, the system server 50, data collector 45, and/or the controller 35 can be collectively configured to output the collected data 70 to a user interface 65 of a user device 60, for viewing and/or analysis by users monitoring the production lines 15 and stations 20. The system 100 can include one or more algorithms, display templates, data matrices, etc. for associating, analyzing, displaying, and/or reporting the station data 70 to a user via the user interface 65. As previously described, at step 115, the users of the system 100 can use the detailed and process input data 75 collected during Stage 1 to identify improvement opportunities, and also to define (establish) the critical criteria for each station 20, e.g., the criteria for determining whether a station 20 is operating in a critical condition or is operating in a non-critical condition. The system 100, for example, via the server 50, can be configured to apply the critical criteria to the station data 70 to determine the condition state of the station 20, as critical or non-critical, and then identify (designate) the station 20 as either a critical station 85 or non-critical station 90 during Stage 1. It would be recognized that the stations 20 identified as critical stations 85 are those stations 20 which are determined to impact throughput and quality the most. During Stage 1 (step 120) the plant operation team can take actions based on the opportunity list (list of potential improvements to critical stations 85) and use the summary and process output data 80 to verify the result of actions taken, achieving improvements, and stabilizing the results. As shown in FIG. 5, detailed and summary data collection are turned on for all stations 20 during Stage 1. When tire opportunities for improvement identified during Stage 1 are achieved and the improvements stabilized, the implementation enters Stage 2 of the method, generally indicated at 210 in FIG. 6 and illustrated by FIG. 7.

[0036] Following Stage 1, e.g., after baselining, improving and/or stabilizing the station operations during Stage 1, Stage 2 is commenced. During Stage 2, the system 100 turn off (cease) most of the detailed data collection for non-critical stations 90, while keeping the summary data collection on for the non-critical stations 90, to monitor the stability of the production operation. Concurrently, the system 100 will identify critical stations 85 based on a comparison of the summary data 80 or process outputs and defined critical criteria, and will turn on detailed data collection on the critical stations 85 as they are identified, to provide focused and pertinent data to the production management to take actions, including improvement and stabilization actions, to return the critical stations 85 to a non-critical operating condition. During Stage 2, the system 100 on a continuous basis compares the summary data 80 to the critical criteria for each station 20, and dynamically turns on and off the detailed data collection for each station 20, depending on the current operating condition (critical or non-critical) of the station 20. In this manner, the volume of data which must be analyzed by the server 50 and stored in the database 55 is reduced substantially, including only the summary data 80 of each station 20, and only the detailed data 75 from the subset of stations 20 identified as critical stations 85 based on their current operating condition.

[0037] Referring again to FIG. 6, this system and method for dynamic scaling of data collection for process visualization and process monitoring includes, at a first step 125 of Stage 2, determining which of the stations 20 are operating as critical stations 85. The critical stations 85 are the stations 20 that impact the production throughput or quality the most. Using the critical criteria defined at step 115 in Stage 1, at step 125 of Stage 2 the critical stations 85 are identified, which may occur by user review or by system algorithms executed, for example, by the server 50 based on data received from the data collector 45. At step 130, for each station 20 determined to be in a non-critical operating condition, e.g., determined to be a non-critical station 90, the collection of detailed data 75 (including process input data) by the data collector 45 is turned off (ceased) and only the summary data 80 (and key process outputs) is collected from the non-critical station 90 by the data collector 45. Also at step 130, for each station 20 determined to be in a critical operating condition, e g., determined to be a critical station 85, the collection of detailed data 75 (including process input data) by the data collector 45 continues and both the detailed data 75 and the summary data 80 (and key process outputs) are collected from the critical station 85 by the data collector 45. At this moment the non- critical stations 90 are in monitoring mode while critical stations 85 are in improvement mode. The detailed data 75 (including process input data) of the critical stations 85 provides essential information for users, at step 135, to identify opportunities and, at step 140, to take actions to improve and stabilize critical stations 85. When the indicators (summary data 80) collected from a critical station 85 show the operating conditions of the critical station 85 arc improved and stabilized such that the critical station 85 is currently operating within acceptable limits, the system 100 determines the critical station 85 is now in a non-critical operating condition, updates the identification of that station to be a non-critical station 90, and turns off collection of the detailed data 75 from that station. As shown in FIG. 6, the scanning and monitoring are conducted continuously (step 140 returning to step 125) so that the system will dynamically scale data collection from each station 20 based on identifying the station 20 as a critical station 85 or a non-critical station 90, as determined by comparing the current summary data 80 to the critical criteria for that station.

[0038] FIG. 7 illustrates an example using critical criteria 95 to determine whether a station is a critical station 85 or a non-critical station 90, where the critical criteria 95 considers throughput, quality, and the average cycle time as an indicator of the bottleneck(s) of a production line. The highest cycle time station(s), Main2 and Main5 in the present example, usually slow the production line 15 down because other stations will be waiting for product from the highest cycle time stations. Also, the bottleneck station(s) usually block the upstream station and starve the downstream station. Therefore, combining the average cycle time and line blocked and starved information, the bottleneck station(s) can be identified, e,g, stations Main5 and Main2 in the example shown in FIG. 7. These stations arc then designated by the system 100 as critical stations 85, and collection of detailed data 75 is turned on for these stations, to collect data which can then be used to analyze, diagnose, and improve station throughput.

[0039] In the present example, the weld data inspection summary data 80 collected from station Sub3 shows the weld quality of the Sub-assembly station Sub3 is trending toward a critical limit, e.g., is approaching the critical criteria defined for that station. The system 100 dynamically identifies station Sub3 as a critical station 85 and turns on collection of the detailed data 75 for station Sub3, as shown in FIG. 7. The detailed data visualization of these stations will provide the insights on what to improve or to fix to return each of these stations to a normal state (acceptable operating condition). [0040] As the summary data collection continues, the dynamic focusing on detailed data collection will adjust to the most important stations and provide the most pertinent information to sustain the production performance. The dy namic scaling is achieved by identifying the critical stations 85, determined by a set of critical criteria 95, and monitoring the collected summary data 80 and/or key process outputs. When identified as critical stations 85, the system 100 or user will turn on collection of detailed data 75 on the critical stations 85. Through data processing and visualization, the users can quickly identify the root cause area and take actions to get the critical process outputs back to a normal operating condition. The benefit of having a dynamic scaling system 100 for selectively collecting both summary data 80 and detailed data 75 from stations 20 which have been identified as critical stations 85, versus a full scale always-on system configured to collect all data regardless of the operating condition of the station 20 from which the data is generated is at least threefold, by providing less on-going demand and loading on the factory network 40 and server/database/cloud resources 50, 55, by providing optimized data collection and pertinent processed insights to improve and sustain production performance, and by reduced on-going operating and software costs including data processing and data storage costs.

[0041] The term “comprising” and variations thereof as used herein is used synonymously with the term “including” and variations thereof and are open, non-limiting terms. Although the terms “comprising” and “including” have been used herein to describe various embodiments, the terms “consisting essentially of’ and “consisting of’ can be used in place of ‘comprising’ and “including” to provide more specific embodiments and are also disclosed. As used in this disclosure and in the appended claims, the singular forms “a”, “an”, “the”, include plural referents unless the context clearly dictates otherwise.

[0042] The detailed description and the drawings or figures are supportive and descriptive of the disclosure, but the scope of the disclosure is defined solely by the claims. While some of the best modes and other embodiments for carrying out the claimed disclosure have been described in detail, various alternative designs and embodiments exist for practicing the disclosure defined in the appended claims. Furthermore, the embodiments shown in the drawings or the characteristics of various embodiments mentioned in the present description are not necessarily to be understood as embodiments independent of each other. Rather, it is possible that each of the characteristics described in one of the examples of an embodiment can be combined with one or a plurality of other desired characteristics from other embodiments, resulting in other embodiments not described in words or by reference to the drawings. Accordingly, such other embodiments fall within the framework of the scope of the appended claims.

[0043] The following Clauses provide example configurations of a system and method for capturing process data from a plurality of process stations disclosed herein.

[0044] Clause 1. A system for capturing process data from a plurality of process stations, the system comprising: a plurality of process stations, wherein each respective process station of the plurality of process stations is configured to: repeatedly perform a process cycle; and generate station data defined by each performance of the process cycle; wherein the station data includes detailed data and summary data; a controller configured to receive the station data from the plurality of process stations; a data collector configured to collect the station data from the controller; a server in communication with a database; the server configured to: receive the station data from the data collector; store, in the database, the station data; associate, in the database, the station data received from each respective process station with the respective process station; determine, using the station data, a station status of the respective process station as critical or non-critical; associate, in the database, the station status with the respective process station; and instruct the data collector to cease collection of the detailed data from each process station of the plurality of process stations having a non-critical station status.

[0045] Clause 2. The system of clause 1, further comprising: the server configured to: associate, in the database, critical station criteria with each respective process station; perform a comparison of the station data and the critical station criteria; and determine the station status of the respective process station using the comparison.

[0046] Clause 3. The system of clause 2, wherein: the critical station criteria includes at least one of an acceptable threshold, an acceptable limit, or an acceptable range of the station data; and the server is configured to compare the station data to the at least one of the acceptable threshold, the acceptable limit, and the acceptable range to determine the station status.

[0047] Clause 4. The system of clause 2, further comprising: the server configured to analyze the station data to determine the critical station criteria.

[0048] Clause 5. The system of clause 2, further comprising: the server configured to analyze the station data to determine a baseline of the station data; wherein the critical station criteria is defined by a deviation of the station data from the baseline.

[0049] Clause 6. The system of clause 1 wherein the server is configured to: continuously receive the station data from the data collector; continuously determine in real time, using the station data, a current station status of the respective process station; continuously associate in real time, in the database, the current station status with the respective process station as either critical or non- critical; instruct the data collector to: when the current station status of the respective process station changes from critical to non-critical, cease collection of the detailed data from the respective process station; and when the current station status of the respective process station changes from non-critical to critical, reinstate collection of the detailed data from the respective process station.

[0050] Clause 7. The system of clause 1, wherein: the process cycle includes a sequence of process steps performed by the respective process station; the detailed data is defined by performance of at least one of the process steps of the sequence of process steps; and the summary data is defined by performance of the process cycle.

[0051] Clause 8. The system of clause 7, wherein: the detailed data includes a step time of each of the process steps of the sequence; and the summary data includes a cycle time of the respective process cycle.

[0052] Clause 9. The system of clause 8, wherein the station data includes a timestamp generated by the controller for each of the process steps.

[0053] Clause 10. The system of clause 7, wherein the detailed data includes a condition state of the process steps of the sequence.

[0054] Clause 11. The system of clause 7, wherein the detailed data includes a fault code generated by the respective process station.

[0055] Clause 12. The system of clause 11, wherein the detailed data includes a fault time at which the fault code was generated.

[0056] Clause 13. The system of clause 12, wherein the fault time is defined by a timestamp generated with the fault code.

[0057] Clause 14. The system of clause 10, wherein the summary data includes an operating condition of the respective process station; and wherein the operating condition of the respective process station is defined by the condition state.

[0058] Clause 15. The system of clause 14, wherein the operating condition of the respective process station is a starved condition, a blocked condition, a faulted condition, or a running condition. [0059] Clause 16. The system of clause 14, wherein the summary data includes a condition time defined by the condition state.

[0060] Clause 17. The system of clause 16, wherein the condition time is a starved time, a blocked time, a faulted time, or a running time.

[0061] Clause 18. The system of clause 10, wherein the summary data includes a number of cycles performed by the respective process station.

[0062] Clause 19. The system of clause 1, wherein: the respective process station is configured to process a product including a plurality of components; the detailed data includes a component attribute of at least one component of the plurality of components; the summary data includes a product attribute of the product; and wherein the product attribute is defined by the component attribute.

[0063] Clause 20. The system of clause 19, wherein the component attribute is a dimension of the at least one component.

[0064] Clause 21. The system of clause 7, wherein: the detailed data includes at least one process input to the respective process cycle; and the summary data includes at least one process output of the respective process cycle.

[0065] Clause 22. The system of clause 21, wherein the at least one process input is related to the at least one process output by a cause-effect relationship.

[0066] Clause 23. The system of clause 21, wherein the station data defines a correlation between the at least one process output and the at least one process input.

[0067] Clause 24. The system of clause 1, wherein the respective process station includes at least one machine; wherein the at least one machine is configmed to output the station data to the controller.

[0068] Clause 25. The system of clause 1, wherein the respective process station includes at least one sensing device; wherein the at least one sensing device is configured to output a sensor signal to tire at least die controller; and wherein at least one of the detailed data and the summary data is defined by the sensor signal.

[0069] Clause 26. The system of clause 1, wherein the process cycle is an automated process cycle; wherein the controller is an automation controller configured to control performance of the automated process cycle by the respective process station.

[0070] Clause 27. The system of clause 7, wherein the sequence of process steps includes at least one automated process step; and wherein the detailed data includes station data defined by the at least one automated process step.

[0071] Clause 28. The system of clause 1, wherein the process cycle includes at least one automated process step and at least one manual process step; and wherein the station data includes station data defined by each of the at least one automated process step and the at least one manual process step.

[0072] Clause 29. The system of clause 7, wherein the sequence of process steps includes at least one manual process step; and wherein the detailed data includes detailed data defined by performance of the manual process step.

[0073] Clause 30. The system of clause 1, further comprising: a user device in communication with the server; the user device configured to: display the station data; and transmit the critical station criteria to the server.

[0074] Clause 31. A method for capturing process data from a plurality of process stations, the method comprising: providing a plurality of process stations; each respective process station of the plurality of process stations: repeatedly performing a process cycle; and generating station data defined by each performance of the process cycle; wherein the station data includes detailed data and summary data; receiving the station data via a controller in communication with the plurality of process stations; collecting, via a data collector, the station data from the controller; receiving the station data from the data collector, via a server in communication with a database; the method further comprising the server: storing, in the database, the station data; associating, in tire database, the station data received from each respective process station with the respective process station; determining, using the station data, a station status of the respective process station as critical or non-critical; associating, in the database, the station status with the respective process station; and instructing the data collector to cease collection of the detailed data from each process station of the plurality of process stations having a non-critical station status.

[0075] Clause 32. The method of clause 31, further comprising the server: associating, in the database, critical station criteria with each respective process station; performing a comparison of the station data and the critical station criteria; and determining the station status of the respective process station using the comparison.

[0076] Clause 33. The method of clause 32, wherein: the critical station criteria includes at least one of an acceptable threshold, an acceptable limit, or an acceptable range of the station data; and the server is configured to compare the station data to the at least one of the acceptable threshold, the acceptable limit, and the acceptable range to determine the station status.

[0077] Clause 34. The method of clause 32, further comprising: analyzing, via the server, the station data to determine the critical station criteria.

[0078] Clause 35. The method of clause 32, further comprising: analyzing, via the server, the station data to determine a baseline of the station data; wherein the critical station criteria is defined by a deviation of the station data from the baseline.

[0079] Clause 36. The method of clause 31 further comprising the server: continuously receiving the station data from the data collector; continuously determining in real time, using the station data, a current station status of the respective process station as critical or non-critical; continuously associating in real time, in the database, the current station status with the respective process station; instructing the data collector to: when the current station status of the respective process station changes from critical to non-critical, cease collection of the detailed data from the respective process station; and when the current station status of the respective process station changes from non-critical to critical, reinstate collection of the detailed data from the respective process station.

[0080] Clause 37. The method of clause 31, wherein: the process cycle includes a sequence of process steps performed by the respective process station; the detailed data is defined by performance of at least one of the process steps of the sequence of process steps; and the summary data is defined by performance of the process cycle.

[0081] Clause 38. The method of clause 37, wherein: the detailed data includes a step time of each of the process steps of the sequence; and the summary data includes a cycle time of the respective process cycle. [0082] Clause 39. The method of clause 38, wherein the station data includes a timestamp generated by the controller for each of the process steps.

[0083] Clause 40. The method of clause 37, wherein the detailed data includes a condition state of the process steps of the sequence.

[0084] Clause 41. The method of clause 37, wherein the detailed data includes a fault code generated by the respective process station.

[0085] Clause 42. The method of clause 41, wherein the detailed data includes a fault time at which the fault code was generated.

[0086] Clause 43. The method of clause 42, wherein the fault time is defined by a timestamp generated with the fault code.

[0087] Clause 44. The method of clause 40, wherein the summary data includes an operating condition of the respective process station; and wherein the operating condition of the respective process station is defined by the condition state.

[0088] Clause 45. The method of clause 44, wherein the operating condition of the respective process station is a starved condition, a blocked condition, a faulted condition, or a running condition. [0089] Clause 46. The method of clause 44, wherein the summary data includes a condition time defined by the condition state.

[0090] Clause 47. The method of clause 46, wherein the condition time is a starved time, a blocked time, a faulted time, or a running time.

[0091 ] Clause 48. The method of clause 40, wherein the summary data includes a number of cycles performed by the respective process station.

[0092] Clause 49. The method of clause 31, wherein: the respective process station is configured to process a product including a plurality of components; the detailed data includes a component attribute of at least one component of the plurality of components; the summary data includes a product attribute of the product; and wherein the product attribute is defined by the component attribute.

[0093] Clause 0. The method of clause 49, wherein the component attribute is a dimension of the at least one component.

[0094] Clause 51. The method of clause 37, wherein: the detailed data includes at least one process input to the respective process cycle; and the summary data includes at least one process output of the respective process cycle.

[0095] Clause 52. The method of clause 51, wherein the at least one process input is related to the at least one process output by a cause-effect relationship.

[0096] Clause 53. The method of clause 51, wherein the station data defines a correlation between the at least one process output and the at least one process input.

[0097] Clause 54. The method of clause 31, wherein the respective process station includes at least one machine; wherein the at least one machine is configured to output the station data to the controller.

[0098] Clause 55. The method of clause 31, wherein the respective process station includes at least one sensing device; wherein the at least one sensing device is configured to output a sensor signal to the controller; and wherein at least one of the detailed data and the summary data is defined by the sensor signal.

[0099] Clause 56. The method of clause 31, wherein the process cycle is an automated process cycle; wherein the controller is an automation controller configured to control performance of the automated process cycle by the respective process station.

[00100] Clause 57. The method of clause 37, wherein the sequence of process steps includes at least one automated process step; and wherein the detailed data includes station data defined by the at least one automated process step.

[00101] Clause 58. The method of clause 31, wherein the process cycle includes at least one automated process step and at least one manual process step; and wherein the station data includes station data defined by each of the at least one automated process step and the at least one manual process step.

[00102] Clause 59. The method of clause 37, wherein the sequence of process steps includes at least one manual process step; and wherein the detailed data includes detailed data defined by performance of the manual process step.

[00103] Clause 60. The method of clause 31, further comprising: transmitting, via a user device in communication with the server, the critical station criteria to the server.

[00104] Clause 61. The method of clause 60, further comprising: receiving, via the user device, the critical station criteria from a user.

[00105] Clause 62. The method of clause 61, wherein the user device includes a user interface, the method further comprising: inputting the critical station criteria to the user device via the user interface.

[00106] Clause 63. The method of clause 31, further comprising: displaying, via a user device in communication with the server, the station data.

[00107] Clause 64. The method of clause 31, further comprising: displaying, via a user device in communication with the server, the station status of the plurality of process stations.

[00108] Clause 65. The method of clause 31, further comprising: instructing the data collector, via a user device in communication with the server, to cease collection of the detailed data from a selected process station of the plurality of process stations.

[00109] Clause 66. The method of clause 31, further comprising: instructing the data collector, via a user device in communication with the server, to reinstate collection of the detailed data from a selected process station of the plurality of process stations.

[00110] Clause 67. A non-transitory computer-readable storage medium comprising computer instructions stored thereon; wherein the computer instructions are configured to enable a computer to perform the method according to clause 31.

[00111] The detailed description and the drawings or figures arc supportive and descriptive of the invention, but the scope of the invention is defined solely by the claims. While some of the best modes and other embodiments for carrying out the claimed invention have been described in detail, various alternative designs and embodiments exist for practicing the invention defined in the appended claims.