Field of the disclosure

The present disclosure relates to a method for monitoring a condition of a hoist, particularly a mining hoist. Embodiments relate to a hoist monitoring system for monitoring a condition of a hoist.

Techmical background

For monitoring a condition, e.g. a bearing condition, of different types of machines, analy sis of x ibration signals is commonly used To obtain reliable and consistent outcomes from the analysis, usually stable machine operation conditions are required.

Hoists are used in various applications, including mining. Since hoists and especially their bearings are subject to wear or electrical current-induced damage, it is desired to monitor the condition of the hoist. For example, conditions of a hoist may be determined by analyzing vibration signals collected on the hoist. Commonly, a spectral analysis of the vibration signals is employed by using a Fourier analysis technique. This is especially useful in the case of production hoists, which usually operate in long cycles. In this case, there can be sufficiently long time periods of stable speed and load. Defect frequencies may be analyzed accurately, using for example data block processing based on fast Fourier transform algorithms.

Hoists arc large machines typically operating at low rotation speed ranging around 40 to 60 RPM. Accordingly, the vibration signal’s frequency components related to the rotation speed are typically low and they are affected by rotation speed variations. Consequently, defect frequencies of hoist components, like e.g. bearings, are typically also low. An accurate estimation using Fourier analysis is likely to require several rotations of the hoist’s drum, i.e. a long acquisition time. During the acquisition time, both loads and rotation speeds of the hoist may vary. In particular, the rotation speed can vary even within a single cycle. To analyze hoist dynamics and to conduct component diagnostics, particularly when the rotation speed varies or operation cycles are short, is a challenge In addition, some types of hoists, particularly service hoists, are operated at changing tool and speed. Moreover, they may be operated in short cycles. In such a case, there may be not enough time to collect sufficiently stable vibration records and to achieve a required frequency resolution tracing the fault. In addition to such variations of speed or load, also electrical interferences or a noisy env ironment may negatively affect the frequency resolutionand even completely mask the bearing signals.

It is therefore an object of the present disclosure to overcome at least some of the above- mentioned problems at least partially.

Summaa ofthe discfaure

In view of the above, a method for monitoring a condition of a hoist is provided. The method includes: measuring a rotation speed of a shaft of the hoist, measuring vibration signals on the hoist, extracting state variables from the measured vibration signals by applying a Kalman filter to the measured v ibration signals as a function of the measured rotation speed, and determining at least one key performance indicator for the hoist, based on the extracted state variables.

Also, a monitoring system for monitoring a condition of a hoist, according to the features of claim 14 is provided.

Further adv antages, features, aspects and details that can be combined w ith embodiments described herein are evident fem the dependent claims, claim combinations, the descriptionand the drawings.

Brief description of the Figures:

The details will be described in the following with reference to the figures, wherein

Fig. 1 is a flow chart illustrating a method for monitoring a condition of a hoist, according to aspects of the present disclosure; and

Fig. 2 is a schematic view of a hoist system according to aspects of the present disclosure. Detailed description of the Figures and of embodiments:

Reference will now be made in detail to the various embodiments, one or more examples of which are illustrated in each figure. Each example is provided by way of explanation and is not meant as a limitation. For example, features illustrated or described as part of one embodiment can be used on or in conjunction w ith an\ other embodiment to yield yet a further embodiment. It is intended that the present disclosure includes such modifications and variations.

Within the following description of the drawings, the same reference numbers refer to the same or to similar components. Generally, only the differences with respect to the individual embodiments are described. Unless specified otherwise, the description of a part or aspect in one embodiment can be applied to a corresponding part or aspect in another embodiment as well.

Fig. 1 is a chart illustrating a method for monitoring a condition of a hoist, according to aspects of the present disclosure. The hoist is preferably a mining hoist. An exemplary hoist is described further below with regard to Fig. 2. The hoist may include a hoist component being and/or interacting with a (at least partially) rotating hoist element. In particular, the hoist’s shaft may drive the rotating hoist element. In particular, the component can be or include a rotating component such as a rotor of the motor, a shaft, or a rotating bearing part, and/or a stationary component interacting with a rotating component, such as a stator of the motor or a stationary bearing part. The hoist component may include or be a bearing, particularly a rolling bearing.

In rolling bearings, a rolling element passing over a damaged zone of the bearing produces vibrations at fault frequencies related to the natural frequencies of the structure. The fault frequencies appear in pre-defined frequency ranges. By passing vibration signals via bandpass and/or notch filters and demodulating the signals, an energy related to the bearing damage can be extracted.

The method includes, in block 102, measuring a rotation speed of a shaft of the hoist. The rotation speed may be measured in various ways, for example by a tachometer. The tachometer may be directly or indirectly measuring the rotation speed of the shaft. For example, measuring the rotation speed of the hoist’s shaft by a tachometer may include measuring a rotation speed of a further shaft and calculating the rotation speed of the hoist’s shaft from the result. In particular, the further shaft may be directly or indirectly connected to the hoist’s shaft. An indirect connection of the shafts can be for example via a gearbox.

The method further includes, in block 104, measuring vibration signals on the hoist. In particular, the vibrations are measured on the hoist component. More particularly, the vibration signals are measured by a vibration sensor. The vibration sensor may include at least one of an acoustic emission sensor operating in the semi or ultrasonic range, accelerometer,optical vibration sensor, shock pulse sensor, strain gauge, microphone, or electrical sensor, such as a Rogowski coil. The method further includes, in block 106, extracting state variables from the measured vibration signals by applying a Kalman filter bo the measured vibration signals as a function of the measured rotation speed. In particular, a Kalman filter is an algorithm producing estimates of predefined state variables from measurement data. A Kalman filter may produce estimates and uncertainties of current state variables and update the estimates based on subsequently measured data using a weighted as erage The w eighting particularly depends on the uncertainties of the estimates. An advantage of the Kalman filter is that signal components having a known structure may be accurately tracked in a signal further including noise or signal components ha\ mg a different structure.

According to an aspect of the present application, the Kalman filter may be a Vold-Kalman filter, particularly a Vold-Kalman Order Tracking Filter Order tracking may be understood as extracting the periodic (sinusoidal) content of measurement data from a system under periodic loading or excitation. Periodic loading produces orders or harmonics at frequencies that are multiples of a fundamental tone. The orders may be regarded as amplitude and phase modulated carrier waves. The modulation function may be called a complex envelope. Applying a Vold-Kalman filter particularly includes defining as a constraint that the unknown complex envelopes are smooth and that the sum of the orders approximates the total measured signal. Details about application of a Vold-Kalman filter can be found in GADE, S,, HERLUFSEN, H„ KONSTANTIN-HANSEN, H„ VOLD, H. Characteristics of the Vold- Kalman Order Tracking Filter. Bruel & Kjaer Technical Review, 1999, No. 1 - 1999. According to an aspect of the present disclosure, the state variables may include excitation parameters for excitations at selected frequencies. The selected frequencies may include for example at least one suspected fault frequency of the hoist component. In particular, the excitation frequencies may be discrete excitation frequencies, for example at most 12, 10 or 8 discrete frequencies. The selected frequencies may be determined based on the measured rotation speed, e.g., as (integer and/or non-integer) multiples of a rotation frequency. Applying the Kalman filter has the advantage of fast convergence toward a signal, ft is also capable of notching out electrical frequencies interfering with c ibration signals Because the Kalman filter uses a small set of state variables (compared to Fourier transform techniques which allow a continuous set of excitations), the Kalman filter is able to achicc c a fast response time and determine the state \ ariablcs quickly Thus, the Kalman filter technique is constantly adjusted. Thereby, the problems mentioned in the introductory section are solved at least to some degree. Thus, by enabling an almost instantaneous yet relatively precise assessment of in-phase and quadrature components at a given speed, and by tracking their changes over flhe time, it becomes possible extract timely signals even in difficult scenarios such as start-ups, speed-changing periods and breakdowns.

In addition, when the state variables include excitation parameters at frequencies that scale with the rotational frequency of the hoist’s shaft, the Kalman filter technique is able to take into account machine speed and possibly other parameters. Thereby, a continuous stream of reliable data may be provided.

Results of the application of the filter may be directly synchronized with a controller, for example using an Open Platform Communications (OPC) standard. A quasi-instantaneous stale of the hoist may be captured. c\en during changes of rotation speed.

The selected frequencies may include integer and/or non-integer multiples of a rotation frequency corresponding to the measured rotation speed. In particular, the selected frequencies can include harmonics of a rotation frequency corresponding to the measured rotation speed. The selected frequencies may include sidebands of the harmonics, particularly as non-integer multiples of a rotation frequency corresponding to the measured rotation speed. The excitation parameters may include a phase and amplitude information of the excitations at the selected frequencies.

The method further includes, in block 108, determining at least one key performance indicator (KPI) for the hoist, based on the extracted state variables.

The KPI allows to provide an insight into hoist performance, which may be monitored over time. Thus, the use of a KPI allow s unified monitoring, displaying and establishing alarms. The determined KPI may be streamed continuously and reflect dynamic behavior of the hoist at any instance of its operation.

According to an aspect of the present disclosure, the determining of the at least one key performance indicator may include determining a vibration energy of the vibrations corresponding to at least one of the selected frequencies. Determining of the at feast one key performance indicator may include determining respective vibration energies for at least some of the selected frequencies, for example at feast 2. 3, or 5 of the selected frequencies

According to an aspect of the present disclosure, the detennming of the at least one key performance indicator may include weighted summing of the determined vibration energies.

According to an aspect of the present disclosure, the determining of the at least one key performance indicator may include determining at least two performance indicators by weigh ted summing of the determined vibration energies with different weights.

The at least one key performance indicator (KPI) may have the following general form: where: a harmonics weight p sidebands weight

Hi, n ^fe harmonics spectral energy

S _m m ^th sideband spectral energy

N number of predefined harmonics

M number of predefined sidebands

According to an aspect of the present disclosure, the method may include detennming in- phase and quadrature components of foe state variables. The method may include passing the measured x ibration signals through a band-pass filler and multi-notch and a demodulator.

According to an aspect of foe present disclosure, the method may further include evaluating at least one alarm condition for the at least one key performance indicator. In particular, foe at least one alarm condition corresponds t) a respective damage type for the hoist. More particularly, the damage type includes at least one of wear, fatigue, plastic flow, fracture, electrical erosion, or plastic deformation. The method may further include issuing an alarm signal when foe at least one alarm condition is satisfied. According to an aspect of the present disclosure, the method monitors the condition of a hoist component being and or interacting w ith a rotating element of the hoist, wherein the key performance indicator is indicative of the condition of the hoist component. The rotational speed of the rotating element may be at most for example 100, 80, or 60 rpm.

Fig. 2 is a schematic view of a hoist system according to aspects of the present disclosure. The hoist system includes a hoist and a hoist monitoring system for monitoring a condition of a hoist The hoist may include a motor 202 connected to a gear box 204 via a first shaft 208. The hoist may further include a drum 206 connected to the gear box 204 via a second shaft 210. The hoist may further include a hoist component being and/or interacting with a rotating hoist element. The hoist component is for example a first bearing 212. As show n in the depicted embodiment, the hoist may further include a second bearing 214.

The hoist monitoring system includes a tachometer 222 for measuring a rotation speed of a shaft of the hoist. In the depicted example, the second shaft 210 is a shaft of the hoist component. In particular, the second shaft 210 drives the rotating element of the hoist component. Measuring the rotation speed of a shaft of rhe rotating hoist component by a tachometer may include measuring a rotation speed of a further shaft, e.g. the first shaft 208 as shown, and calculating the rotation speed of the shaft of the rotating hoist component from the result.

The hoist monitoring system further includes a vibration sensor 224 for measuring vibration signals on the hoist. In particular, the vibrations are measured on the hoist component. The vibration sensor 224 may be attached to the hoist component, like for example the first bearing 212. The hoist monitoring system may include at least one additional vibration sensor. For example, as show n in the depicted embodiment, an additional \ ibration sensor 22S may be atached to the second bearing 214 of the hoist .

The hoist monitoring system further includes a controller 226. The controller 226 is connected to the tachometer 222 for receiving the measured rotation speed The controller 226 is connected to the vibration sensor 224 for receiving the measured vibration signals. The tachometer 222 and the vibration sensor 224 may be connected to the controller 230 wirelessly or via respective cables (not shown).

In embodiments, the vibration sensor may include at least one of an acoustic emission sensor, accelerometer, optical vibration sensor, shock pulse sensor, strain gauge, microphone, or electrical sensor. The acoustic emission sensor particularly operates in the semi or ultrasonic range. The electrical sensor can be for example a Rogowski coil.

The controller is configured for extracting state variables from the measured vibration signals by applying a Kalman filter to the measured vibration signals as a function of the measured rotation speed. The controller is further configured for determining at least one key performance indicator for the hoist, based on the extracted state variables.

The controller may be configured te> perform a method for monitoring a condition of a hoist as described herein, particularly with regard to Fig. 1.