Fault early warning method and system for driving motor of underground coal mine scraper conveying equipment

Abstract

The invention discloses a fault early warning method and system for a driving motor of underground coal mine scraper conveying equipment, and relates to the technical field of driving motors, and the method comprises the steps: collecting the current operation data of each index of the driving motor when the operation of the driving motor is monitored; predicting to-be-predicted current operation data in the indexes to obtain fault probabilities of the indexes in a preset time period; and obtaining fault early warning information of the driving motor according to the fault probability of each index. The method is suitable for protecting the driving motor before the driving motor breaks down, the fault occurrence probability can be effectively reduced, and meanwhile the loss of the driving motor can be reduced to the minimum.

Description

Technical Field

[0001] The present application relates to the technical field of drive motors, and in particular to a fault warning method and system for drive motors of scraper conveyor equipment in underground coal mines. Background Art

[0002] Scraper conveyors can be used for horizontal transportation or inclined transportation. When the coal seam has a large inclination angle, anti-skid devices should be installed. The working principle of the scraper conveyor is to use the open chute as a bearing for coal, gangue or materials, and fix the scraper on the chain (forming a scraper chain) as a traction component. When the head transmission part is started, it drives the sprocket on the head shaft to rotate, so that the scraper chain circulates and drives the material to move along the chute until it is unloaded at the head. The scraper chain bypasses the sprocket to perform a stepless closed cycle to complete the material transportation.

[0003] The main structure and components of the scraper conveyor are basically the same, consisting of three parts: the head, the middle part and the tail. In addition, there are also auxiliary parts such as the hydraulic jack device used for the push conveyor and the chain tightener used when tightening the chain. The head part is composed of the head frame, motor, hydraulic coupler, reducer and sprocket, the middle part is composed of the transition trough, middle trough, chain and scraper, and the tail is the device for the scraper chain to return. The tail of the heavy-duty scraper conveyor is also equipped with a power transmission device like the head, and the upper head and the lower head are distinguished by the installation position.

[0004] The drive motor of the scraper conveyor equipment provides power for the equipment, so the power output of the drive motor is particularly important. In order to ensure the stable power output of the drive motor and to be able to provide early warning before the drive motor fails, it is convenient for timely maintenance and replacement. Therefore, the existing technical solution is to install a three-axis acceleration sensor on the left and right front wheel suspensions and the drive motor protection cover of the pure electric vehicle; the vibration data collected by the three-axis acceleration sensor is output to the digital signal processor through the signal conditioning circuit module for conversion and processing, and the vibration spectrum information of the vibration intensity and frequency at the positions of the left and right front wheel suspensions and the drive motor protection cover is obtained, and the vibration spectrum information is transmitted to the vehicle controller of the pure electric vehicle through the CAN communication module for decoupling, and the independent real-time vibration power spectrum of the drive motor is obtained. The real-time vibration power spectrum is compared with the standard vibration power spectrum of the drive motor in the vehicle controller database, so that the vehicle controller can issue a prompt to the driver.

[0005] The shortcomings of the existing technical solutions are that, for the fault analysis of the drive motor, it cannot be simply judged by the vibration of the drive motor. The fault of the drive motor involves many factors, and it is necessary to combine various factors to determine the operating fault of the drive motor. In addition, the existing technical solutions are more inclined to realize the fault identification of the drive motor, rather than the fault warning of the drive motor. Summary of the invention

[0006] In view of this, the present application provides a fault warning method and system for the drive motor of underground scraper conveyor equipment in coal mines. The main purpose is to solve the technical problem that the existing drive motor fault warning considers a single influencing factor, and the existing technical solution is used to realize the fault identification of the drive motor, but cannot protect the drive motor through fault warning.

[0007] According to one aspect of the present application, a fault early warning method for a drive motor of a scraper conveyor equipment in an underground coal mine is provided, the method comprising:

[0008] When the drive motor is monitored to be running, the current operation data of various indicators of the drive motor are collected;

[0009] Predicting the current operating data to be predicted among the various indicators to obtain the failure probability of the various indicators within a predetermined time period;

[0010] According to the failure probabilities of the various indicators, the failure warning information of the drive motor is obtained.

[0011] According to another aspect of the present application, a fault warning system for a driving motor of a scraper conveyor equipment in an underground coal mine is provided, the system comprising:

[0012] The acquisition module is used to collect the current operation data of various indicators of the drive motor when the drive motor is monitored to be running;

[0013] A prediction module, used to predict the current operating data to be predicted among the various indicators, and obtain the failure probability of the various indicators within a predetermined time period;

[0014] The early warning module is used to obtain the fault early warning information of the drive motor according to the failure probability of each indicator.

[0015] By means of the above technical scheme, the fault warning method and system of the drive motor of the scraper conveyor equipment in the underground coal mine provided by the present application, compared with the technical scheme that the existing drive motor fault warning considers a single influencing factor, is only used to realize the fault identification of the drive motor, and cannot realize the protection of the drive motor by means of fault warning, the present application collects the current operation data of various indicators of the drive motor when the drive motor is monitored to be running; predicts the current operation data to be predicted among the various indicators to obtain the failure probability of the various indicators within a predetermined time period; obtains the failure warning information of the drive motor according to the failure probability of the various indicators. It can be seen that, unlike the existing technical scheme for identifying the failure of the drive motor based on a single influencing factor, the present application can comprehensively consider the influence of various indicators on the drive motor, and by predicting the drive motor in a normal operating state, so as to realize the protection of the drive motor before the drive motor fails, it can not only effectively reduce the probability of failure, but also minimize the loss of the drive motor.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A schematic flow chart of a method for early warning of a failure of a driving motor of a scraper conveyor device in an underground coal mine provided by an embodiment of the present application is shown;

[0019] Figure 2 A schematic flow chart of another method for early warning of a failure of a driving motor of a scraper conveyor equipment in an underground coal mine provided by an embodiment of the present application is shown;

[0020] Figure 3 A schematic diagram of the structure of a fault warning system for a driving motor of a scraper conveyor equipment in an underground coal mine provided by an embodiment of the present application is shown;

[0021] Figure 4 A structural schematic diagram of another fault warning system for a drive motor of an underground scraper conveyor equipment in a coal mine provided in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0022] The present application will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0023] The existing coal mine underground scraper conveyor equipment drive motor fault warning considers only a single factor, and the existing technical solution is used to realize the fault identification of the drive motor, but cannot realize the technical problem of protecting the drive motor through fault warning. This embodiment provides a coal mine underground scraper conveyor equipment drive motor fault warning method, which can comprehensively consider the influence of various indicators on the drive motor, and predict the drive motor in normal operation so as to realize the protection of the drive motor before the drive motor fails. It can not only effectively reduce the probability of failure, but also minimize the loss of the drive motor. Figure 1 As shown, the above method comprises the following steps:

[0024] Step 101: When the driving motor is monitored to be running, current operating data of various indicators of the driving motor are collected.

[0025] In this embodiment, during the operation of the drive motor, the current operating data of various indicators are collected through the angular velocity sensor, the voltage detection circuit, the current detection circuit, the temperature sensor and the oscillation sensor, or the current operating data are indirectly calculated, and the current operating data of each item are classified and processed, namely the angular velocity w of the drive motor, the voltage v of the drive motor, the temperature t of the drive motor and the vibration u of the drive motor. That is, by jointly analyzing and processing the voltage v of the drive motor, the angular velocity w of the drive motor, the temperature t of the drive motor and the vibration u of the drive motor, the operating state of the drive motor can be analyzed more comprehensively, thereby effectively reducing the probability of failure.

[0026] Step 102: predict the current operating data to be predicted in the various indicators to obtain the failure probability of the various indicators within a predetermined time period.

[0027] Step 103: Obtain fault warning information of the drive motor according to the fault probabilities of the various indicators.

[0028] In this embodiment, the fault tree analysis algorithm is used to analyze the collected current operation data, determine the current operation data to be predicted in each indicator, predict the current operation data to be predicted in the indicators, and obtain the failure probability of the indicators within a predetermined time period. It can be seen that by analyzing the current operation data of each indicator, it is determined which are in the edge range of the stable operation range, and by predicting the current operation data in the edge range for a period of time in the future, the probability that the current operation data may fail in a period of time in the future is determined. When the probability exceeds the preset value, it is determined that the indicator corresponding to the current operation data may fail in a period of time in the future, thereby generating fault warning information containing corresponding indicator information, so that relevant personnel can know the corresponding fault risk in advance, thereby effectively reducing the probability of fault occurrence and realizing the protection of the drive motor to the greatest extent.

[0029] For this embodiment, according to the above scheme, when the drive motor is monitored to be running, the current operating data of various indicators of the drive motor are collected; the current operating data to be predicted among the various indicators are predicted to obtain the failure probability of the various indicators within a predetermined time period; according to the failure probability of the various indicators, the fault warning information of the drive motor is obtained. Compared with the technical scheme that the existing drive motor fault warning considers a single influencing factor and is only used to realize the fault identification of the drive motor, and cannot realize the protection of the drive motor by means of fault warning, this embodiment can comprehensively consider the influence of various indicators on the drive motor, and predict the drive motor in normal operation so as to realize the protection of the drive motor before the drive motor fails, which can not only effectively reduce the probability of failure, but also minimize the loss of the drive motor.

[0030] Furthermore, as a refinement and expansion of the specific implementation of the above-mentioned embodiment, in order to fully explain the specific implementation process of this embodiment, another fault warning method for the drive motor of the scraper conveyor equipment in the coal mine is provided. This embodiment can be applied to the scraper conveyor equipment in the coal mine. This embodiment can comprehensively consider the influence of various indicators on the drive motor, and predict the drive motor in normal operation so as to protect the drive motor before the drive motor fails. It can not only effectively reduce the probability of failure, but also minimize the loss of the drive motor. Figure 2 As shown, the method includes:

[0031] Step 201: Acquire historical operation data of the drive motor.

[0032] Step 202: Calculate the historical operation data to obtain the stable operation range of various indicators.

[0033] During implementation, the stable operating range of the various indicators includes the stable operating range of the driving motor vibration parameters, and the interval data of the various indicators also includes one or more of the stable operating range of the driving motor voltage parameters, the stable operating range of the driving motor angular velocity parameters, and the stable operating range of the driving motor temperature parameters; the stable operating range is determined based on the maximum error and minimum error of the various indicators.

[0034] The historical operating data of the normal operation of the drive motor is calculated, and the stable operating range of each indicator is set based on the maximum error and minimum error of each indicator. Specifically, the maximum error and minimum error of each indicator are obtained by calculating the average relative error and the root mean square error of the historical operating data of normal operation, so as to quickly obtain the stable operating range of each indicator when the drive motor is operating normally, which is convenient for the subsequent analysis of the fault location of the drive motor and can realize early warning of subsequent faults of the drive motor.

[0035] Among them, the average relative error is, The root mean square error is,

[0036] Where n represents the total number of historical operation data items of a single indicator, i represents the number of items of the i-th historical operation data, and yi represents the i-th data information. Represents the average value of historical operation data, and the maximum error of the average relative error is determined as A max , the minimum error of the average relative error is A min =0, the maximum error of the root mean square error is R max , the minimum error of the root mean square error is R min =0.

[0037] During implementation, in order to illustrate the specific implementation method of step 202, as a preferred embodiment, a clustering algorithm is used to eliminate abnormal data values ​​in the historical operation data of the normal operation of the drive motor. The step 202 includes: determining abnormal data by clustering the historical operation data; obtaining the stable operation range of various indicators by eliminating the abnormal data in the historical operation data; wherein, according to the total number of historical operation data and the number of historical operation data of each cluster obtained by clustering processing, the outlier factor used to determine the abnormal data is calculated.

[0038] According to the needs of the actual application scenario, the historical operation data is clustered and divided into z clusters. The center of each cluster is determined, and the distance from each historical operation data point to the cluster center of its cluster is calculated; based on the distance, each historical operation data point is judged to be an outlier, and the outlier is removed as abnormal data. Among them, an outlier is defined as a historical operation data point whose distance from the cluster center of its cluster exceeds a set value (outlier factor), that is, abnormal data. It can be seen that in order to prevent the existence of abnormal data in the historical operation data and affect the calculation results of the stable operation range of various indicators, the abnormal data is removed through the clustering algorithm to ensure the accuracy of the historical operation data.

[0039] Furthermore, the historical operation data of the normal operation of the drive motor is divided into z clusters, where F is the outlier factor, D is the number of samples of the historical operation data of the normal operation of the drive motor, and |z j | is the number of samples in the jth cluster group, d(p,z j ) is the distance between the historical running data sample p and the jth cluster center (cluster center), z j is the jth cluster center. The calculation formula of the clustering algorithm is:

[0040]

[0041] It can be seen that by calculating the maximum error and minimum error of each indicator by performing average relative error and root mean square error on the historical operation data of normal operation, the stable operation range of each indicator during the normal operation of the drive motor can be quickly obtained, which is convenient for the subsequent analysis of the fault location of the drive motor and can realize early warning of subsequent faults of the drive motor.

[0042] Step 203: When the driving motor is monitored to be running, current operating data of various indicators of the driving motor are collected.

[0043] During implementation, in order to illustrate the specific implementation method of step 203, as a preferred embodiment, the current operating data of the various indicators include the vibration parameters of the drive motor, and the current operating data also includes one or more of the voltage parameters of the drive motor, the angular velocity parameters of the drive motor, and the temperature parameters of the drive motor.

[0044] During implementation, in order to illustrate the specific implementation method of step 203, as a preferred embodiment, taking the collection of angular velocity parameters of the drive motor as an example, the step of collecting the angular velocity parameters of the drive motor also includes: calculating the real-time angular velocity of the drive motor according to the angular velocity difference of the drive motor within a certain time period; correcting the real-time angular velocity according to the angular velocity calculated previously to obtain the corrected angular velocity parameters of the drive motor.

[0045] Specifically, the angular velocity w of the driving motor is detected by an angular velocity sensor. The angular velocity w of the driving motor is calculated by calculating the motor speed once within a 1 ms period of the rotating shaft, and the angle difference is Δθ=θ now -θ last , the real-time angular velocity is w now =Δθ*16.66, the actual angular velocity w is obtained by correction real =w now *τ1+w last *τ2, τ1, τ2 are weighted coefficients. Among them, the ideal motor speed W = (θ / 360*1000*60) rpm, the converted θ / 360*1000*60 is approximately equal to 16.66, the weighted average filter weight coefficients are τ1, τ2, the value range is between 0 and 1, the weight coefficient = ∑ (variable value weight) / ∑ weight, θ last and w last are all angular velocity parameters obtained in the previous calculation.

[0046] Step 204 : Filter the collected current operating data of various indicators of the drive motor using an FIR filter to obtain processed current operating data of various indicators of the drive motor.

[0047] In the implementation, in order to improve the accuracy of the current operating data of each indicator collected, the FIR filter can be used to remove the interference noise in the current operating data, thereby maintaining the security and accuracy of the current operating data of each indicator. Specifically, after the current operating data of each indicator is collected and transmitted, it is filtered by a FIR filter (finite length unit impulse response filter). Assume that the relationship between the output of the FIR filter with a length of M and the input time series z(n) is a finite convolution and relationship. For an M-1 order FIR filter, there are M taps, consisting of M multipliers and M-1 accumulators, and each tap requires a multiplier accumulator unit that consumes logical resources. Among them, z(n) is the input signal, h(g) is the FIR filter coefficient, v(n) is the current operating data after filtering, g is the g-th current operating data, and the calculation formula is:

[0048]

[0049] Step 205: Analyze the collected current operation data using a fault tree analysis algorithm to determine whether each indicator is in a fault state.

[0050] During implementation, in order to illustrate the specific implementation method of step 205, as a preferred embodiment, step 205 includes: using a fault tree analysis algorithm to analyze the collected current operating data in sequence to determine whether each indicator is in a fault state; wherein, the order of the sequential analysis is the voltage parameters of the drive motor, the angular velocity parameters of the drive motor, the temperature parameters of the drive motor, and the vibration parameters of the drive motor. According to the needs of the actual application scenario, the analysis order is not specifically limited.

[0051] During implementation, while predicting the fault of the drive motor, the fault tree analysis method is used to analyze the fault of the drive motor when the drive motor is running to determine whether the drive motor is currently faulty. If the drive motor is not running or is running abnormally, it is detected whether the switch is disconnected. If the switch is closed normally, the voltage v of the drive motor is further detected.

[0052] If the voltage v of the driving motor is within the positive and negative range of the rated voltage max range, and at positive and negative R max If the voltage v of the drive motor cannot simultaneously meet the positive and negative A values ​​of the rated voltage, the power supply voltage of the drive motor is stable. max and the rated voltage of positive and negative R max , then the fault is determined to be a voltage fault;

[0053] Further, the angular velocity w of the drive motor is detected. If the angular velocity w of the drive motor is 0 (A min ) <w<A max and at the same time meet 0(R min ) <w<R max , then the angular velocity w of the drive motor is normal. If the angular velocity w of the drive motor cannot meet 0 (A min ) <w<A max and 0(R min ) <w<R max , then the fault is determined to be an angular velocity fault;

[0054] Further, the temperature t of the drive motor is detected. If the temperature t of the drive motor is below 0 (A min ) <t<A max and at the same time meet 0(R min ) <t<R max , then the temperature of the drive motor is normal. If the temperature t of the drive motor cannot meet 0 (A min ) <t<A max and R min =0 <t<R max, then the fault is determined to be a temperature fault; the temperature t of the drive motor is detected by a temperature sensor. A temperature fault of the drive motor will directly burn the motor. Temperature fault warnings include overload operation, blockage of the drive motor heat dissipation port, damage to the drive motor exhaust fan, etc.;

[0055] Further, the vibration u of the driving motor is detected. If the vibration u of the driving motor is within 0 (A min ) <u<A max and at the same time meet 0(R min ) <u<R max , then the vibration of the drive motor is normal. If the vibration u of the drive motor cannot meet 0 (A min ) <u<A max and 0(R min ) <u<R max , then the fault is determined to be a vibration fault; the vibration u of the drive motor is detected by an oscillation sensor, and the vibration fault warning includes excessive friction between bearings, loose installation of the drive motor, etc.;

[0056] In addition, the voltage v detection of the drive motor includes voltage detection, current detection, phase loss detection and leakage detection, which correspond to the fault problems of damage to the internal components of the drive motor, short circuit, phase loss and leakage respectively. Specifically, the voltage and current are detected by the voltage detection circuit and the current detection circuit, and it is determined whether the drive motor has a phase loss according to the voltage and current, that is, if the voltage and current of each line are the same, there is no phase loss, and the voltage detection circuit is used to detect the voltage of the ground wire. If there is voltage on the ground wire, there is leakage in the drive motor, and if there is no voltage on the ground wire, there is no leakage in the drive motor.

[0057] It can be seen that by comparing the calculated stable operating range of various indicators during normal operation of the drive motor with the current operating data of various indicators collected in real time by the drive motor, the cause of the failure of the drive motor can be quickly determined, the fault can be located, and timely maintenance can be carried out.

[0058] Step 206: Calculate the current operating data of each indicator using a normalization algorithm to obtain a deviation value of each indicator.

[0059] Step 207: Determine the current operating data to be predicted in each indicator by comparing the deviation value with the corresponding threshold value.

[0060] Step 208: Calculate the current operating data to be predicted in the various indicators using a Poisson distribution algorithm to obtain the failure probability of the various indicators within a predetermined time period.

[0061] During implementation, after analysis and judgment using the fault tree analysis algorithm, the normalization algorithm is used to calculate the current operating data in the normal state to determine the deviation value of the current operating data in the normal state. When the deviation value exceeds the corresponding threshold (the threshold is set for each indicator), it is determined as the current operating data to be predicted in each indicator, and the Poisson distribution algorithm is used to predict the probability of failure of the drive motor after continuous operation for a certain period of time, thereby realizing early warning reminders.

[0062] Specifically, in the normalization algorithm, X is set as the current operating data of various indicators in the normal state, and the dependency relationship between Y and X is: Y i =α+βX i +ε i ; Among them, α and β are constants, collectively referred to as the parameters of the overall regression equation. α is the constant term of the overall regression equation, the intercept of the overall regression line on the Y axis, and β is the overall regression coefficient, which is the slope of the overall regression line; ε i is a random disturbance term, which is a random variable that cannot be directly observed, and i is the number of items of the current operating data in the i-th normal state.

[0063] Furthermore, the Poisson distribution algorithm is used to calculate the probability of an event occurring at a certain moment, that is, the maximum possibility that will be achieved; where k is the number of simulation calculations, and k is a positive integer between 0 and ∞, and λ is the expected value or variance of the current operating data to be predicted. The calculation formula is:

[0064]

[0065] It can be seen that when the drive motor is running, when the current operating data of various indicators collected are close to the edge range of the stable operating range (the deviation value of each indicator exceeds the corresponding threshold), failure may occur in the subsequent operation. Therefore, after the deviation of the current operating data of each indicator is calculated through the normalization algorithm, the probability of failure is calculated for the current operating data at the edge range of the stable operating range through the Poisson distribution algorithm, which is convenient for early warning and timely maintenance or replacement of equipment.

[0066] Step 209: Obtain fault warning information of the drive motor according to the fault probabilities of the various indicators.

[0067] During implementation, a failure probability threshold is set for each indicator, and the failure probability of each indicator calculated using the Poisson distribution algorithm is compared with the corresponding failure probability threshold to determine that the corresponding indicator may fail at some point in the future, and generate fault warning information containing the corresponding indicator information. The fault warning information can also be warning components such as indicator lights corresponding to the corresponding indicators.

[0068] By applying the technical solution of this embodiment, the angular velocity w of the drive motor, the voltage v of the drive motor, the temperature t of the drive motor and the vibration u of the drive motor are jointly analyzed and processed in combination with the stable operating range of various indicators of the drive motor calculated based on historical operating data. If the current operating data of various indicators of the drive motor are in the stable operating range, the drive motor is running stably. If they are beyond the stable operating range, the drive motor has a fault, and then the fault indicator is determined for easy maintenance. For early warning, this embodiment determines the current operating data to be predicted in various indicators by analyzing the deviation of the current operating data for the current operating data in the stable operating range, and then predicts whether the drive motor will fail in future use, which is convenient for early warning.

[0069] Further, as Figure 1 The specific implementation of the method, the embodiment of the present application provides a fault warning system for the drive motor of the scraper conveyor equipment in the coal mine, such as Figure 3 As shown, the system includes: a collection module 31 , a prediction module 36 , and an early warning module 37 .

[0070] The acquisition module 31 is used to acquire current operation data of various indicators of the drive motor when the drive motor is monitored to be running.

[0071] The prediction module 36 is used to predict the current operating data to be predicted in the various indicators to obtain the failure probability of the various indicators within a predetermined time period.

[0072] The early warning module 37 is used to obtain the fault early warning information of the driving motor according to the failure probability of each indicator.

[0073] In specific application scenarios, such as Figure 4 As shown, the current operating data of the various indicators include the vibration parameters of the drive motor, and the current operating data also includes one or more of the voltage parameters of the drive motor, the angular velocity parameters of the drive motor, and the temperature parameters of the drive motor.

[0074] In a specific application scenario, the acquisition module 31 is also used to acquire the angular velocity parameters of the drive motor, specifically including: calculating the real-time angular velocity of the drive motor according to the angular velocity difference of the drive motor within a certain time period; and correcting the real-time angular velocity according to the angular velocity calculated previously to obtain the corrected angular velocity parameters of the drive motor.

[0075] In a specific application scenario, the system further includes: a transmission module 32 .

[0076] The transmission module 32 is used to filter the collected current operation data of various indicators of the driving motor by using an FIR filter to obtain the processed current operation data of various indicators of the driving motor.

[0077] In a specific application scenario, the system further includes: a processing module 33 .

[0078] The processing module 33 is used to obtain the historical operation data of the drive motor; and calculate the historical operation data to obtain the stable operation range of various indicators; wherein the interval data of the various indicators include the stable operation range of the drive motor vibration parameters, and the interval data of the various indicators also include one or more of the stable operation range of the drive motor voltage parameters, the stable operation range of the drive motor angular velocity parameters, and the stable operation range of the drive motor temperature parameters.

[0079] In a specific application scenario, the processing module 33 includes: a clustering unit 331 and a elimination unit 332 .

[0080] The clustering unit 331 is used to determine abnormal data by clustering the historical operation data.

[0081] The elimination unit 332 is used to obtain the stable operation range of each indicator by eliminating the abnormal data in the historical operation data; wherein, the outlier factor used to determine the abnormal data is calculated according to the total number of historical operation data and the number of historical operation data of each cluster obtained by clustering processing.

[0082] In a specific application scenario, the system further includes: a first fault analysis module 34 .

[0083] The first fault analysis module 34 is used to analyze the collected current operation data using a fault tree analysis algorithm to determine whether various indicators are in a fault state.

[0084] In a specific application scenario, the system further includes: a second fault analysis module 35 .

[0085] The second fault analysis module 35 is used to calculate the current operating data of the various indicators using a normalization algorithm to obtain deviation values ​​of the various indicators; and to determine the current operating data to be predicted in each indicator by comparing the deviation values ​​with corresponding thresholds.

[0086] In a specific application scenario, the prediction module 36 is specifically used to calculate the current operating data to be predicted in the various indicators using a Poisson distribution algorithm to obtain the failure probability of the various indicators within a predetermined time period.

[0087] It can be seen that the acquisition module 31 is used to collect the current operating data of various indicators of the drive motor. The acquisition module 31 includes a voltage detection circuit, an angular velocity sensor, a current detection circuit, a temperature sensor and an oscillation sensor to detect the voltage v of the drive motor, the angular velocity w of the drive motor, the temperature t of the drive motor and the vibration u of the drive motor; the transmission module 32 is used to transmit the current operating data of various indicators collected by the acquisition module. The transmission module 32 includes an FIR filter, and the current operating data of various indicators collected are filtered by the FIR filter; the processing module 33 is used to collect the historical operating data of the drive motor during normal operation, and average the historical operating data. The relative error and the root mean square error are used to calculate the maximum error and the minimum error of each indicator; the first fault analysis module 34 is used to analyze and process the current operating data of each indicator through the fault tree analysis algorithm, and detect the voltage v of the drive motor, and then detect the angular velocity w of the drive motor, the temperature t of the drive motor, and the vibration u of the drive motor in turn; the second fault analysis module 35 is used to calculate the maximum error and the minimum error of each indicator calculated by the processing module 33 when there is no fault in the drive motor, calculate the deviation value of each indicator by using the normalization algorithm, and calculate the probability of failure of the drive motor after a certain period of continuous operation through the Poisson distribution algorithm, so as to provide an early warning reminder.

[0088] It should be noted that for other corresponding descriptions of the functional units involved in the fault warning system for the drive motor of the scraper conveyor equipment in the coal mine provided in the embodiment of the present application, reference can be made to Figure 1 and Figure 2 The corresponding description in will not be repeated here.

[0089] Based on the above Figure 1 and Figure 2 The method shown in the embodiment of the present application accordingly provides a computer storage medium on which a computer program is stored, and when the program is executed by a processor, the above-mentioned Figure 1 and Figure 2 The fault warning method of the drive motor of the scraper conveyor equipment in the coal mine is shown.

[0090] Based on this understanding, the technical solution of the present application can be embodied in the form of a software product, which can be stored in a storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present application.

[0091] Based on the above Figure 1 , Figure 2 The method shown, and Figure 3 , Figure 4In order to achieve the above-mentioned purpose, the embodiment of the present application further provides a computer device, which can be a personal computer, a server, a network device, etc. The physical device includes a storage medium and a processor; the storage medium is used to store a computer program; the processor is used to execute the computer program to achieve the above-mentioned Figure 1 and Figure 2 The fault warning method of the drive motor of the scraper conveyor equipment in the coal mine is shown.

[0092] Optionally, the computer device may further include a user interface, a network interface, a camera, a radio frequency (RF) circuit, a sensor, an audio circuit, a WI-FI module, etc. The user interface may include a display, an input unit such as a keyboard, etc., and the optional user interface may also include a USB interface, a card reader interface, etc. The network interface may optionally include a standard wired interface, a wireless interface (such as a Bluetooth interface, a WI-FI interface), etc.

[0093] Those skilled in the art will appreciate that the computer device structure provided in this embodiment does not constitute a limitation on the physical device, and may include more or fewer components, or a combination of certain components, or different component arrangements.

[0094] The storage medium may also include an operating system and a network communication module. The operating system is a program that manages the hardware and software resources of a computer device and supports the operation of information processing programs and other software and / or programs. The network communication module is used to achieve communication between components within the storage medium and with other hardware and software in the physical device.

[0095] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform, or by hardware. By applying the technical solution of the present application, compared with the existing drive motor fault warning, which considers a single influencing factor and is only used to realize the fault identification of the drive motor, and cannot realize the protection of the drive motor by means of fault warning, the present embodiment can comprehensively consider the influence of various indicators on the drive motor, and predict the drive motor in normal operation so as to realize the protection of the drive motor before the drive motor fails, which can not only effectively reduce the probability of failure, but also minimize the loss of the drive motor.

[0096] Those skilled in the art will appreciate that the accompanying drawings are only schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily necessary for implementing the present application. Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the present implementation scenario. The modules of the above-mentioned implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0097] The above serial numbers of this application are only for description and do not represent the advantages and disadvantages of the implementation scenarios. The above disclosure is only a few specific implementation scenarios of this application, but this application is not limited to them, and any changes that can be thought of by technicians in this field should fall within the scope of protection of this application.

Claims

1. A fault early warning method for a driving motor of a scraper conveyor equipment in a coal mine, characterized in that: include: When the drive motor is monitored to be running, the current operation data of various indicators of the drive motor are collected; Predicting the current operating data to be predicted among the various indicators to obtain the failure probability of the various indicators within a predetermined time period; According to the failure probabilities of the various indicators, the failure warning information of the drive motor is obtained.

2. The method according to claim 1, characterized in that The current operating data of the various indicators include a vibration parameter of the drive motor, and the current operating data also includes one or more of a voltage parameter of the drive motor, an angular velocity parameter of the drive motor, and a temperature parameter of the drive motor.

3. The method according to claim 2, characterized in that The step of collecting the angular velocity parameters of the driving motor includes: Calculating the real-time angular velocity of the driving motor according to the angular velocity difference of the driving motor within a certain time period; The real-time angular velocity is corrected according to the previously calculated angular velocity to obtain a corrected angular velocity parameter of the drive motor.

4. The method according to claim 1, characterized in that: After the step of collecting current operation data of various indicators of the drive motor when the drive motor is monitored to be running, the method further includes: The collected current operating data of various indicators of the driving motor are filtered by using an FIR filter to obtain the processed current operating data of various indicators of the driving motor.

5. The method according to claim 1, characterized in that The method further comprises: Acquiring historical operation data of the drive motor; Calculating the historical operating data to obtain the stable operating range of various indicators; Among them, the interval data of each indicator includes a stable operating range of a driving motor vibration parameter, and the interval data of each indicator also includes one or more of a stable operating range of a driving motor voltage parameter, a stable operating range of a driving motor angular velocity parameter, and a stable operating range of a driving motor temperature parameter.

6. The method according to claim 5, characterized in that The step of calculating the historical operation data to obtain the stable operation range of each indicator includes: Determining abnormal data by clustering the historical operation data; By eliminating abnormal data in the historical operation data, a stable operation range of each indicator is obtained; The outlier factor for determining abnormal data is calculated based on the total amount of historical operation data and the amount of historical operation data of each cluster obtained by clustering processing.

7. The method according to claim 1 or 5, characterized in that: After the step of collecting current operation data of various indicators of the drive motor when the drive motor is monitored to be running, the method further includes: The fault tree analysis algorithm is used to analyze the collected current operating data to determine whether each indicator is in a fault state.

8. The method according to claim 1, characterized in that Before the step of predicting the current operating data to be predicted in the various indicators to obtain the failure probability of the various indicators within a predetermined time period, the step further includes: Calculating the current operating data of the various indicators using a normalization algorithm to obtain deviation values ​​of the various indicators; By comparing the deviation value with the corresponding threshold value, the current operating data to be predicted in each indicator is determined.

9. The method according to claim 1 or 8, characterized in that: The step of predicting the current operating data to be predicted among the various indicators to obtain the failure probability of the various indicators within a predetermined time period includes: The Poisson distribution algorithm is used to calculate the current operating data to be predicted in the various indicators to obtain the failure probability of the various indicators within a predetermined time period.

10. A fault warning system for a driving motor of a scraper conveyor equipment in a coal mine, characterized in that: include: The acquisition module is used to collect the current operation data of various indicators of the drive motor when the drive motor is monitored to be running; A prediction module, used to predict the current operating data to be predicted among the various indicators, and obtain the failure probability of the various indicators within a predetermined time period; The early warning module is used to obtain the fault early warning information of the drive motor according to the failure probability of each indicator.

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