Coal mining machine fault diagnosis method and system

Abstract

The present invention provides a coal mining machine fault diagnosis method and system, which relates to the technical field of coal mining machine fault diagnosis. The coal mining machine fault diagnosis method includes: obtaining real-time working data of the coal mining machine; calculating the effective value of the coal mining machine within a preset time period based on the real-time working data; calculating the fault value of the coal mining machine based on the effective value of the coal mining machine; comparing the fault value with a preset alarm threshold value, and if the fault value is greater than the preset alarm threshold, storing the real-time working data corresponding to the fault value as the fault working data; inputting the fault working data into a preset fault diagnosis model to determine whether the coal mining machine has a fault; if the coal mining machine has a fault, outputting the fault type and fault location; if the coal mining machine has no fault, stopping the operation of the preset fault diagnosis model. The coal mining machine fault diagnosis method of the present invention solves the technical problems in the related art of large computational complexity and easy misjudgment due to environmental influences when diagnosing coal mining machine faults.

Description

Technical Field

[0001] The present invention relates to the technical field of coal shearer fault diagnosis, and in particular to a coal shearer fault diagnosis method and system. Background Art

[0002] The proper operation of shearers ensures the production efficiency of fully mechanized mining faces. However, the underground working environment is extremely harsh. During operation, shearers are subjected not only to massive impact loads from coal and rock, but also to pollution from coal dust and gas. Furthermore, the various key components of shearers inevitably wear and damage over long periods of operation, leading to shearer failures. Due to the noisy underground environment, faults such as bearing wear and fracture are difficult to detect early. Once a fault develops to a critical stage, it not only causes the shearer to shut down, but can also severely impact the production line and even cause safety accidents. Therefore, research on shearer fault diagnosis technology has important practical significance and economic value.

[0003] At present, the main method for diagnosing coal mining machine faults is to monitor various parameters of the coal mining machine (such as temperature, vibration, noise, etc.) and set thresholds to determine whether the coal mining machine has failed, or to use artificial intelligence methods to obtain a large amount of sensor information to train deep learning models.

[0004] However, when a coal mining machine is operating, when it cuts hard objects such as rocks or large pieces of coal collapse, vibration and noise will be increased. It is easy to misjudge whether the coal mining machine has a fault by monitoring its various parameters. Artificial intelligence methods usually require the acquisition and storage of a large amount of information. In addition, the data used in general fault diagnosis methods are all obtained in a relatively stable environment. Directly applying them to coal mining machines can also easily lead to misjudgments.

[0005] Since the vibration characteristics of coal mining machines are significantly different from those in other fields (such as aerospace, automobiles, machine tools, wind power generation, etc.), mainly reflected in the vibration source, working environment, load characteristics, frequency range, monitoring methods and safety requirements, although the deep learning model has achieved certain results in coal mining machine fault diagnosis, there are still problems such as model complexity and large computational complexity to a certain extent. This leads to its slow operation on resource-constrained equipment when applied in real time. Moreover, the long-term operation of the deep learning model during non-fault periods will also cause a waste of resources.

[0006] Therefore, the prior art needs to be further developed. Summary of the Invention

[0007] The purpose of the present invention is to overcome the above technical deficiencies and provide a coal mining machine fault diagnosis method and system to solve the technical problems in the related art that coal mining machine fault diagnosis requires large amount of calculation and is easily misjudged due to environmental influences.

[0008] In order to achieve the above technical objectives, the present invention adopts the following technical solutions: a coal mining machine fault diagnosis method is provided, including: obtaining real-time working data of the coal mining machine; calculating the effective value of the coal mining machine within a preset time period based on the real-time working data; calculating the fault value of the coal mining machine based on the effective value of the coal mining machine; comparing the fault value with the preset alarm threshold, if the fault value is greater than the preset alarm threshold, storing the real-time working data corresponding to the fault value as the fault working data; inputting the fault working data into a preset fault diagnosis model to determine whether the coal mining machine has a fault; if the coal mining machine has a fault, outputting the fault type and fault location, and if the coal mining machine has no fault, stopping the operation of the preset fault diagnosis model.

[0009] Furthermore, the real-time working data includes vibration raw data and noise raw data. The vibration raw data includes the acceleration values of the coal mining machine in the X, Y, and Z axes collected by the vibration sensor, and the noise raw data includes the analog voltage values collected by the noise sensor.

[0010] Furthermore, the effective value of the coal mining machine includes the effective value of speed, the effective value of displacement and the effective value of noise decibels.

[0011] Furthermore, the method for calculating the effective value of the coal shearer within a preset time period based on real-time working data includes: calculating the effective value of the speed of the coal shearer based on the original vibration data; and calculating the effective value of the displacement of the coal shearer based on the effective value of the speed of the coal shearer.

[0012] Furthermore, the method for calculating the effective value of the speed of the coal mining machine is:

[0013] ;

[0014] in, v'(t) is the instantaneous value of speed, a(t) For coal mining machines X,Y,Z The instantaneous value of acceleration in the three-axis direction, v is the effective value of speed, T is a preset time period;

[0015] The method for calculating the effective value of the displacement of the coal mining machine is:

[0016] x=v*T ;

[0017] in, x is the effective value of displacement, v is the effective value of speed, T The preset time period.

[0018] Furthermore, the method for calculating the effective value of the coal mining machine within a preset time period based on the real-time working data includes: calculating the effective value of noise based on the original noise data, wherein the method for calculating the effective value of noise is:

[0019] ;

[0020] in, p is the sound pressure, u(t) is the analog voltage value collected by the noise sensor, S is the sensitivity of the noise sensor, L is the effective value of noise in decibels, p 0 is the reference sound pressure, in air, p 0 =2×10 -5 pa .

[0021] Furthermore, the method for calculating the fault value based on the effective value of the coal mining machine includes:

[0022] K=a(x+v)+bW+cL ;

[0023] in, K is the fault value, a is the vibration response coefficient, b is the temperature response coefficient, c is the noise decibel response coefficient, 0< a<1 and 0<b<1 and 0<c<1 and a+b+c=1 , x is the effective value of displacement, v is the effective value of speed, W is the real-time temperature of the coal mining machine, L It is the effective value of noise in decibels.

[0024] Furthermore, if the fault value is greater than a preset alarm threshold, the real-time working data corresponding to the fault value is recorded; if the fault value is less than or equal to the preset alarm threshold, the real-time working data corresponding to the fault value is discarded.

[0025] On the other hand, a coal mining machine fault diagnosis system is provided, which includes: a data acquisition unit, which is used to obtain real-time working data of the coal mining machine; a first calculation unit, which is used to calculate the effective value of the coal mining machine within a preset time period based on the real-time working data; a second calculation unit, which is used to calculate the fault value of the coal mining machine based on the effective value of the coal mining machine; a fault monitoring unit, which is used to compare the fault value with the preset alarm threshold, and if the fault value is greater than the preset alarm threshold, run a preset fault diagnosis model; a fault diagnosis unit, which is used to input real-time working data into a preset fault diagnosis model to determine whether there is a fault in the coal mining machine; an output unit, which is used to output the fault type and fault location if there is a fault in the coal mining machine, and stop running the preset fault diagnosis model if there is no fault in the coal mining machine.

[0026] On the other hand, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the various steps of the coal mining machine fault diagnosis method are implemented.

[0027] Beneficial effects:

[0028] 1. The present invention assigns weights to different working parameters of the coal shearer based on the actual working environment of the coal shearer, and calculates multiple working parameters within a period of time as an effective value. This reduces fluctuations in the vibration and noise data of the coal shearer caused by external environmental factors such as cutting hard objects such as rocks and falling off of large pieces of coal seams during operation, which may cause the fault diagnosis system to misjudge the working status of the coal shearer.

[0029] 2. The present invention compares the effective value of the coal mining machine with a preset fault threshold, filters out data that may cause faults, and then performs specific fault diagnosis. Data that does not exceed the fault threshold is not stored, thereby reducing the amount of data storage and saving hardware resources. At the same time, only the filtered data is input into the fault diagnosis model for fault diagnosis, which not only reduces the amount of calculation of the fault diagnosis model and saves hardware resources, but also optimizes data quality and improves the accuracy of fault diagnosis.

[0030] 3. The present invention sets up two sets of data acquisition systems, one of which is used to collect the actual working parameters of the coal mining machine, and the other is used to collect the effective values of the coal mining machine working parameters based on the actual working parameters of the coal mining machine. After the system responsible for collecting the effective values of the working parameters filters out the data that may have faults, the system for collecting the actual working parameters selectively stores the actual working parameters corresponding to the data that may have faults. In this way, not only can the storage amount of data be reduced, but the system and fault diagnosis model used to collect the actual working parameters of the coal mining machine are in a stopped state during the data screening process. It can also avoid the waste of resources caused by the long-term operation of the data acquisition system and the fault diagnosis model, and achieve the effect of quickly processing a small amount of effective data and outputting accurate diagnostic results. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 This is the process of the coal mining machine fault diagnosis method adopted in the embodiment of the present invention;

[0032] Figure 2 4 is a block diagram of a coal mining machine fault diagnosis system used in an embodiment of the present invention. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0034] According to an embodiment of the present invention, a method for diagnosing faults of a coal mining machine is provided. Figure 1 ,include:

[0035] S100 obtains real-time working data of the coal mining machine;

[0036] Specifically, the real-time working data includes vibration raw data and noise raw data. The vibration raw data includes the acceleration values of the coal mining machine in the X, Y, and Z axes collected by the vibration sensor, and the noise raw data includes the analog voltage values collected by the noise sensor.

[0037] In actual practice, the real-time working data of the coal mining machine requires a very high sampling frequency to receive vibration and noise information with high intensity, so a sufficiently large database is required to store this data.

[0038] S200 calculates the effective value of the coal mining machine within a preset time period based on real-time working data;

[0039] In this embodiment, the effective value of the coal mining machine includes the effective value of speed, the effective value of displacement and the effective value of noise decibels.

[0040] Specifically, the method for calculating the effective value of the coal mining machine within a preset time period based on real-time working data includes:

[0041] The effective value of the speed of the coal mining machine is calculated based on the original vibration data. The method for calculating the effective value of the speed of the coal mining machine is:

[0042] ;

[0043] in, v'(t) is the instantaneous value of speed, a(t) For coal mining machines X,Y,Z The instantaneous value of acceleration in the three-axis direction, v is the effective value of speed, T is a preset time period;

[0044] The method for calculating the effective value of the displacement of the coal mining machine is:

[0045] x=v*T ;

[0046] in, x is the effective value of displacement, v is the effective value of speed, T The preset time period.

[0047] In specific practice, the method for calculating the effective value of the coal mining machine within a preset time period based on real-time working data includes: calculating the effective value of noise based on the original noise data, wherein the calculation method of the effective value of noise is:

[0048] ;

[0049] in, p is the sound pressure, u(t) is the analog voltage value collected by the noise sensor, S is the sensitivity of the noise sensor, L is the effective value of noise in decibels, p 0 is the reference sound pressure, in air, p 0 =2×10 -5 pa .

[0050] In this way, by calculating multiple data within the preset time period T, the effective value within the time period is obtained, which effectively reduces the storage volume of the coal mining machine's working data, filters and stores data that may cause faults, and discards data that does not cause faults, saving hardware resources and avoiding waste of resources.

[0051] S300 calculates a fault value of the coal shearer based on the effective value of the coal shearer;

[0052] Specifically, the method for calculating the fault value based on the effective value of the coal mining machine includes:

[0053] K=a(x+v)+bW+cL ;

[0054] in, K is the fault value, a is the vibration response coefficient, b is the temperature response coefficient, c is the noise decibel response coefficient, 0< a<1 and 0<b<1 and 0<c<1 and a+b+c=1 , x is the effective value of displacement, v is the effective value of speed, W is the real-time temperature of the coal mining machine, L It is the effective value of noise in decibels.

[0055] In this embodiment, the calculation method of a, b, and c is:

[0056] ;

[0057] in, βa , βb , βc are the average diagnostic accuracy of vibration, temperature, and noise, respectively.

[0058] Specifically, the original fault and non-fault data corresponding to vibration, temperature, and noise in the same time period are respectively input into the CNN model for training to obtain the average diagnostic accuracy of vibration, temperature, and noise.

[0059] It's important to note that the CNN model, through its convolution-pooling-fully connected hierarchical structure, achieves powerful modeling capabilities for high-dimensional data. Its core value lies in automatic feature learning and efficient parameter management. In industrial scenarios, such as coal mining machine vibration analysis, sensor data can be converted into spectrograms or time-frequency graphs, which are then used by CNN for feature extraction and fault classification.

[0060] In other embodiments, the three response coefficients a, b, and c can be assigned values respectively through a large amount of data collected during the operation of the coal mining machine and rich experience in coal mining machine operation, and the values of a, b, and c can be adjusted at any time according to the accuracy of fault diagnosis. Specifically, during the operation of the coal mining machine, vibration is the data that can most directly reflect the operating status of the coal mining machine. Therefore, according to work experience, the vibration response coefficient (i.e., a) is usually large. Secondly, temperature can also reflect the working status of the coal mining machine, but the temperature of the coal mining machine is easily affected by the environment, and the temperature fluctuations in different environments will also be different. Therefore, the temperature response coefficient (i.e., b) is usually determined based on the actual working data collected by the coal mining machine in the same or similar working environment. At the same time, noise can also indirectly reflect the operating status of the coal mining machine, but the noise parameters of the coal mining machine are also easily affected by the environment. The environmental noise will interfere with the noise parameters of the coal mining machine, so the noise response coefficient (i.e., c) also needs to be collected by the coal mining machine in the same or similar noise environment to determine the noise response coefficient (i.e., c). In summary, the three response coefficients are usually set to a>b>c .

[0061] Since the fault diagnosis method of the coal mining machine is mainly to determine whether a fault occurs by monitoring the vibration, temperature and noise of the coal mining machine, the vibration response coefficient + temperature response coefficient + noise response coefficient = 1 (that is, a + b + c = 1).

[0062] Because the vibration characteristics of coal shearers differ significantly from those of other fields, the primary vibration source is the impact load from cutting coal and rock. However, these vibrations are also affected by environmental factors (such as extreme operating conditions like large coal chunks falling off, uneven coal and rock hardness), or the combined effects of mechanical transmission, cutting loads, and travel mechanism vibrations. Therefore, assigning values to the vibration response coefficients based on the actual operating parameters of the coal shearer can greatly improve the applicability of the fault diagnosis model and the accuracy of the fault diagnosis model's output results. By collecting a large number of operating parameters from the coal shearer during actual operation and selecting the actual operating parameters from an environment identical or similar to the actual operating environment of the coal shearer to determine the three response coefficients, the calculation of the fault value can be made more consistent with the actual operating conditions, thereby making the fault diagnosis results of the coal shearer more accurate.

[0063] It should be noted that in this embodiment, after the coal mining machine starts working, the values of a, b, and c can be adjusted in real time according to the accuracy of the fault diagnosis model output structure. Adjusting the setting of the response coefficient according to the on-site working conditions can make the fault value more accurately reflect the working status of the coal mining machine and improve the accuracy of the coal mining machine fault diagnosis model.

[0064] In some embodiments, the calculation of the effective value is related to the coal seam cutting hardness. Specifically, the coal seam hardness parameter is obtained in real time by sensors (such as cutting resistance sensors and acoustic emission sensors) of the coal mining machine cutting part. H , according to the cutting hardness parameter H , dynamically calculate the weight coefficient of triaxial vibration data a x (H) 、 a y (H) 、 a z (H) , based on the weight coefficient, calculate the dynamic weighted vibration effective value v 动态 (H) , the calculation formula is:

[0065] ;

[0066] in, v 动态 (H) is the dynamic weighted vibration effective value, a x (H) 、 a y (H) 、 a z (H) is the weight coefficient of the triaxial vibration data, is the instantaneous value of the triaxial vibration velocity.

[0067] In this way, by dynamically associating the cutting hardness parameters with the three-axis vibration weights, the shortcomings of traditional methods in vibration data processing under complex working conditions are solved. When cutting coal seams of different hardness, the effective vibration value of the coal mining machine can be accurately calculated.

[0068] S400 compares the fault value with a preset alarm threshold. If the fault value is greater than the preset alarm threshold, the real-time working data corresponding to the fault value is stored as the fault working data.

[0069] Specifically, if the fault value calculated in step S300 is greater than the preset alarm threshold, it indicates that the state of the coal mining machine within the preset time period T may be abnormal. In this case, it is necessary to record the real-time working data corresponding to the fault value, and then further analyze and judge the real-time state of the coal mining machine;

[0070] If the fault value is less than or equal to the preset alarm threshold, it means that the coal mining machine is working normally within the preset time period T. Therefore, the real-time working data within the preset time period T can be discarded. This not only reduces the data storage volume and saves hardware resources, but also improves the accuracy of fault diagnosis.

[0071] In specific practice, set the alarm threshold K T , when K>K T , the preset fault diagnosis model is triggered, and the real-time working data of the coal mining machine obtained within the preset time period T corresponding to the fault value K is input into the preset fault diagnosis model to further determine whether there is a fault. If there is no fault, the preset fault diagnosis model is stopped. At the same time, during the calculation of the fault value K, the preset fault diagnosis model does not need to be run, so as to avoid the waste of resources caused by long-term real-time operation of the fault diagnosis model.

[0072] S500 inputs the fault working data into a preset fault diagnosis model to determine whether the coal mining machine has a fault;

[0073] In practice, the pre-set fault diagnosis model is trained using a large amount of operating data collected during the actual operation of the shearer. This data is used to collect both normal operating data and fault data for different operating environments. Furthermore, a portion of this data can be collected during the operation of the shearer as a test set to evaluate the performance of the trained model. Compared to data acquired in a more stable environment, data collected during actual operation can more accurately determine the shearer's real-time operating status, improving the accuracy of fault diagnosis.

[0074] In this embodiment, the real-time working data input into the fault diagnosis model is first screened, and the real-time working data corresponding to the possible fault of the coal mining machine (i.e., fault working data) is obtained after screening, and the fault working data is input into the trained fault diagnosis model. In this way, the calculation amount of the fault diagnosis model is reduced, the calculation speed of the fault diagnosis model is improved, and the accuracy of the fault diagnosis model can be improved.

[0075] If there is a fault in the coal mining machine, S600 outputs the fault type and fault location. If there is no fault in the coal mining machine, it stops running the preset fault diagnosis model.

[0076] In actual practice, the fault diagnosis model will not be run under normal circumstances. It will only be started when the fault value detected is greater than the preset alarm threshold. This can avoid the waste of resources caused by the long-term operation of the fault diagnosis model during non-fault periods.

[0077] In some embodiments, when the coal mining machine is working, after the fault diagnosis model determines the fault type and fault location, the operator records and reviews it in a timely manner. If the output result of the fault diagnosis model is inaccurate, the operator needs to record and provide feedback in a timely manner, and optimize the model by adjusting the model structure (such as increasing the number of layers or neurons of the neural network), adjusting hyperparameters (such as learning rate, regularization coefficient), increasing the amount of data or re-performing feature engineering, and then retraining and evaluating until satisfactory results are obtained.

[0078] The present embodiment provides a coal mining machine fault diagnosis system, which includes: a data acquisition unit, which is used to obtain real-time working data of the coal mining machine; a first calculation unit, which is used to calculate the effective value of the coal mining machine within a preset time period based on the real-time working data; a second calculation unit, which is used to calculate the fault value based on the effective value of the coal mining machine; a fault monitoring unit, which is used to compare the fault value with a preset alarm threshold value. If the fault value is greater than the preset alarm threshold, the real-time working data corresponding to the fault value is stored as the fault working data; a fault diagnosis unit, which is used to input the real-time working data into a preset fault diagnosis model to determine whether there is a fault in the coal mining machine; and an output unit, which is used to output the fault type and fault location if there is a fault in the coal mining machine.

[0079] With the above-mentioned setting, the first calculation unit and the second calculation unit perform data calculation on the data collected by the data collection unit to obtain the fault value of the coal mining machine. The fault monitoring unit monitors the fault value of the coal mining machine to obtain data corresponding to the time period when the coal mining machine may fail (that is, the fault value is greater than the preset time period corresponding to the preset alarm threshold). The above-mentioned data is input into the fault diagnosis unit to further diagnose the working status of the coal mining machine. In this way, the screened working data of the coal mining machine is input into the fault diagnosis unit, and a small amount of possible fault data is used for fault diagnosis. This not only reduces the load of the model and saves hardware resources, but also improves the accuracy of the fault diagnosis unit in judging the coal mining machine fault.

[0080] It should be noted that before the fault monitoring unit determines that the fault value is greater than the preset alarm threshold, the fault diagnosis unit is in a stopped state to avoid wasting resources caused by long-term real-time operation of the fault diagnosis model.

[0081] Example 1:

[0082] The coal mining machine fault diagnosis method of this embodiment adopts two sets of data acquisition systems, wherein the first data acquisition system is used to collect the original data of vibration and noise. The original data of vibration is the acceleration value in the three-axis direction of X, Y, and Z collected by the vibration sensor, and the original data of noise is the analog voltage value collected by the noise sensor. These values require a very high sampling frequency to restore the vibration and noise information with high intensity, so a sufficiently large database is required to store these data. The second data acquisition system is used to collect the calculated values of vibration and noise, including vibration displacement, vibration velocity, noise decibels, and temperature values. These values are calculated by the acceleration value of the vibration sensor and the analog voltage value of the noise sensor. The calculated value is the effective value within the t period, which reduces the amount of data storage.

[0083] Specifically, the vibration displacement threshold, vibration velocity threshold, temperature threshold, and noise decibel threshold are set through the second data acquisition system. Once any parameter is detected to exceed the threshold, the first data acquisition system is triggered, and the original data collected by the first data acquisition system is input into the preset fault diagnosis model to further determine whether there is a fault. If no fault exists, the first data acquisition system is stopped. If a fault exists, the fault type and fault location are output.

[0084] In this way, using a small amount of possible fault data to input the fault diagnosis model for fault diagnosis not only reduces the model's load and calculation amount, saves hardware resources, but also improves the fault diagnosis accuracy.

[0085] This embodiment provides a computer-readable storage medium, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, each step of the coal mining machine fault diagnosis method is implemented.

[0086] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0087] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0088] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0089] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0090] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0091] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for diagnosing coal mining machine faults, characterized in that: including: Obtain the real-time working data of the coal mining machine; Calculate the effective value of the coal mining machine within a preset time period based on the real-time working data, where the effective value of the coal mining machine includes speed effective value, displacement effective value, and noise decibel effective value; Calculate the fault value of the coal mining machine based on the effective value of the coal mining machine. The method for calculating the fault value based on the effective value of the coal mining machine includes: K = a(x + v) + bW + cL, in, K is the fault value, a is the vibration response coefficient, b is the temperature response coefficient, c is the noise decibel response coefficient, 0<1<a and 0 < b < 1 and 0 < c < 1 and a + b + c = 1 , x is the effective value of displacement, v is the effective value of speed, W is the real-time temperature of the coal mining machine, L is the effective value of noise in decibels, where a, b, c The calculation method is: ， ， ， in, βa, βb, βc are the average diagnostic accuracy of vibration, temperature, and noise, respectively; Compare the size of the fault value with a preset alarm threshold. If the fault value is greater than the preset alarm threshold, store the real-time working data corresponding to the fault value as fault working data; Input the fault working data into a preset fault diagnosis model to determine whether the coal mining machine has a fault; If the coal mining machine has a fault, output the fault type and fault location. If the coal mining machine has no fault, stop running the preset fault diagnosis model.

2. The coal mining machine fault diagnosis method according to claim 1, characterized in that: The real-time working data includes vibration original data and noise original data. The vibration original data includes the acceleration values of the coal mining machine in the X, Y, and Z axis directions collected by vibration sensors, and the noise original data includes the analog voltage values collected by noise sensors.

3. The coal mining machine fault diagnosis method according to claim 2, characterized in that: The method for calculating the effective value of the coal mining machine within a preset time period based on the real-time working data includes: Calculate the speed effective value of the coal mining machine based on the vibration original data; Calculate the displacement effective value of the coal mining machine based on the speed effective value of the coal mining machine.

4. The coal mining machine fault diagnosis method according to claim 3, characterized in that: The method for calculating the speed effective value of the coal mining machine is: ， ， Among them, v'(t) is the instantaneous value of velocity, is the instantaneous value of the acceleration of the coal mining machine in the X, Y, and Z directions, v is the effective value of the velocity, and T is the preset time period; The method for calculating the displacement effective value of the coal mining machine is: ， where x is the displacement effective value, v is the speed effective value, and T is the preset time period.

5. The coal mining machine fault diagnosis method according to claim 2, characterized in that: The method for calculating the effective value of the coal mining machine within a preset time period based on the real-time working data includes: calculating the noise decibel effective value based on the noise original data, where the calculation method for the noise decibel effective value is: Where p is the sound pressure, u(t) is the analog voltage value collected by the noise sensor, S is the sensitivity of the noise sensor, and L is the effective value of the noise in decibels. p 0 is the reference sound pressure, in air, 。 6. The coal mining machine fault diagnosis method according to claim 1, characterized in that: If the fault value is greater than the preset alarm threshold, record the real-time working data corresponding to the fault value; If the fault value is less than or equal to the preset alarm threshold, discard the real-time working data corresponding to the fault value.

7. A coal mining machine fault diagnosis system, characterized in that: The coal mining machine fault diagnosis system includes: A data acquisition unit for obtaining the real-time working data of the coal mining machine; A first calculation unit for calculating the effective value of the coal mining machine within a preset time period based on the real-time working data, where the effective value of the coal mining machine includes speed effective value, displacement effective value, and noise decibel effective value; A second calculation unit for calculating the fault value of the coal mining machine based on the effective value of the coal mining machine. The method for calculating the fault value based on the effective value of the coal mining machine includes: K = a(x + v) + bW + cL, in, K is the fault value, a is the vibration response coefficient, b is the temperature response coefficient, c is the noise decibel response coefficient, 0<1<a and 0 < b < 1 and 0 < c < 1 and a + b + c = 1 , x is the effective value of displacement, v is the effective value of speed, W is the real-time temperature of the coal mining machine, L is the effective value of noise in decibels, where a, b, c The calculation method is: ， ， ， in, βa, βb, βc are the average diagnostic accuracy of vibration, temperature, and noise, respectively; a fault monitoring unit, the fault monitoring unit being configured to compare the fault value with a preset alarm threshold, and if the fault value is greater than the preset alarm threshold, storing the real-time working data corresponding to the fault value as fault working data; a fault diagnosis unit, the fault diagnosis unit being used to input the fault operation data into a preset fault diagnosis model to determine whether the coal mining machine has a fault; An output unit is used to output the fault type and fault location if the coal mining machine has a fault, and to stop running the preset fault diagnosis model if the coal mining machine does not have a fault.

8. A computer-readable storage medium having computer-readable instructions stored thereon, characterized in that: When the computer-readable instructions are executed by a processor, the steps of the coal mining machine fault diagnosis method according to any one of claims 1 to 6 are implemented.

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