Coal mine equipment and fault diagnosis method and device thereof

Abstract

The invention provides coal mine equipment and a fault diagnosis method and device thereof. The fault diagnosis method for the coal mine equipment comprises the steps of obtaining vibration data of at least one measuring point of at least one rotating part of the coal mine equipment; determining a fault diagnosis item corresponding to the at least one rotating component; processing the vibration data to obtain feature data; and according to the obtained feature data and the fault feature of the fault diagnosis item, determining whether the at least one rotating part has a fault corresponding to the fault diagnosis item or not. According to the fault diagnosis method of the coal mine equipment, fault diagnosis can be carried out on the coal mine equipment.

Description

Technical Field

[0001] The present disclosure generally relates to the field of coal mining, and more particularly, to a coal mining equipment and a fault diagnosis method and apparatus thereof. Background Art

[0002] The coal mining industry is a high-risk industry with complex underground operating conditions and large regional differences. Current technical and management methods are insufficient, and coal mine safety production faces challenges. Monitoring and maintenance of key equipment in coal mines is of great significance to coal mine safety production.

[0003] Currently, there are two ways to detect the status of coal mine equipment: manual detection or offline detection. The current detection methods are difficult to make accurate judgments on the health status of key equipment in coal mine equipment.

[0004] In addition, operation and maintenance personnel cannot understand the operating status and health status of coal mine equipment in a timely manner, making it difficult to detect failures of coal mine equipment in the early stages and easily missing the best time to deal with the failures. Summary of the Invention

[0005] One of the objectives of exemplary embodiments of the present disclosure is to provide a fault diagnosis method capable of performing fault diagnosis on coal mining equipment.

[0006] According to a first aspect of the present disclosure, a fault diagnosis method for coal mining equipment includes: obtaining vibration data of at least one measuring point of at least one rotating component of the coal mining equipment; determining a fault diagnosis item corresponding to the at least one rotating component; processing the vibration data to obtain characteristic data; and determining whether the at least one rotating component has a fault corresponding to the fault diagnosis item based on the obtained characteristic data and the fault characteristics of the fault diagnosis item.

[0007] According to a second aspect of the present disclosure, a computer-readable storage medium stores a program or instruction, and when the program or instruction is executed by a processor, the above-mentioned fault diagnosis method for coal mining equipment is implemented.

[0008] According to a third aspect of the present disclosure, a fault diagnosis device for coal mining equipment includes: a data acquisition unit, which obtains vibration data of at least one measuring point of at least one rotating component of the coal mining equipment; a diagnostic item determination unit, which determines a fault diagnosis item corresponding to the at least one rotating component; a processing unit, which processes the vibration data to obtain characteristic data; and a fault determination unit, which determines whether the at least one rotating component has a fault corresponding to the fault diagnosis item based on the obtained characteristic data and the fault characteristics of the fault diagnosis item.

[0009] According to a fourth aspect of the present disclosure, a coal mine equipment includes the above-mentioned fault diagnosis device.

[0010] The fault diagnosis method and apparatus according to the embodiments of the present disclosure can achieve early prediction of faults, accurate fault determination, and timely equipment maintenance, thereby improving production efficiency and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other objects and features of the exemplary embodiments of the present disclosure will become more apparent from the following description taken in conjunction with the accompanying drawings which exemplarily illustrate the embodiments, in which:

[0012] Figure 1 is a flow chart illustrating a fault diagnosis method for coal mining equipment according to an embodiment of the present disclosure;

[0013] Figure 2 is a graph showing frequency domain characteristics of gear wear according to an embodiment of the present disclosure;

[0014] Figure 3 is a graph showing frequency domain characteristics of gear cracks according to an embodiment of the present disclosure;

[0015] Figure 4 is a graph showing frequency domain characteristics of gear tooth breakage according to an embodiment of the present disclosure;

[0016] Figure 5 is a graph showing frequency domain characteristics of gear eccentricity according to an embodiment of the present disclosure;

[0017] Figure 6 is a graph illustrating frequency domain characteristics of gear misalignment according to an embodiment of the present disclosure;

[0018] Figure 7 is a graph showing frequency domain characteristics of belt wear, belt looseness, or belt mismatch according to an embodiment of the present disclosure;

[0019] Figure 8 is a graph showing frequency domain characteristics of belt resonance according to an embodiment of the present disclosure;

[0020] Figure 9 is a graph showing frequency domain characteristics of belt eccentricity according to an embodiment of the present disclosure;

[0021] Figure 10 is a graph illustrating frequency domain characteristics of belt or pulley misalignment according to an embodiment of the present disclosure;

[0022] Figure 11 is a graph showing frequency domain characteristics of rotor dynamic imbalance according to an embodiment of the present disclosure;

[0023] Figure 12 is a graph illustrating frequency domain characteristics of rotor misalignment according to an embodiment of the present disclosure;

[0024] Figure 13 is a flow chart illustrating a fault diagnosis method for coal mining equipment according to another embodiment of the present disclosure;

[0025] Figure 14 is a block diagram illustrating a fault diagnosis apparatus for coal mining equipment according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0026] The following detailed description is provided to assist in gaining a comprehensive understanding of the methods, apparatuses, and / or systems described herein. However, the order of operations described herein is merely exemplary and is not limited to the order set forth herein. Equivalent substitutions or variations are possible, except for operations that must occur or be performed in a specific order. Furthermore, for the sake of clarity and conciseness, descriptions of matters known in the art may be omitted or simplified.

[0027] Unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which the present disclosure pertains after understanding the present disclosure. Unless expressly defined otherwise herein, terms (such as those defined in general dictionaries) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal manner.

[0028] Unless otherwise specified, the same reference numerals generally refer to the same elements (e.g., components, steps, and methods). Reference numerals described in previous embodiments may be omitted if they appear again in later embodiments. In addition, the technical features described in different or the same embodiments may be combined in any manner, as long as the combined embodiment or technical solution is complete and can solve the technical problems of the present application or achieve the technical effects described or not described in the present application but can be determined based on the above complete technical solution.

[0029] The fault diagnosis method for coal mine equipment disclosed in the present invention can be based on the vibration data of at least one measuring point of the coal mine equipment (for example, a belt conveyor, a vibrating screen), and utilizes various data processing methods such as data mining, machine learning, and signal analysis to diagnose faults in various key components (rotating components) of the coal mine equipment.

[0030] In view of the commonality and frequency of rotating component failures, this disclosure classifies the failures into four types, namely, gear failure, bearing failure, power frequency failure and belt failure (the characteristics of these failures will be analyzed in detail later). Figures 1 to 13 Specific embodiments of the present disclosure are described.

[0031] Figure 1 is a flowchart illustrating a fault diagnosis method for coal mining equipment according to an embodiment of the present disclosure.

[0032] Reference Figure 1 The fault diagnosis method for coal mine equipment according to an embodiment of the present disclosure may include step S110, step S120, step S130 and step S140.

[0033] In step S110, vibration data of at least one measuring point of at least one rotating component of the coal mining equipment is obtained. Obtaining the vibration data of at least one measuring point of at least one rotating component of the coal mining equipment can be a detection step performed by a sensor or a data reading step performed by a software module.

[0034] Sensors can detect vibration data from various components of coal mining equipment. As the number of connected devices increases, the sensor data can be fed into an IoT system, which can be expanded to accommodate the influx of diverse data. The disclosed fault diagnosis method can be executed by an IoT system, which writes data smoothly, continuously, with high concurrency and high throughput. The IoT system typically writes more data than it reads, writing the most recently generated sensor data in real time, with virtually no data updates.

[0035] At least one rotating component of the coal mining equipment may be a moving component of the coal mining equipment, at least one rotating component of the coal mining equipment may include a key equipment of the coal mining equipment, at least one rotating component of the coal mining equipment may include any one or any combination of two or more of a crusher, a belt conveyor, a scraper, a vibrating screen and a briquetting machine, but this is only an example, and the at least one rotating component of the coal mining equipment may also include other types of equipment.

[0036] This paper selects five important equipment in the coal mine workflow as fault diagnosis objects. In order to quickly engineer the diagnostic model, a unified diagnostic process can be adopted. Due to the different functions of various components or equipment of coal mine equipment, the components of each type of equipment are also different, and the diagnostic models of various equipment can also be different.

[0037] In step S120 , a fault diagnosis item corresponding to at least one rotating component is determined.

[0038] The fault diagnosis items here may include at least one of gear fault, power frequency fault, belt fault, and bearing fault. For example, the fault diagnosis items may include at least one of gear fault, power frequency fault, belt fault, and bearing fault. The fault diagnosis items for each component can be predetermined, or all fault diagnosis items can be diagnosed by default. If a certain type of fault does not exist in the corresponding component, the corresponding fault cannot be detected. If the corresponding component has multiple types of faults, any one or more of them can be selected in advance for fault detection, or all existing types of faults can be detected by default. The specific fault diagnosis items for a component will be described in detail below.

[0039] In one example, the fault diagnosis items of each component in the crusher may include at least power frequency fault and bearing fault, the fault diagnosis items of each component in the belt conveyor may include at least bearing fault, the fault diagnosis items of each component in the scraper may include at least power frequency fault and bearing fault, and the fault diagnosis items of each component in the briquetting machine may include at least power frequency fault and bearing fault.

[0040] Fault detection can be performed on two or more measuring points of at least one rotating component of coal mining equipment, thereby ensuring the accuracy of fault diagnosis.

[0041] The crusher's at least one measurement point can include at least one of the following: the motor free end, the motor load end, the gearbox shaft output side, the gearbox shaft input side, the gearbox output side, the crushing shaft input side, and the crushing shaft output side. Depending on the crusher's structure, specific measurement points and fault diagnosis items can be shown in Table 1 below.

[0042] Table 1

[0043]

[0044]

[0045] A crusher may include multiple drive units, each of which may include a motor, gearbox, and crushing shaft. Vibration data can be measured at multiple measurement points across each component of the crusher's drive unit. Each measurement point collects data through its own channel, preventing interference between vibration data from different measurement points. Fault detection at multiple measurement points on the same crusher component can accurately determine if it is faulty, providing support for predictive maintenance.

[0046] The at least one measurement point on a belt conveyor can include at least one of the following: the pedestrian side of the roller, the non-pedestrian side of the roller, the free end of the motor, the load end of the motor, the fluid coupling input side, the fluid coupling output side, the gearbox input shaft, the gearbox output shaft, the gearbox shaft input side, the gearbox shaft output side, the drive side of the drive roller, the non-drive side of the drive roller, the pedestrian side of the tensioning roller, and the non-pedestrian side of the tensioning roller. Depending on the belt conveyor's structure, specific measurement points and fault diagnosis items can be shown in Table 2 below.

[0047] Table 2

[0048]

[0049]

[0050] A belt conveyor may include a conveyor belt, multiple drive units, and rollers. Each drive unit may include a motor, gearbox, and other components. Vibration data can be detected at measuring points on various belt conveyor components. Each measuring point collects data through its own measuring point channel to prevent interference between vibration data from different measuring points. Fault detection at multiple measuring points on the same belt conveyor component improves fault diagnosis accuracy and provides a basis for predictive maintenance.

[0051] The at least one measurement point on a scraper conveyor may include at least one of the following: the motor free end, the motor load end, the gearbox input shaft, the gearbox output shaft, the gearbox shaft input side, the gearbox shaft output side, the head sprocket input side, the head sprocket output side, the tail sprocket input side, and the tail sprocket output side. Depending on the scraper conveyor's structure, specific measurement points and fault diagnosis items may be shown in Table 3 below.

[0052] Table 3

[0053]

[0054] A scraper can include a motor, gearbox, nose, and tail. Vibration data can be detected at measuring points on each component of the scraper. Each measuring point collects data through its own measuring point channel to prevent interference between vibration data from different measuring points. Fault detection at multiple measuring points on the same scraper component (e.g., motor, gearbox) improves fault diagnosis accuracy and facilitates proactive maintenance based on the diagnostic results, thereby extending the service life of related components and improving equipment safety.

[0055] The at least one measuring point on the vibrating screen can include at least one of the following: a belt-related measuring point, the motor free end, the motor load end, and the vibrating screen main shaft. Depending on the vibrating screen's structure, specific measuring points and fault diagnosis items can be shown in Table 4 below.

[0056] Table 4

[0057]

[0058] A vibrating screen may include a belt, a motor, and a spindle. The measuring points on the vibrating screen's belt can correspond to those on the motor, and data can be collected through measuring point channels 2H and 3H. Vibration data can be detected at measuring points on various components of the vibrating screen, with each measuring point collecting data through its own measuring point channel to prevent interference between vibration data from different measuring points. Performing fault detection at multiple measuring points on the same component of the vibrating screen (e.g., the motor or spindle) can improve the accuracy of fault diagnosis and facilitate early maintenance based on the fault diagnosis results, thereby increasing the service life of related components and improving equipment safety.

[0059] The at least one measuring point of the briquetting machine includes at least one of the following: the free end of the motor, the load end of the motor, the input side of the reducer shaft, the output side of the reducer shaft, the input side of the movable roller shaft, the output side of the movable roller shaft, the input side of the fixed roller shaft, the output side of the fixed roller shaft, the load end of the forced feeder motor, and the input side of the forced feeder reducer shaft. Depending on the structure of the briquetting machine, the specific measuring points and fault diagnosis items can be shown in Table 5 below.

[0060] Table 5

[0061]

[0062] A briquetting machine may include a motor, a reducer, and other components. Vibration data can be detected at measuring points on various components of the briquetting machine, with each measuring point collecting data through its own measuring point channel. Performing fault detection at multiple measuring points on the same component of the briquetting machine (e.g., a motor, a reducer) can improve the accuracy of fault diagnosis and facilitate early maintenance based on the fault diagnosis results, thereby extending the service life of related components and improving equipment safety.

[0063] As an example, obtaining vibration data of at least one measuring point of at least one rotating component of coal mining equipment may include obtaining vibration data of multiple measuring points of motors of a crusher, a belt conveyor, a scraper, a vibrating screen, and a briquetting machine, and the step of determining a fault diagnosis item corresponding to the at least one rotating component may include: determining a fault diagnosis item corresponding to the motor (e.g., a power frequency fault and a bearing fault). In this way, the fault diagnosis accuracy of key components of key equipment can be improved. The vibration data of multiple measuring points of the motor can be obtained through different detection channels, and the two do not interfere with each other, but this is only an example, and the vibration data of multiple measuring points can be obtained through a single channel.

[0064] Before fault diagnosis, it is possible to determine whether the detection data of the sensor is valid, that is, the judgment can be made by detecting whether the bias voltage of the sensor exceeds a preset threshold.

[0065] For example, the step of determining a fault diagnosis item corresponding to at least one rotating component may include: extracting a bias voltage of a sensor at at least one measuring point from collected data of at least one measuring point, and determining a fault diagnosis item corresponding to at least one rotating component in response to the bias voltage data being greater than a preset threshold (e.g., 3.5 V) (hereinafter referred to as Figure 13 for description).

[0066] In step S130 , the vibration data is processed to obtain feature data.

[0067] As an example, processing vibration data may include filtering and / or feature extraction. Noise in the vibration data may be removed using a filter, and the noise-free data may be processed using signal processing methods such as spectrum analysis (e.g., Fourier transform) to extract feature data or feature values. The feature data may be frequency domain feature data or time domain feature data.

[0068] Before performing feature extraction or filtering, it can be determined whether the vibration data is valid data, that is, whether the vibration data is data collected when the coal mine equipment is in operation.

[0069] The step of processing the vibration data to obtain characteristic data includes: calculating the rotational speed and the effective value of low-frequency acceleration of at least one rotating component based on the vibration data, and extracting features from the vibration data when the rotational speed and the effective value of low-frequency acceleration meet preset conditions to obtain characteristic data.

[0070] As an example, feature extraction can be performed on vibration data when the rotational speed and the effective low-frequency acceleration of at least one rotating component are both greater than preset thresholds. The preset thresholds can be empirical values ​​and can be determined based on the minimum rotational speed and minimum low-frequency acceleration of at least one rotating component of the coal mining equipment during normal operation. For example, the minimum rotational speed and the minimum low-frequency acceleration can be set as corresponding thresholds.

[0071] The validity judgment of the vibration data may also be performed before determining the fault diagnosis item corresponding to the at least one rotating component and after obtaining the vibration data of at least one measuring point of the at least one rotating component of the coal mining equipment.

[0072] In step S140 , based on the acquired characteristic data and the fault characteristics of the fault diagnosis item, it is determined whether at least one rotating component has a fault corresponding to the fault diagnosis item.

[0073] The step of determining whether at least one rotating component has a fault corresponding to the fault diagnosis item based on the acquired characteristic data and the fault characteristics of the fault diagnosis item includes: determining whether at least one rotating component has a fault corresponding to the fault diagnosis item in response to the acquired characteristic data presenting at least one fault characteristic of a gear fault, a power frequency fault, a belt fault, and a bearing fault. Figures 2 to 12 The spectrum characteristics under various faults disclosed in the present invention are described in detail.

[0074] Figure 2 is a graph showing frequency domain characteristics of gear wear according to an embodiment of the present disclosure; Figure 3 is a graph showing frequency domain characteristics of gear cracks according to an embodiment of the present disclosure; Figure 4is a graph showing frequency domain characteristics of gear tooth breakage according to an embodiment of the present disclosure; Figure 5 is a graph showing frequency domain characteristics of gear eccentricity according to an embodiment of the present disclosure; Figure 6 is a graph illustrating frequency domain characteristics of gear misalignment according to an embodiment of the present disclosure.

[0075] like Figure 2 As shown, in the case of gear wear, the characteristic data in the frequency domain manifests as the presence of the gear's natural frequency Fn, gear speed frequency sidebands on both sides of the gear's natural frequency, and the amplitude of the gear meshing frequency GMF exceeding a normal value more than a predetermined number of times. In other words, in response to the acquired characteristic data manifesting in the frequency domain as the presence of the gear's natural frequency, gear speed frequency sidebands on both sides of the gear's natural frequency, and the amplitude of the gear meshing frequency exceeding a normal value more than a predetermined number of times, it can be determined that at least one rotating component has a gear fault and that the type of gear fault is a gear wear fault. The gear meshing frequency GMF is equal to the number of teeth on the low-speed gear multiplied by the low-speed gear rotational frequency, and is equal to the number of teeth on the high-speed gear multiplied by the high-speed gear rotational frequency.

[0076] In addition, the characteristic data of gear wear faults in the frequency domain often show that the amplitude (or magnitude) under 2GMF and 3GMF is often very high (for example, 2 or 3 times that of normal conditions).

[0077] like Figure 3 As shown, in the case of a gear crack, the acquired characteristic data in the frequency domain appears as a primary frequency domain feature with n*fr (n is a positive integer), a multiple of the gear rotation frequency. Additionally, although not shown, the characteristic data (filtered vibration data) in the time domain appears as an impact pulse with a period corresponding to the gear rotation frequency. Therefore, based on the acquired characteristic data appearing as an impact pulse with a period corresponding to the gear rotation frequency in the time domain and as a primary frequency domain feature with multiples of the gear rotation frequency in the frequency domain, it can be determined that a gear fault exists in at least one rotating component and that the type of gear fault is a gear crack.

[0078] like Figure 4As shown, in the case of broken gear teeth, the characteristic data obtained are expressed in the frequency domain as the gear meshing frequency and the gear meshing harmonic frequency as the carrier frequency, the rotation frequency of the gear shaft and the multiple of the rotation frequency as the modulation frequency, the various orders of natural frequencies of the gear as the carrier frequency, and the modulation sideband is wider and higher than the sideband bandwidth in normal cases. Therefore, in response to the obtained characteristic data being expressed in the frequency domain as the gear meshing frequency and the gear meshing harmonic frequency as the carrier frequency, the rotation frequency of the gear shaft and the multiple of the rotation frequency as the modulation frequency, the various orders of natural frequencies of the gear as the carrier frequency, and the modulation sideband is wider and higher than the sideband bandwidth in normal cases, it can be determined that there is a gear fault in at least one rotating component and the type of gear fault is broken gear teeth.

[0079] like Figure 5 As shown, in the case of gear eccentricity, the obtained characteristic data in the frequency domain manifests as a pulse amplitude greater than a predetermined value at the gear rotation frequency, the presence of load fluctuations with a period of one gear rotation, and a modulation frequency of the gear rotation frequency. Furthermore, although not shown, the obtained characteristic data (filtered vibration data) manifests as amplitude-modulated vibrations in the time domain. Therefore, in response to the obtained characteristic data manifesting as amplitude-modulated vibrations in the time domain and manifesting as a pulse amplitude greater than a predetermined value at the gear rotation frequency in the frequency domain, the presence of load fluctuations with a period of one gear rotation, and a modulation frequency of the gear rotation frequency, it can be determined that a gear fault exists in at least one rotating component and that the type of gear fault is gear eccentricity.

[0080] like Figure 6 As shown, in the case of gear misalignment, the obtained characteristic data is expressed in the frequency domain as the gear meshing frequency and the gear meshing harmonic frequency as the carrier frequency, the rotation frequency of the gear shaft and the multiple of the rotation frequency as the modulation frequency, the amplitude at the double frequency of the modulation frequency is the maximum, and the amplitudes of the sideband frequencies on the left and right sides of the gear meshing frequency and the gear meshing harmonic frequency are not equal. Therefore, in response to the obtained characteristic data being expressed in the frequency domain as the gear meshing frequency and the gear meshing harmonic frequency as the carrier frequency, the rotation frequency of the gear shaft and the multiple of the rotation frequency as the modulation frequency, the amplitude at the double frequency of the modulation frequency is the maximum, and the amplitudes of the sideband frequencies on the left and right sides of the gear meshing frequency and the gear meshing harmonic frequency are not equal, it can be determined that there is a gear fault in at least one rotating component and the type of the gear fault is gear misalignment.

[0081] Figure 7 is a graph showing frequency domain characteristics of belt wear, belt looseness, or belt mismatch according to an embodiment of the present disclosure; Figure 8 is a graph showing frequency domain characteristics of belt resonance according to an embodiment of the present disclosure; Figure 9 is a graph showing frequency domain characteristics of belt eccentricity according to an embodiment of the present disclosure;

[0082] Figure 10 is a graph illustrating frequency domain characteristics of belt or pulley misalignment according to an embodiment of the present disclosure.

[0083] like Figure 7 As shown, in the case of belt wear, belt looseness, or belt mismatch, the characteristic data in the frequency domain manifests as follows: unstable belt vibration amplitude, the vibration amplitude at the belt vibration frequency is greater than the vibration amplitude at the belt vibration harmonic frequency, and the belt vibration frequency is lower than the belt driven speed frequency. In addition, the characteristic data in the frequency domain also manifests as the double frequency of the belt speed frequency dominating the belt speed frequency. These situations are mostly belt wear, belt looseness, or belt mismatch faults. Therefore, in response to the obtained characteristic data manifesting in the frequency domain as the double frequency of the belt speed frequency dominating the belt speed frequency, the belt vibration amplitude is unstable, the vibration amplitude at the belt vibration frequency is greater than the vibration amplitude at the belt vibration harmonic frequency, and the belt vibration frequency is lower than the belt driven speed frequency, it can be determined that at least one rotating component has a belt fault and the type of belt fault is belt wear, belt looseness, or belt mismatch.

[0084] like Figure 8 As shown, in the case of belt resonance, the characteristic data obtained in the frequency domain indicates that the vibration amplitude of the belt when the natural frequency of the belt coincides with the driven speed frequency of the belt is greater than a predetermined threshold (e.g., a third predetermined threshold). Furthermore, the natural frequency changes when the belt tension changes. In other words, based on the characteristic data obtained in the frequency domain indicating that the vibration amplitude of the belt when the natural frequency of the belt coincides with the driven speed frequency of the belt is greater than the third predetermined threshold, and the natural frequency changes when the belt tension changes, it can be determined that a belt fault exists in at least one rotating component and that the type of belt fault is belt resonance.

[0085] like Figure 9 As shown, in the case of belt eccentricity, the obtained characteristic data in the frequency domain indicates that the vibration amplitude of the pulley at the belt speed frequency is greater than a predetermined threshold value (e.g., a fourth predetermined threshold value). Therefore, in response to the obtained characteristic data in the frequency domain indicating that the vibration amplitude of the pulley at the belt speed frequency is greater than the fourth predetermined threshold value, it can be determined that a belt fault exists in at least one rotating component and the type of the belt fault is belt eccentricity.

[0086] like Figure 10As shown, in the case of belt or pulley misalignment, the characteristic data in the frequency domain is manifested as the pulley's axial vibration amplitude at the belt speed frequency being greater than a predetermined threshold value (e.g., a fifth predetermined threshold value). Thus, in response to the obtained characteristic data in the frequency domain being manifested as the pulley's axial vibration amplitude at the belt speed frequency being greater than the fifth predetermined threshold value, it can be determined that a belt fault exists in at least one rotating component and that the belt fault is belt or pulley misalignment. The aforementioned predetermined pulley-related threshold values ​​can all be empirical values.

[0087] Figure 11 is a graph showing frequency domain characteristics of rotor dynamic imbalance according to an embodiment of the present disclosure, and Figure 12 is a graph illustrating frequency domain characteristics of rotor misalignment according to an embodiment of the present disclosure.

[0088] Power frequency faults may include rotor dynamic unbalance, rotor misalignment and other power frequency faults.

[0089] like Figure 11 As shown, rotor dynamic imbalance manifests itself as characteristic data in the frequency domain, with the fundamental frequency amplitude exceeding a predetermined threshold (e.g., the first predetermined threshold), and the amplitude at one times the power frequency exceeding the amplitude at two times the power frequency. Furthermore, rotor dynamic imbalance manifests itself as a larger fundamental frequency amplitude, which is proportional to the square of the rotational speed, with the one-time frequency dominating.

[0090] Furthermore, harmonic energy is primarily concentrated at the rotor's operating frequency (1X), meaning the fundamental frequency vibration component dominates. During acceleration and deceleration, when the speed is below the critical speed, the amplitude increases with increasing speed. After the speed exceeds the critical speed, the amplitude decreases with increasing speed, tending toward a smaller, stable value. When the speed is at or near the critical speed, the rotor resonates, with the amplitude reaching its maximum value. When the operating speed remains constant, the vibration phase stabilizes, the rotor's axis trajectory assumes an elliptical shape, and the rotor's wobbling motion is characterized by synchronous positive precession.

[0091] Therefore, in response to the fact that the obtained characteristic data in the frequency domain shows that the amplitude of the fundamental frequency is greater than the first predetermined threshold, and the amplitude at one times the power frequency is greater than the amplitude at two times the power frequency, it is determined that there is a power frequency fault in at least one rotating component and the type of the power frequency fault is rotor dynamic imbalance.

[0092] like Figure 12 As shown, rotor misalignment manifests itself as characteristic data in the frequency domain, with the amplitude at twice the power frequency exceeding a predetermined threshold and being greater than or equal to the amplitude at single frequency. Furthermore, rotor misalignment manifests itself as shear force and deflection moment generated on the coupling side, and double frequency and high double frequency amplitudes measured radially at the bearings on both sides of the coupling, with the amplitude at double the power frequency being higher than the amplitude at single frequency.

[0093] Other power frequency faults other than the above faults may be manifested as the amplitudes of other multiple frequencies except the first multiple and the second multiple being greater than a predetermined threshold.

[0094] Furthermore, in the case of rotor misalignment, the more severe the shaft misalignment, the greater the proportion of the double frequency. In many cases, the double frequency energy even exceeds the single frequency energy. The time domain waveform of the vibration signal appears as a distorted sine wave. When the coupling is misaligned, the axial vibration is greater, the vibration frequency is at single frequency, the vibration amplitude and phase are stable, and the axial vibration phase difference on both sides of the coupling is 180 degrees.

[0095] Therefore, in response to the obtained characteristic data showing in the frequency domain that the amplitude at twice the power frequency is greater than the second predetermined threshold and greater than or equal to the amplitude at one times the power frequency, it can be determined that there is a power frequency fault in at least one rotating component and the type of the power frequency fault is rotor misalignment.

[0096] In addition to the two faults mentioned above, there may be other power frequency faults such as rotor bending. The specific characteristics are shown in Table 6.

[0097] Table 6

[0098]

[0099] The power frequency fault can be judged as rotor bending by at least two of the characteristics in Table 6.

[0100] Approximately 30% of rotating component failures are caused by rolling element bearing failures. Bearing failures are categorized by location as inner race, outer race, rolling element, and cage failures. Key methods for bearing fault diagnosis include time series waveform RMS analysis, kurtosis coefficient analysis, and envelope spectrum analysis. Time series waveform RMS analysis is suitable for conditions of long-term wear or surface roughness; the higher the RMS value, the greater the degree of wear. The kurtosis coefficient method is suitable for early fault diagnosis and is sensitive to large shocks. Envelope spectrum analysis primarily defines faults based on the amplitude of the characteristic fault frequencies of the inner race, outer race, rolling element, and cage.

[0101] Specifically, in response to the acquired characteristic data in the frequency domain showing that the amplitude at the bearing frequency exceeds a predetermined threshold (eg, a sixth threshold), it can be determined that at least one rotating component has a bearing fault. The predetermined threshold here can also be an empirical value.

[0102] Figure 13 is a flowchart illustrating a fault diagnosis method for coal mining equipment according to another embodiment of the present disclosure.

[0103] According to another embodiment of the present disclosure, a fault diagnosis method for coal mining equipment may include at least one of the steps of scheduling task setting, receiving and parsing scheduling instructions, and bias voltage determination.

[0104] like Figure 13 As shown, the fault diagnosis method for coal mine equipment according to another embodiment of the present disclosure may include step S1310, step S1320, step S1330, step S1340, step S1350, step S1360, step S1370, step S1380, step S1381, step S1390 and step S1391.

[0105] In step S1310, the scheduling task is set, for example, the type of equipment that needs to be fault detected (for example, a crusher) and measurement point information are set, the fault diagnosis program to be started (for example, a crusher fault diagnosis program), the sending cycle of instructions related to the scheduling task are set, etc.

[0106] In step S1320, the scheduling instructions are received and parsed. For example, the basic parameter information of the relevant fault diagnosis method (or fault diagnosis algorithm) and the algorithm configuration information (for example, the number of threads for executing the algorithm, which can be set to different values ​​for different servers to speed up the execution efficiency of the algorithm when the algorithm is started) can be parsed from the set information.

[0107] In step S1330, the corresponding measurement point information of the device is obtained.

[0108] In step S1340 , the collected data of the corresponding measuring point is obtained.

[0109] In step S1350 , the bias voltage of the sensor (eg, the probe of the sensor) is acquired.

[0110] In step S1360, parameter information of the device is obtained, which may include the model of the bearing, parameters of the gear (for example, the number of teeth), etc.

[0111] In step S1370, waveform file analysis is performed on the collected data. The specific analysis process may include the time domain and frequency domain analysis as described above.

[0112] In step S1380 , it is determined whether the bias voltage exceeds a preset threshold (eg, 3.5 V).

[0113] In step S1390 , fault diagnosis of each component is performed (a fault diagnosis algorithm is executed) in response to the bias voltage exceeding a preset threshold.

[0114] In step S1381, in response to the bias voltage not exceeding the preset threshold, a low battery voltage warning of the sensor is issued, which indicates that the collected data is invalid.

[0115] In step S1391, the diagnosis result of the fault is pushed.

[0116] Figure 14 is a block diagram illustrating a fault diagnosis apparatus according to an embodiment of the present disclosure.

[0117] The fault diagnosis device 1300 for coal mining equipment according to an embodiment of the present disclosure may include: a data acquisition unit 1310 , a diagnosis item determination unit 1320 , a processing unit 1330 , and a fault determination unit 1340 .

[0118] The data acquisition unit 1310 may obtain vibration data of at least one measuring point of at least one rotating component of coal mining equipment (eg, a crusher, a belt conveyor, a scraper, a vibrating screen, and a briquetting machine).

[0119] The diagnosis item determination unit 1320 may determine a fault diagnosis item (eg, a gear fault, a power frequency fault, a belt fault, and a bearing fault) corresponding to at least one rotating component.

[0120] The processing unit 1330 may process the vibration data to obtain feature data. The processing unit 1130 may filter and / or extract features from the vibration data to obtain feature data (eg, time domain feature data and / or frequency domain feature data).

[0121] The fault determining unit 1340 may determine whether at least one rotating component has a fault corresponding to the fault diagnosis item based on the obtained characteristic data and the fault characteristics of the fault diagnosis item.

[0122] As an example, the data acquisition unit 1310 may be a vibration sensor, and the data acquisition unit 1310 may obtain vibration data of multiple measuring points of a motor of equipment such as a crusher, a belt conveyor, a scraper, a vibrating screen, and a briquetting machine.

[0123] As an example, the diagnosis item determination unit 1320 may determine fault diagnosis items corresponding to the motor (eg, at least two diagnosis items such as a power frequency fault and a bearing fault).

[0124] As an example, fault determination unit 1340 can determine whether the motor has a fault corresponding to the fault diagnosis item based on the acquired characteristic data and the determined fault characteristics of at least two fault diagnosis items. This allows for multiple aspects of the motor fault in the coal mining equipment to be determined, facilitating early diagnosis and proactive maintenance.

[0125] It should be understood that the various units or modules according to the exemplary embodiments of the present disclosure may be implemented by hardware components and / or software components. Those skilled in the art may implement the various units, for example, using a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a software algorithm, etc., based on the processing performed by the defined various units.

[0126] Each operation of the above steps can be written as a software program or instruction. Therefore, the control method according to the exemplary embodiment of the present disclosure can be implemented via software. The computer-readable storage medium of the exemplary embodiment of the present disclosure can store a computer program, and when the computer program is executed by the processor, the fault diagnosis method of the above exemplary embodiment is implemented.

[0127] According to various embodiments of the present disclosure, a device (such as a module or its functions) or a method can be implemented by a program or instruction stored in a computer-readable storage medium. When the instruction is executed by a processor, the processor can perform the function corresponding to the instruction or perform the method corresponding to the instruction. At least a portion of a module can be implemented (for example, executed) by a processor. At least a portion of a programming module may include modules, programs, routines, instruction sets, and processes for performing at least one function. In one example, an instruction or software includes machine code (such as machine code generated by a compiler) directly executed by one or more processors or computers. In another example, an instruction or software includes a higher-level code executed by one or more processors or computers using an interpreter. Any programming language can be used to write instructions or software based on the block diagrams and flow charts shown in the accompanying drawings and the corresponding description in the specification.

[0128] Computer-readable storage media include non-transitory computer-readable storage media, for example, magnetic media such as floppy disks and magnetic tapes, optical media (including compact disc (CD) ROMs and DVD ROMs), magneto-optical media such as flexible optical disks, hardware devices such as ROMs, RAMs, and flash memories designed to store and execute program commands. Program commands include language codes executable by a computer using an interpreter and machine language codes generated by a compiler. The above-mentioned hardware devices can be implemented by one or more software modules for performing the operations of various embodiments of the present disclosure.

[0129] The modules or programming modules of the present disclosure may include at least one of the aforementioned components with some components omitted or other components added. The operations of the modules, programming modules or other components may be performed sequentially, in parallel, in a loop or in a trial manner. In addition, some operations may be performed in a different order, may be omitted or expanded with other operations.

[0130] The computer-readable storage medium of the exemplary embodiment of the present disclosure may be part of a coal mine diagnostic device. The coal mine equipment of the exemplary embodiment of the present disclosure may include the above-mentioned fault diagnostic device.

[0131] A controller according to an exemplary embodiment of the present disclosure may include: a processor and a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program or instructions, and when the computer program or instructions are executed by the processor, the fault diagnosis method as described in the exemplary embodiment is implemented.

[0132] The fault diagnosis method and apparatus according to the exemplary embodiments of the present disclosure can perform predictive maintenance through fault diagnosis, which will effectively improve enterprise management efficiency and reduce the enterprise's comprehensive operation and maintenance management costs.

[0133] The fault diagnosis method and apparatus according to the exemplary embodiments of the present disclosure can avoid excessive maintenance, prevent reduction in equipment accuracy due to unnecessary disassembly, and extend the life of the equipment.

[0134] The fault diagnosis method and apparatus according to the exemplary embodiments of the present disclosure can achieve accurate fault diagnosis and early prediction of faults through real-time monitoring.

[0135] Although some exemplary embodiments of the present disclosure have been shown and described, it will be understood by those skilled in the art that modifications may be made to these embodiments without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents; for example, the technical features of different embodiments may be combined.

Claims

1. A fault diagnosis method for coal mine equipment, characterized in that: include: Obtaining vibration data of at least one measuring point of at least one rotating component of coal mining equipment; determining a fault diagnosis item corresponding to the at least one rotating component; Processing the vibration data to obtain characteristic data; According to the acquired characteristic data and the fault characteristic of the fault diagnosis item, it is determined whether the at least one rotating component has a fault corresponding to the fault diagnosis item.

2. The fault diagnosis method according to claim 1, characterized in that: The fault diagnosis items include at least one of a gear fault, a power frequency fault, a belt fault and a bearing fault.

3. The fault diagnosis method according to claim 2, characterized in that: The step of determining whether the at least one rotating component has a fault corresponding to the fault diagnosis item according to the acquired characteristic data and the fault characteristic of the fault diagnosis item comprises: In response to the acquired characteristic data presenting the fault characteristics of at least one of the gear fault, the power frequency fault, the belt fault and the bearing fault, it is determined that the at least one rotating component has a fault corresponding to the fault diagnosis item.

4. The fault diagnosis method according to claim 1, characterized in that: The step of determining a fault diagnosis item corresponding to the at least one rotating component includes: extracting a bias voltage of a sensor at the at least one measuring point from collected data of the at least one measuring point, and determining a fault diagnosis item corresponding to the at least one rotating component in response to the bias voltage data being greater than a preset threshold.

5. The fault diagnosis method for coal mine equipment according to claim 3, characterized in that: In response to the obtained characteristic data being manifested in the frequency domain as the existence of a natural frequency of the gear, the presence of gear speed frequency sidebands on both sides of the natural frequency of the gear, and the number of times that the amplitude of the gear meshing frequency is greater than a normal value exceeds a predetermined number, determining that a gear fault exists in the at least one rotating component and the type of the gear fault is a gear wear fault; In response to the acquired characteristic data being expressed as an impact pulse with a period corresponding to the gear rotation frequency in the time domain and being expressed as a frequency domain feature with a multiple of the gear rotation frequency as the main frequency domain feature in the frequency domain, it is determined that a gear fault exists in the at least one rotating component and the type of the gear fault is a gear crack; In response to the obtained characteristic data being expressed in the frequency domain as the carrier frequency using the gear meshing frequency and the gear meshing harmonic frequency, the modulation frequency using the rotation frequency of the shaft where the gear is located and the multiple of the rotation frequency, the carrier frequency using the natural frequencies of each order of the gear, and the modulation sideband being wider and higher than the sideband bandwidth under normal conditions, it is determined that the at least one rotating component has a gear fault and the type of the gear fault is a broken gear; In response to the acquired characteristic data showing that amplitude modulated vibration is generated in the time domain, that the pulse amplitude at the gear rotation frequency is greater than a predetermined value in the frequency domain, that there is a load fluctuation with the time of one rotation of the gear as a period and the gear rotation frequency as a modulation frequency, it is determined that a gear fault exists in the at least one rotating component and the type of the gear fault is gear eccentricity; In response to the obtained characteristic data being expressed in the frequency domain as the gear meshing frequency and the gear meshing harmonic frequency as the carrier frequency, the rotation frequency of the gear shaft and the multiples of the rotation frequency as the modulation frequency, the amplitude being the largest at the double frequency of the modulation frequency, and the amplitudes of the sideband frequencies on the left and right sides of the gear meshing frequency and the gear meshing harmonic frequency being unequal, it is determined that a gear fault exists in at least one rotating component and the type of the gear fault is gear misalignment.

6. The fault diagnosis method for coal mine equipment according to claim 3, characterized in that: In response to the obtained characteristic data being expressed in the frequency domain as the amplitude of the fundamental frequency being greater than a first predetermined threshold value, and the amplitude at one times the power frequency being greater than the amplitude at two times the power frequency, determining that the at least one rotating component has a power frequency fault and the type of the power frequency fault is rotor dynamic unbalance; In response to the acquired characteristic data showing in the frequency domain that the amplitude at twice the power frequency is greater than a second predetermined threshold and greater than or equal to the amplitude at one times the power frequency, it is determined that a power frequency fault exists in the at least one rotating component and the type of the power frequency fault is rotor misalignment.

7. The fault diagnosis method for coal mine equipment according to claim 3, characterized in that: In response to the obtained characteristic data showing in the frequency domain that the double frequency of the belt speed frequency is dominant over the belt speed frequency, the vibration amplitude of the belt is unstable, the vibration amplitude at the belt vibration frequency is greater than the vibration amplitude at the belt vibration harmonic frequency, and the belt vibration frequency is lower than the belt driven speed frequency, determining that the at least one rotating component has a belt fault and the type of the belt fault is belt wear, belt looseness, or belt mismatch; In response to the obtained characteristic data being expressed in the frequency domain as a vibration amplitude greater than a third predetermined threshold when the belt's natural frequency is consistent with the belt's driven speed frequency, and the natural frequency changes when the belt tension changes, determining that the at least one rotating component has a belt fault and the type of the belt fault is belt resonance; In response to the obtained characteristic data being expressed in the frequency domain as a vibration amplitude of the pulley at the belt speed frequency being greater than a fourth predetermined threshold, determining that a belt fault exists in the at least one rotating component and the type of the belt fault is belt eccentricity; In response to the obtained characteristic data being represented in the frequency domain as an axial vibration amplitude of the pulley at the belt speed frequency being greater than a fifth predetermined threshold, it is determined that a belt fault exists in the at least one rotating component and the type of the belt fault is belt or pulley misalignment.

8. The fault diagnosis method for coal mine equipment according to claim 3, characterized in that: In response to the acquired characteristic data being represented in the frequency domain as having an amplitude at a bearing frequency exceeding a sixth threshold, it is determined that a bearing fault exists in the at least one rotating component.

9. The fault diagnosis method for coal mine equipment according to any one of claims 1 to 8, characterized in that: The at least one rotating component includes at least one of a crusher, a belt conveyor, a scraper, a vibrating screen, and a briquetting machine.

10. The fault diagnosis method for coal mine equipment according to claim 9, characterized in that: The at least one measuring point of the crusher includes at least one of: a free end of the motor, a load end of the motor, an output side of a gearbox shaft, an input side of a gearbox shaft, an output side of a gearbox, an input side of a crushing shaft, and an output side of a crushing shaft; The at least one measuring point of the belt conveyor includes: at least one of the pedestrian side of the roller, the non-pedestrian side of the roller, the free end of the motor, the load end of the motor, the input side of the hydraulic coupler, the output side of the hydraulic coupler, the input shaft of the gearbox, the output shaft of the gearbox, the input side of the gearbox shaft, the output side of the gearbox shaft, the driving side of the driving part roller, the non-driving side of the driving part roller, the pedestrian side of the tensioning room roller, and the non-pedestrian side of the tensioning room roller; The at least one measuring point of the scraper comprises: at least one of the free end of the motor, the load end of the motor, the input shaft of the gearbox, the output shaft of the gearbox, the input side of the gearbox shaft, the output side of the gearbox shaft, the input side of the head sprocket, the output side of the head sprocket, the input side of the tail sprocket, and the output side of the tail sprocket; The at least one measuring point of the vibrating screen includes: at least one of a belt-related measuring point, a motor free end, a motor load end, and a main shaft of the vibrating screen; At least one measuring point of the briquetting machine includes: at least one of the free end of the motor, the load end of the motor, the input side of the reducer shaft, the output side of the reducer shaft, the input side of the movable roller shaft, the output side of the movable roller shaft, the input side of the fixed roller shaft, the output side of the fixed roller shaft, the load end of the forced feeder motor, and the input side of the forced feeder reducer shaft.

11. The fault diagnosis method for coal mine equipment according to claim 10, characterized in that: The fault diagnosis items of each component in the crusher at least include power frequency fault and bearing fault; The fault diagnosis items of each component in the belt conveyor at least include bearing faults; The fault diagnosis items of each component in the scraper conveyor at least include power frequency fault and bearing fault; The fault diagnosis items of each component in the briquetting machine at least include power frequency fault and bearing fault.

12. The fault diagnosis method for coal mine equipment according to claim 1, characterized in that: The step of processing the vibration data to obtain characteristic data includes: calculating the rotational speed and the effective value of low-frequency acceleration of the at least one rotating component according to the vibration data, and extracting features from the vibration data when the rotational speed and the effective value of low-frequency acceleration meet preset conditions to obtain the characteristic data.

13. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or an instruction, and when the program or the instruction is executed by a processor, the fault diagnosis method for coal mining equipment according to any one of claims 1 to 12 is implemented.

14. A fault diagnosis device for coal mine equipment, characterized in that: include: A data acquisition unit, which obtains vibration data of at least one measuring point of at least one rotating component of the coal mining equipment; a diagnostic item determination unit, which determines a fault diagnostic item corresponding to the at least one rotating component; A processing unit, processing the vibration data to obtain characteristic data; The fault determination unit determines whether the at least one rotating component has a fault corresponding to the fault diagnosis item according to the acquired characteristic data and the fault characteristic of the fault diagnosis item.

15. A coal mining equipment, characterized in that: Comprising the fault diagnosis device according to claim 13.

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