A method, apparatus, computer equipment, and medium for diagnosing rolling bearing failures.

Abstract

This invention provides a method, apparatus, computer equipment, and medium for diagnosing rolling bearing faults. The method includes: acquiring a first rotational speed of the physical shaft of the rolling bearing; determining a second rotational speed of a corresponding virtual shaft based on the first rotational speed of the physical shaft and the structural parameter information of the bearing; performing time-domain synchronous averaging analysis on a vibration signal on the virtual shaft based on the second rotational speed of the virtual shaft to obtain a processed vibration signal; and mapping the processed vibration signal onto the physical shaft for order spectrum analysis to obtain a fault diagnosis result for the rolling bearing. By using the method, apparatus, computer equipment, and medium provided by this invention to diagnose rolling bearing faults, the fault type of the rolling bearing is determined by performing time-domain synchronous averaging on the vibration signal on the virtual shaft and then mapping it onto the physical shaft for order spectrum analysis, thereby improving the accuracy of bearing fault diagnosis results.

Description

Technical Field

[0001] This invention relates to the field of bearing failure, and more specifically to a method, apparatus, computer equipment, and medium for diagnosing rolling bearing failure. Background Technology

[0002] In rotating machinery, rolling bearings are typically used to support rotating shafts and their components. The normal operation of rolling bearings supports the normal functioning of the shaft, as they convert the sliding friction between the rotating shaft and its housing into rolling friction, thus reducing frictional losses. However, because rolling bearings are subjected to alternating loads for extended periods during their operating cycle, they are prone to failure.

[0003] In existing technologies, the vibration response signal of a mechanical structure is determined by combining periodic synchronization signals, periodic asynchrony signals, and noise signals, and the fault condition of the mechanical structure is judged based on the vibration response signal. However, this method is only suitable for diagnosing gear faults where the meshing frequency and shaft rotation frequency are integer multiples of each other. But the bearing fault frequency and shaft rotation frequency are generally not integer multiples of each other. Therefore, using this method to diagnose bearing faults will lead to inaccurate fault diagnosis results. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problem that the existing technology is not suitable for diagnosing bearing faults, resulting in inaccurate fault diagnosis results, and thus provide a method, device, computer equipment and medium for diagnosing rolling bearing faults.

[0005] According to a first aspect, the present invention provides a method for diagnosing rolling bearing failures, the method comprising:

[0006] Obtain the first rotational speed of the physical shaft of the rolling bearing;

[0007] Based on the first rotational speed of the physical axis and the structural parameter information of the bearing, the second rotational speed of the corresponding virtual axis is determined;

[0008] Based on the second rotational speed of the virtual axis, a time-domain synchronous averaging analysis is performed on the vibration signal on the virtual axis to obtain the processed vibration signal;

[0009] The processed vibration signal is mapped onto the physical axis, and order spectrum analysis is performed to obtain the fault diagnosis results of the rolling bearing.

[0010] In one embodiment, determining the second rotational speed of the corresponding virtual axis based on the first rotational speed of the physical axis includes:

[0011] Obtain the pre-set correspondence between the first and second rotational speeds;

[0012] Based on the first rotational speed of the physical axis and the corresponding relationship, the second rotational speed of the corresponding virtual axis is determined.

[0013] In one embodiment, mapping the processed vibration signal onto the physical axis and performing order spectrum analysis to obtain the fault diagnosis result of the rolling bearing includes:

[0014] The order spectrum is obtained by performing a fast Fourier transform on the vibration signal mapped to the physical axis.

[0015] Based on the order spectrum, the rolling bearing faults are analyzed to obtain the fault diagnosis results of the rolling bearings.

[0016] In one embodiment, the analysis of rolling bearing faults based on the order spectrum to obtain rolling bearing fault diagnosis results includes:

[0017] Determine the fault characteristic order in the order spectrum, and determine the fault diagnosis result of the rolling bearing based on the fault characteristic order.

[0018] In one embodiment, when the fault diagnosis result of the rolling bearing is that there is a fault, the method further includes:

[0019] Order spectrum analysis is performed based on vibration signals mapped onto the physical axis to determine the fault type of the rolling bearing.

[0020] In one embodiment, the inner ring fault characteristic frequency f BPFI for:

[0021]

[0022] Outer ring fault characteristic frequency f BPFO for:

[0023]

[0024] cage failure characteristic frequency f FIF for:

[0025]

[0026] Rolling element failure characteristic frequency f BSF for:

[0027]

[0028] The formulas for calculating the order of failure characteristics for the inner ring, outer ring, rolling elements, and cage are as follows:

[0029]

[0030]

[0031]

[0032]

[0033] Among them, f r Where n is the physical shaft rotational frequency, D is the number of rolling elements, d is the rolling bearing pitch diameter, and α is the contact angle.

[0034] In one embodiment, the method further includes:

[0035] If the characteristic order of inner ring failure and its harmonic order appear on the order spectrum, it is determined that there is an inner ring failure in the rolling bearing.

[0036] If the outer ring fault characteristic order and its harmonic order appear on the order spectrum, it is determined that the rolling bearing has an outer ring fault.

[0037] If the characteristic order of cage failure and its harmonic order appear on the order spectrum, then it is determined that the rolling bearing has a cage failure.

[0038] If the rolling element fault characteristic order and its harmonic order appear on the order spectrum, then it is determined that there is a rolling element fault in the rolling bearing.

[0039] According to a second aspect, the present invention provides a diagnostic apparatus for rolling bearing failures, the apparatus comprising:

[0040] The acquisition module is used to acquire the first rotational speed of the physical shaft of the rolling bearing;

[0041] The determination module is used to determine the second rotational speed of the corresponding virtual shaft based on the first rotational speed of the physical shaft and the structural parameter information of the bearing;

[0042] The first obtaining module is used to perform time-domain synchronous averaging analysis on the vibration signal on the virtual axis based on the second rotational speed of the virtual axis, so as to obtain the processed vibration signal;

[0043] The second module is used to map the processed vibration signal onto the physical axis, perform order spectrum analysis, and obtain the fault diagnosis result of the rolling bearing.

[0044] According to a third aspect, the present invention provides a computer device including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the method for diagnosing rolling bearing failure as described in any one of the first aspects and its alternative embodiments.

[0045] According to a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions for causing the computer to perform a method for diagnosing rolling bearing failure according to any one of the first aspect and its alternative embodiments.

[0046] The technical solution of this invention has the following advantages:

[0047] This invention provides a method for diagnosing rolling bearing faults. By setting a virtual axis, the rotational speed of the virtual axis is determined based on the rotational speed of the physical axis and the bearing structural parameters. Time-domain synchronous averaging analysis is then performed on the virtual axis to avoid the inability to diagnose rolling bearing faults due to the bearing fault characteristic frequency and the shaft rotational frequency not being integer multiples of each other. Time-domain synchronous averaging analysis is performed on the vibration signal on the virtual axis, and the processed signal is mapped onto the physical axis for spectrum analysis. This method enables the diagnosis of rolling bearing faults by setting a virtual axis, thereby facilitating the diagnosis of bearing faults and improving the accuracy of rolling bearing fault diagnosis results. Attached Figure Description

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1 This is a flowchart of a method for diagnosing rolling bearing failures proposed in an embodiment of the present invention;

[0050] Figure 2 This is a time-domain diagram of the simulation signal for an inner ring fault proposed in an embodiment of the present invention;

[0051] Figure 3 This is the order spectrum of inner ring faults proposed in the embodiments of the present invention;

[0052] Figure 4 This is a virtual axis synchronous average time-domain diagram proposed in an embodiment of the present invention;

[0053] Figure 5 This is a time-domain diagram of the physical axis fault impact response proposed in an embodiment of the present invention;

[0054] Figure 6 This is the order spectrum on the physical axis proposed in the embodiments of the present invention;

[0055] Figure 7 This is the original time-domain diagram of the vibration signal proposed in the embodiments of the present invention;

[0056] Figure 8 This is the envelope spectrum after amplitude adjustment proposed in the embodiments of the present invention;

[0057] Figure 9 This is the order spectrum of the envelope signal proposed in the embodiments of the present invention;

[0058] Figure 10 This is the time-domain synchronization average time-domain diagram proposed in the embodiments of the present invention;

[0059] Figure 11 This is the order spectrum of the time-domain synchronous average signal proposed in the embodiments of the present invention;

[0060] Figure 12 This is a structural block diagram of a rolling bearing fault diagnosis device proposed in an embodiment of the present invention;

[0061] Figure 13 This is a schematic diagram of the hardware structure of a computer device proposed in an embodiment of the present invention. Detailed Implementation

[0062] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Rolling bearings are core components in rotating machinery. Their function is to convert the sliding friction between the shaft and the bearing housing into rolling friction, thereby reducing frictional losses. However, errors are unavoidable during the manufacturing and assembly of rolling bearings. Furthermore, the harsh working environment of rolling bearings, subjected to alternating loads over long periods, can all lead to bearing failure.

[0064] Currently, the main methods for diagnosing rolling bearing faults include: Fast Fourier Transform (FFT), Empirical Mode Decomposition (EMD), Wavelet Transform (WT), and Time Synchronous Averaging (TSA). While all these methods can diagnose bearing faults, they all have certain limitations.

[0065] Fast Fourier Transform (FFT) is effective in processing stationary periodic signals, but most bearing signals in actual operation are non-periodic and non-stationary. While FFT can intuitively reflect each frequency component, it cannot reflect the effect of time variation on each frequency component.

[0066] Empirical Mode Decomposition (EMD) is highly effective for processing aperiodic and non-stationary signals. EMD can adaptively decompose a signal from high to low frequency into several intrinsic mode components and a residual signal, achieving signal subdivision. This allows for the extraction of the most fault-sensitive frequency bands to construct damage factors that can effectively identify faults. However, EMD suffers from drawbacks such as mode aliasing and endpoint effects when processing signals.

[0067] The greatest advantage of wavelet transform in signal processing is its adaptive adjustment of the time-frequency window. Based on the characteristics of the signal being processed, it adaptively adjusts the time-domain and frequency resolution, thus achieving better results. However, the quality of wavelet transform results is affected by the wavelet basis. Currently, wavelet bases are selected based on past experience, without a clear standard, which limits the application of wavelet transform to some extent.

[0068] Time-domain synchronous averaging is one of the most effective techniques for extracting periodic signal components from complex signals. However, it typically only enhances the coherent signal components synchronized with the fundamental frequency signal. This is because gear meshing frequency and damage information generally have an integer multiple relationship with shaft rotation frequency, thus limiting its application to gear fault studies. In contrast, rolling bearing fault characteristic frequencies generally do not have such an integer multiple relationship with shaft rotation frequency; that is, the damage signal and shaft speed signal of rolling bearings are not synchronized. Therefore, time-domain synchronous averaging also has limitations in extracting fault information from rolling bearings.

[0069] Time-domain synchronous averaging technology can enhance the synchronous coherent signal components with the fundamental frequency signal. Since gear meshing frequency and damage information generally have an integer relationship with shaft rotation frequency, time-domain synchronous averaging technology can extract fault characteristic frequencies from complex gear signals. Researchers collected cage rotational speed by drilling holes in the outer ring of the bearing and constructed a relative reference frame with the cage stationary using the shaft speed signal and cage rotational speed information. In this relative reference frame, the bearing fault characteristic frequency has an integer multiple relationship with the shaft rotation frequency, meaning the bearing damage frequency components are synchronized with the relative speed signal of the shaft. Therefore, time-domain synchronous averaging technology can be used for bearing fault analysis. In practical engineering applications, drilling holes in the outer ring of rolling bearings and installing additional devices on the cage are often not permitted, as these would affect the integrity and structural strength of the rolling bearing.

[0070] To diagnose rolling bearing faults, this invention provides a method for diagnosing rolling bearing faults, such as... Figure 1 As shown, the method includes the following steps S101 to S104.

[0071] Step S101: Obtain the first rotational speed of the physical shaft of the rolling bearing.

[0072] In this embodiment of the invention, on the experimental platform, a drive motor drives the rotation of the shaft. An infrared sensor is installed on the platform, and a reflector is installed on the shaft at the output end of the drive motor. Every time the rolling bearing rotates once, the infrared sensor receives a pulse signal. The rotation frequency of the shaft is calculated by calculating the frequency of the pulse signal, and this is used as the first rotation speed of the physical shaft of the rolling bearing.

[0073] Step S102: Based on the first rotational speed of the physical axis and the structural parameter information of the bearing, determine the second rotational speed of the corresponding virtual axis.

[0074] In this embodiment of the invention, the structural parameter information of the bearing includes the number of rolling elements, the rolling shaft pitch diameter, the rolling element diameter, and the contact angle. Since the fault characteristic frequency of the rolling bearing and the shaft rotation frequency are not integer multiples of each other, that is, the fault information of the physical shaft and the shaft speed information are not synchronized, it is impossible to diagnose the rolling bearing fault by the rotation speed of the physical shaft. Therefore, a virtual shaft is introduced. Based on the rotation speed of the physical shaft and the structural parameter information of the bearing, the rotation speed of the virtual shaft is determined so as to perform analysis on the virtual shaft.

[0075] Step S103: Based on the second rotational speed of the virtual shaft, perform time-domain synchronous averaging analysis on the vibration signal on the virtual shaft to obtain the processed vibration signal.

[0076] In this embodiment of the invention, a time-domain synchronous averaging analysis is performed on the virtual axis based on the second rotational speed of the virtual axis. Specifically, the time-domain synchronous averaging analysis on the virtual axis involves performing iso-angle sampling on the virtual axis. Iso-angle sampling means sampling according to the same angle rotated by the axis during the sampling process, rather than sampling according to time. Iso-angle sampling can also be obtained by interpolating iso-time sampling data.

[0077] After sampling the virtual axis at equal angles, the vibration signal is obtained. Then, the vibration signal is synchronously averaged and analyzed. That is, the vibration signal is divided equally according to the data length, the equally divided data are superimposed, and finally the superimposed value is divided by the number of equally divided data to obtain the vibration signal.

[0078] It should be noted that the periodic signal axis in this research process is T0 = l*T c , among which, T c Indicates the virtual axis period. l represents the number of times the fault information is repeated after processing. l can be set according to the actual application scenario, and the value of l can be the same as the number of rollers.

[0079] Step S104: Map the processed vibration signal onto the physical axis, perform order spectrum analysis, and obtain the fault diagnosis results of the rolling bearing.

[0080] In this embodiment of the invention, since the signal on the virtual axis lacks a corresponding physical concept, the signal of the virtual axis is mapped to the physical axis, and the vibration signal mapped to the physical axis is analyzed to determine whether the rolling bearing has a fault.

[0081] Through the above embodiments, a virtual axis is first set up, and the rotational speed of the virtual axis is determined based on the rotational speed of the physical axis. Time-domain synchronous averaging analysis of the vibration signal is then performed on the virtual axis. This avoids the inability to diagnose rolling bearing faults due to the lack of an integer multiple relationship between the bearing fault characteristic frequency and the shaft rotational frequency. The vibration signal is obtained by performing time-domain synchronous averaging analysis on the virtual axis and mapped onto the physical axis. This enables the diagnosis of rolling bearing faults by setting up a virtual axis, thereby improving the accuracy of rolling bearing fault diagnosis results.

[0082] Specifically, in one embodiment, determining the second rotational speed of the corresponding virtual axis based on the first rotational speed of the physical axis in step S102 includes the following steps:

[0083] Step S1021: Obtain the pre-set correspondence between the first speed and the second speed.

[0084] Step S1022: Based on the first rotational speed of the physical axis and the corresponding relationship, determine the second rotational speed of the corresponding virtual axis.

[0085] In this embodiment of the invention, there is a multiple relationship between rotational speed and rotational frequency. Multiplying the rotational frequency by 60 gives the rotational speed, and the first rotational frequency f of the physical axis is... r and the second rotational frequency f of the virtual axis c There exists a ratio relationship between them, and this ratio is the value k. The value k is a constant determined by the structural parameters of the rolling bearing, k = f c / f r The rotational frequency and rotational speed are in a multiple of 60, meaning the first and second rotational speeds of the physical axis are also in a multiple of k. Based on the first rotational speed of the physical axis, multiplying the first rotational speed of the physical axis by the ratio k yields the second rotational speed of the virtual axis.

[0086] By setting up a virtual axis, the rotational speed of the virtual axis can be determined based on the rotational speed information of the physical axis. This avoids the situation where the bearing fault characteristic frequency of the physical axis and the shaft rotational frequency generally do not have such an integer multiple relationship, making it impossible to analyze using time-domain synchronous averaging technology. Setting up a virtual axis can improve the applicability of time-domain synchronous averaging technology to this solution, so as to facilitate the fault diagnosis of rolling bearings using time-domain synchronous averaging technology based on the rotational speed of the virtual axis.

[0087] Specifically, in one embodiment, step S104 above maps the processed vibration signal onto the physical shaft, performs order spectrum analysis, and obtains the fault diagnosis result of the rolling bearing. This specifically includes the following steps:

[0088] Step S1041: Perform a fast Fourier transform on the vibration signal mapped to the physical axis to obtain the order spectrum.

[0089] Step S1042: Analyze the rolling bearing faults based on the order spectrum to obtain the fault diagnosis results of the rolling bearings.

[0090] In this embodiment of the invention, a Fast Fourier Transform (FFT) is performed on the vibration signal mapped to the physical axis to obtain the order spectrum of the vibration signal. The method of obtaining the order spectrum by performing a FFT on the signal is prior art and will not be elaborated upon here. Analyzing rolling bearing faults based on the order spectrum can improve the accuracy of rolling bearing fault diagnosis results.

[0091] Specifically, in one embodiment, step S1042 above, which analyzes the rolling bearing fault based on the order spectrum to obtain the rolling bearing fault diagnosis result, specifically includes the following steps:

[0092] Step S10421: Determine the fault characteristic order in the order spectrum, and determine the fault diagnosis result of the rolling bearing based on the fault characteristic order.

[0093] In this embodiment of the invention, by determining the fault diagnosis result of the rolling bearing based on the fault feature order in the order spectrum, the interference of other interfering components on the fault diagnosis result can be reduced, thereby improving the accuracy of the rolling bearing fault diagnosis result.

[0094] Specifically, in one embodiment, when the bearing diagnosis result of the rolling bearing is that there is a fault, the rolling bearing fault diagnosis method provided by the present invention further includes the following steps:

[0095] Step S105: Based on the vibration signal mapped onto the physical axis, perform order spectrum analysis to determine the fault type of the rolling bearing.

[0096] In this embodiment of the invention, the corresponding fault characteristic order of the inner ring, the fault characteristic order of the outer ring, the fault characteristic order of the cage, the fault characteristic order of the rolling element and their harmonic order are found from the order spectrum, thereby determining the fault type of the rolling bearing.

[0097] Rolling bearings generally consist of an inner ring, an outer ring, rolling elements, and a cage. These correspond to inner ring failures, outer ring failures, rolling element failures, and cage failures, respectively. By analyzing the fault characteristics, the corresponding fault order can be identified, which helps to pinpoint the specific component of the rolling bearing that is failing. This improves the accuracy of fault diagnosis and facilitates timely repair and handling of the faulty component by the staff.

[0098] Among them, the inner ring fault characteristic frequency f BPFI for:

[0099]

[0100] Outer ring fault characteristic frequency f BPFO for:

[0101]

[0102] cage failure characteristic frequency f FIF for:

[0103]

[0104] Rolling element failure characteristic frequency f BSF for:

[0105]

[0106] Among them, f r Where n is the physical shaft rotational frequency, D is the number of rolling elements, d is the rolling bearing pitch diameter, and α is the contact angle.

[0107] The formulas for calculating the order of failure characteristics for the inner ring, outer ring, rolling elements, and cage are as follows:

[0108]

[0109]

[0110]

[0111]

[0112] Specifically, the second rotational speed of the corresponding virtual axis can be determined using the first rotational speed of the physical axis and the bearing structural parameter information through the following steps:

[0113] (1) Assume the rotational frequency of the virtual axis is f. c If we are studying the failure of the bearing inner ring, let f c =f BPFI Then f c f r =C, where C is the corresponding inner ring fault order, obtained according to formula (5):

[0114]

[0115] (2) If we are studying the failure of the bearing outer ring, let f c =f BPFO Then f c f r =C, where C is the corresponding outer ring fault order, obtained according to formula (6):

[0116]

[0117] (3) If we are studying bearing cage failure, let f c =f FTF Then f c f r =C, where C is the corresponding cage failure order, obtained according to formula (7):

[0118]

[0119] (4) If we are studying bearing rolling element failure, let f c =f BSF Then f c f r =C, where C is the corresponding rolling element failure order, obtained according to formula (8):

[0120]

[0121] As can be seen from the above formula, the ratio C of the virtual axis frequency to the physical axis frequency is only related to the parameters of the bearing structure. Therefore, the virtual axis frequency can be calculated by using the physical axis frequency and the bearing structure parameter information.

[0122] Specifically, in one embodiment, the method for diagnosing rolling bearing failures provided by this invention further includes the following steps:

[0123] When studying bearing inner ring faults, step S106: if the characteristic order of inner ring faults and its harmonic order appear on the corresponding order spectrum, then it is determined that the rolling bearing has an inner ring fault.

[0124] When studying bearing outer ring faults, step S107: if the characteristic order of outer ring faults and its harmonic order appear on the corresponding order spectrum, then it is determined that the rolling bearing has an outer ring fault.

[0125] When studying bearing cage failure, step S108: if the characteristic order of cage failure and its harmonic order appear on the corresponding order spectrum, then it is determined that the rolling bearing has a cage failure.

[0126] When studying rolling element failures in bearings, step S109: if the characteristic order of rolling element failure and its harmonic order appear on the corresponding order spectrum, then it is determined that there is a rolling element failure in the rolling bearing.

[0127] In this embodiment of the invention, the fault type of the rolling bearing is determined based on the fault characteristic order or its harmonic order appearing on the order spectrum.

[0128] For example, this application embodiment uses a bearing with the simulation model NU204 ECP, whose specific structural parameters are: 11 rollers, a pitch circle diameter of 34mm, a roller diameter of 7.5mm, and a contact angle of 0deg. In the simulation, the input motor speed is set to a constant 1500r / min. According to the above formulas (1) to (4), the fault characteristic order of the corresponding parts is calculated. The fault characteristic order of each part is shown in Table 1.

[0129] Table 1

[0130] Damaged area Feature order Inner circle 6.713 Outer ring 4.287 cage 0.3897 Rolling body 2.156

[0131] Simulated time-domain signal of inner ring fault as follows Figure 2 As shown, order analysis of the simulated signal yields the order spectrum as follows. Figure 3 As shown in Table 1, the characteristic order of the inner circle is 6.713, i.e., c = 6.713. Figure 4 This is the result of time-domain synchronous averaging analysis performed on the virtual axis. Figure 4 Within each virtual axis cycle, there exists an impact response caused by a fault. Figure 4 The signal is returned to the physical axis, and the signal representing the fault impact response on the physical axis is obtained, such as... Figure 5 As shown, Figure 4 The signal is subjected to a Fast Fourier Transform to obtain the order spectrum, and then... Figure 3 compared to, Figure 6 The amplitudes of the fault characteristic order and its harmonic order are significantly higher than those of other orders, indicating that... Figure 5 The signal mainly consists of inner ring fault information, and this method accurately identifies bearing inner ring faults.

[0132] For example, this application embodiment uses a bearing of model 6304, whose specific structural parameters are: pitch circle diameter of 36mm, roller diameter of 9.525mm, number of rollers of 7, contact angle of 0 degrees, and drive motor speed of 1500r / min. The bearing is pre-damaged in the inner ring using wire cutting. The characteristic order of faults in various parts of the 6034 bearing is shown in Table 2.

[0133] Table 2

[0134] Damaged area Feature order Inner circle 4.426 Outer ring 2.574 cage 0.3677 Rolling body 1.757

[0135] Vibration signals collected by accelerometers, such as Figure 7 As shown, the vibration signal has complex components, making it difficult to extract useful information. A bandpass filter of 4kHz to 5kHz is used to... Figure 7 The signal is filtered to extract the corresponding high-frequency components, and then the Hilbert transform is used for amplitude demodulation to obtain the envelope signal, such as... Figure 8 As shown. Order analysis is performed on the envelope signal, as follows: Figure 9 As shown, many spectral lines were found in the order spectrum, which obscured the spectral lines of the fault characteristic order and its harmonic order, making it impossible to diagnose the fault in the bearing inner ring.

[0136] right Figure 7 The signal is analyzed by time-domain synchronous averaging using a virtual axis to obtain a high signal-to-noise ratio fault impulse response, such as... Figure 10 As shown. An order analysis is performed on the results after time-domain synchronous averaging analysis, as follows: Figure 11 As shown, the order spectrum amplitudes are concentrated at the fault order and its harmonics, while other complex background signals are suppressed.

[0137] Analysis of bearing inner ring fault test data shows that envelope analysis may fail in the case of early faults or background noise interference. However, time-domain synchronous averaging technology based on a virtual axis improves the signal-to-noise ratio of fault characteristic signals, thereby enhancing the accuracy of bearing fault diagnosis.

[0138] Based on the same inventive concept, the present invention also provides a diagnostic device for rolling bearing failure.

[0139] Figure 12 This is a structural block diagram of a diagnostic device for rolling bearing faults according to an exemplary embodiment. Figure 12 As shown, the device includes:

[0140] The acquisition module 101 is used to acquire the first rotational speed of the physical shaft of the rolling bearing. For details, please refer to the relevant description of step S101 above, which will not be repeated here.

[0141] The determination module 102 is used to determine the second rotational speed of the corresponding virtual axis based on the first rotational speed of the physical axis and the structural parameter information of the bearing. For details, please refer to the relevant description of step S102 above, which will not be repeated here.

[0142] The first module 103 is used to perform time-domain synchronous analysis of the vibration signal on the virtual axis based on the second rotational speed of the virtual axis, thereby obtaining the processed vibration signal. For details, please refer to the relevant description of step S103 above, which will not be repeated here.

[0143] The second module 104 is used to map the processed vibration signal onto the physical axis, perform order spectrum analysis, and obtain the fault diagnosis results of the rolling bearing. For details, please refer to the relevant description of step S104 above, which will not be repeated here.

[0144] The rolling bearing fault diagnosis device provided in this embodiment of the invention sets up a virtual axis and determines the rotational speed of the virtual axis based on the rotational speed of the physical axis. This allows for time-domain synchronous averaging analysis of the vibration signal on the virtual axis, thus avoiding the inability to diagnose rolling bearing faults due to the lack of an integer multiple relationship between the bearing fault characteristic frequency and the shaft rotational frequency. The vibration signal obtained through time-domain synchronous averaging analysis on the virtual axis is then mapped onto the physical axis, enabling the diagnosis of rolling bearing faults by setting up a virtual axis. This facilitates bearing fault diagnosis and improves the accuracy of rolling bearing fault diagnosis results.

[0145] The specific limitations and beneficial effects of the aforementioned diagnostic device for rolling bearing faults can be found in the limitations of the diagnostic method for rolling bearing faults described above, and will not be repeated here. Each of the above modules can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0146] Figure 13 This is a schematic diagram of the hardware structure of a computer device according to an exemplary embodiment. For example... Figure 13 As shown, the device includes one or more processors 1310 and a memory 1320, the memory 1320 including persistent memory, volatile memory, and a hard disk. Figure 13 Taking a processor 1310 as an example, the device may also include an input device 1330 and an output device 1340.

[0147] The processor 1310, memory 1320, input device 1330, and output device 1340 can be connected via a bus or other means. Figure 13 Taking the example of a connection between China and Israel via a bus.

[0148] Processor 1310 can be a Central Processing Unit (CPU). Processor 1310 can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or combinations thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0149] The memory 1320, as a non-transitory computer-readable storage medium, includes persistent memory, volatile memory, and a hard disk. It can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the business management method in this embodiment. The processor 1310 executes various server functions and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 1320, thereby implementing any of the above-mentioned methods for diagnosing rolling bearing faults.

[0150] The memory 1320 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data that is needed and required. Furthermore, the memory 1320 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1320 may optionally include memory remotely located relative to the processor 1310, and these remote memories may be connected to the data processing device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0151] Input device 1330 can receive input numerical or character information, and generate key signal inputs related to user settings and function control. Output device 1340 may include display devices such as a display screen.

[0152] One or more modules are stored in memory 1320, and when executed by one or more processors 1310, they perform actions such as... Figure 1 The method for diagnosing rolling bearing failure is shown.

[0153] The above-described product can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in [reference 1]. Figure 1 The relevant descriptions in the illustrated embodiments.

[0154] This invention also provides a non-transitory computer storage medium storing computer-executable instructions that can execute the methods described in any of the above method embodiments. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk drive (HDD), or solid-state drive (SSD), etc.; the storage medium may also include combinations of the above types of memory.

[0155] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method of diagnosing a fault of a rolling bearing, characterized in that, The method includes: Obtain the first rotational speed of the physical shaft of the rolling bearing; Based on the first rotational speed of the physical axis and the structural parameter information of the bearing, the second rotational speed of the corresponding virtual axis is determined; Based on the second rotational speed of the virtual axis, a time-domain synchronous averaging analysis is performed on the vibration signal on the virtual axis to obtain the processed vibration signal; The processed vibration signal is mapped onto the physical axis, and order spectrum analysis is performed to obtain the fault diagnosis results of the rolling bearing. Based on the first rotational speed of the physical axis, determine the second rotational speed of the corresponding virtual axis, including: Obtain the pre-set correspondence between the first and second rotational speeds; Based on the first rotational speed of the physical axis and the corresponding relationship, the second rotational speed of the corresponding virtual axis is determined; There is a multiple relationship between rotational speed and rotational frequency. Multiplying the rotational frequency by 60 gives the rotational speed, which is the first rotational frequency of the physical axis. f r and the second rotation frequency of the virtual axis f c There is a ratio correspondence between them, and the ratio is... k value, k The value is a constant determined by the structural parameters of the rolling bearing. k = f c / f r The frequency and rotational speed are in a 60-fold relationship, meaning the first and second rotational speeds of the physical axis are also in a 60-fold relationship. k The ratio is based on the first rotational speed of the physical axis, multiplied by a ratio. k The second rotational speed of the virtual axis is obtained.

2. The method according to claim 1, characterized in that, The process of mapping the processed vibration signal onto the physical axis and performing order spectrum analysis to obtain the fault diagnosis results of the rolling bearing includes: The order spectrum is obtained by performing a fast Fourier transform on the vibration signal mapped to the physical axis. Based on the order spectrum, the rolling bearing faults are analyzed to obtain the fault diagnosis results of the rolling bearings.

3. The method according to claim 2, characterized in that, The analysis of rolling bearing faults based on the order spectrum to obtain rolling bearing fault diagnosis results includes: Determine the fault characteristic order in the order spectrum, and determine the fault diagnosis result of the rolling bearing based on the fault characteristic order.

4. The method according to claim 1, characterized in that, When the fault diagnosis result of the rolling bearing is that there is a fault, the method further includes: Order spectrum analysis is performed based on vibration signals mapped onto the physical axis to determine the fault type of the rolling bearing.

5. The method according to claim 4, characterized in that, Inner ring fault characteristic frequency for: Outer ring fault characteristic frequency for: Cage Failure Characteristic Frequency for: Rolling element failure characteristic frequency for: The formulas for calculating the order of failure characteristics for the inner ring, outer ring, rolling elements, and cage are as follows: in, Where n is the physical shaft rotational frequency, n is the number of rolling elements, and D is the rolling bearing pitch diameter. The diameter of the rolling element, It is the contact angle.

6. The method according to claim 5, characterized in that, The method further includes: If the characteristic order of inner ring failure and its harmonic order appear on the order spectrum, it is determined that there is an inner ring failure in the rolling bearing. If the outer ring fault characteristic order and its harmonic order appear on the order spectrum, it is determined that the rolling bearing has an outer ring fault. If the characteristic order of cage failure and its harmonic order appear on the order spectrum, then it is determined that the rolling bearing has a cage failure. If the rolling element fault characteristic order and its harmonic order appear on the order spectrum, then it is determined that there is a rolling element fault in the rolling bearing.

7. A diagnostic device for rolling bearing faults, characterized in that, The device includes: The acquisition module is used to acquire the first rotational speed of the physical shaft of the rolling bearing; The determination module is used to determine the second rotational speed of the corresponding virtual shaft based on the first rotational speed of the physical shaft and the structural parameter information of the bearing; The first obtaining module is used to perform time-domain synchronous averaging analysis on the vibration signal on the virtual axis based on the second rotational speed of the virtual axis, so as to obtain the processed vibration signal; The second module is used to map the processed vibration signal onto the physical axis, perform order spectrum analysis, and obtain the fault diagnosis result of the rolling bearing. The determining module is specifically used for: obtaining a pre-set correspondence between a first rotational speed and a second rotational speed; determining the second rotational speed of the corresponding virtual axis based on the first rotational speed of the physical axis and the correspondence; wherein, there is a multiple relationship between rotational speed and rotational frequency, and the rotational frequency is multiplied by 60 to obtain the rotational speed, and the first rotational frequency of the physical axis... f r and the second rotation frequency of the virtual axis f c There is a ratio correspondence between them, and the ratio is... k value, k The value is a constant determined by the structural parameters of the rolling bearing. k = f c / f r The frequency and rotational speed are in a 60-fold relationship, meaning the first and second rotational speeds of the physical axis are also in a 60-fold relationship. k The ratio is based on the first rotational speed of the physical axis, multiplied by a ratio. k The second rotational speed of the virtual axis is obtained.

8. A computer device, characterized in that, The method includes a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the method for diagnosing rolling bearing failure as described in any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the diagnostic method for rolling bearing failure as described in any one of claims 1-6.

Images