Bearing composite fault diagnosis method and system

Abstract

The invention discloses a bearing composite fault diagnosis method and system. Firstly, a bearing composite fault vibration observation signal is obtained; then short-time Fourier transform and cepstrum threshold processing are carried out on the observation signals, two-dimensional time-frequency masking blind source separation is carried out, inverse short-time Fourier transform and band-pass filtering are carried out on obtained time-frequency domain separation signals, and independent time domain estimation signals are obtained; performing Hilbert envelope demodulation and Fourier transform on the estimated signal to obtain an envelope spectrum of the filtered signal; finally, searching to obtain each order of characteristic frequency band of the target fault, constructing a harmonic energy impact index # imgabs0 #, and calculating an impact pulse value; and comparing the calculated impact pulse value with a threshold value, and diagnosing whether the bearing has a target type of fault or not.

Description

Technical Field

[0001] The invention belongs to the technical field of rolling bearing fault diagnosis, and in particular relates to a bearing composite fault diagnosis method and system. Background Art

[0002] Rolling bearings play an important role in industrial production. As they often work at high speeds, heavy loads and in complex environments, various forms of failures are unavoidable, such as pitting, cracking and spalling of the outer ring and rolling elements. Moreover, the degradation of the bearing health status is often not just a single point failure, but occurs in the form of compound failures. In fault diagnosis based on vibration signals, multi-source fault signals are coupled with each other, making it difficult to extract fault features, which brings great challenges to accurate bearing fault diagnosis.

[0003] At present, there are two main technical routes for bearing fault diagnosis based on vibration signals. One is to extract fault features by signal processing, and then identify faults through expert experience or shallow machine learning models; the other is to directly input the signal into a trained deep neural network model to automatically extract features and complete fault identification and classification; however, the fault detection method based on signal processing is more dependent on expert experience and feature extraction quality, and the generalization ability of shallow machine learning models is usually poor; the fault diagnosis method based on deep learning has poor interpretability and relies on a large number of labeled samples for model pre-training; both of the above have drawbacks that cannot be ignored. The shock pulse method determines the health status of the bearing by detecting the shock information, which has the advantages of being simple and effective; however, the traditional shock pulse method can only evaluate the health status of the bearing, and cannot perform accurate fault diagnosis and achieve fault location.

[0004] In the complex fault diagnosis problem, due to the convolution coupling effect of the transmission path, it is difficult to extract fault features and make accurate diagnosis. Classical signal processing methods such as bandpass filtering can only filter out unexpected information in the specified frequency band, and cannot decompose the coupled resonant frequency band; traditional signal decomposition methods such as integrated empirical mode decomposition (EEMD) and wavelet packet decomposition (WPD) can only decompose the signal according to a certain scale, and the decoupling effect of signals with similar characteristic frequencies is poor. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a bearing composite fault diagnosis method and system in view of the deficiencies in the above-mentioned prior art, so as to solve the technical problems that the existing bearing composite fault diagnosis has low accuracy, high fault false alarm rate, and traditional signal processing methods and impact pulse methods are difficult to simply and efficiently realize accurate diagnosis of bearing composite faults.

[0006] The present invention adopts the following technical solutions: A bearing composite fault diagnosis method comprises the following steps: Firstly, the vibration observation signal of the bearing composite fault is obtained; Then the observed signal is subjected to short-time Fourier transform and cepstrum threshold processing, two-dimensional time-frequency masking blind source separation, and the obtained time-frequency domain separation signal is subjected to inverse short-time Fourier transform and bandpass filtering to obtain an independent time domain estimation signal; Then, the estimated signal is subjected to Hilbert envelope demodulation and Fourier transform to obtain the envelope spectrum of the filtered signal; Finally, the characteristic frequency bands of each order of the target fault are searched and a harmonic energy impact index is constructed. , and calculate the shock pulse value; The calculated shock pulse value is compared with the threshold value to diagnose whether the bearing has a target type fault.

[0007] Preferably, the observation signal is subjected to short-time Fourier transform and cepstrum threshold processing as follows: In the time-frequency domain, the observed signal The absolute value of , determine the cepstrum of the time-frequency logarithmic spectrum of the observed signal ; The inverse spectrum threshold is obtained by introducing the Fourier threshold ; Determine the logarithmic amplitude spectrum.

[0008] Preferably, the inverse short-time Fourier transform for:

[0009] in, t For time, f is the frequency, for, is the inverse frequency index, n is the signal channel index, For the n The observed signal of the channel.

[0010] Preferably, the cepstrum of the time-frequency logarithmic spectrum of the observed signal is for:

[0011]

[0012] in, is the Fourier transform in the frequency direction, for, for, is the frequency scale.

[0013] Preferably, the two-dimensional time-frequency masking blind source separation is specifically: For the form The signal time-frequency domain convolution mixing process, and its unmixing process is , for dimensional mixing matrix, is the source signal, is the separation matrix to be solved, To estimate the signal; by solving the unmixing matrix Restore the source signal and use the mask to perform time-frequency shielding on the group threshold operator to achieve blind source separation of the observed signal.

[0014] Preferably, solving the mixing matrix The objective function used is:

[0015] in, is the number of observed signal channels, is the frequency scale of the Fourier transform, is the loss function.

[0016] Preferably, the group threshold operator is as follows:

[0017] in, is an input dependent A nonnegative scalar.

[0018] Preferably, the shock pulse value for:

[0019] in, is the shaft speed, It is the harmonic energy impact index. is the bearing inner diameter.

[0020] Preferably, the harmonic energy impact index for:

[0021] in, For the i Characteristic frequency band.

[0022] In a second aspect, an embodiment of the present invention provides a bearing composite fault diagnosis system, comprising: Signal module, to obtain vibration observation signal of bearing composite fault; The processing module performs short-time Fourier transform and cepstrum threshold processing on the observed signal, and two-dimensional time-frequency masking blind source separation, and performs inverse short-time Fourier transform and bandpass filtering on the obtained time-frequency domain separation signal to obtain an independent time domain estimation signal; A transform module performs Hilbert envelope demodulation and Fourier transform on the estimated signal to obtain the envelope spectrum of the filtered signal; The construction module searches for characteristic frequency bands of each order of the target fault and constructs a harmonic energy impact index. , and calculate the shock pulse value; The judgment module compares the calculated impact pulse value with the threshold value to diagnose whether the bearing has a target type fault.

[0023] In a third aspect, a computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned bearing composite fault diagnosis method when executing the computer program.

[0024] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, comprising a computer program, which implements the steps of the above-mentioned bearing composite fault diagnosis method when executed by a processor.

[0025] In a fifth aspect, a chip comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned bearing composite fault diagnosis method when executing the computer program.

[0026] In a sixth aspect, an embodiment of the present invention provides an electronic device, comprising a computer program, which implements the steps of the above-mentioned bearing composite fault diagnosis method when executed by the electronic device.

[0027] Compared with the prior art, the present invention has at least the following beneficial effects: A bearing composite fault diagnosis method uses effective signal processing methods to construct effective impact indicators to improve the traditional impact pulse method. It can combine the advantages of signal processing methods and impact pulse methods, and simply and effectively complete the task of bearing accurate fault diagnosis through vibration signals, solving the problem that the traditional impact pulse method can only evaluate the health status of the bearing and cannot perform accurate fault diagnosis. The advanced blind source separation method is used to separate and decouple the composite fault observation signal, and then the improved impact pulse method is used to perform fault diagnosis on the estimated signal, which can accurately and automatically complete the bearing composite fault diagnosis, solving the problem that the traditional signal filtering and decomposition methods have low separation accuracy for the bearing composite fault observation signal and are difficult to independently complete the composite fault diagnosis task.

[0028] Furthermore, by performing short-time Fourier transform on the observed signal to obtain a logarithmic amplitude spectrum, the time domain convolution effect in the observed signal can be eliminated, while the sparsity of the signal in the time and frequency domains can be enhanced; the logarithmic amplitude spectrum is further processed by cepstrum thresholding to enhance the dominant signal with a harmonic structure and weaken other noise components.

[0029] Furthermore, by performing two-dimensional time-frequency masking blind source separation on the enhanced observation signal, it is possible to accurately separate the independent single fault signal from the composite fault observation signal without the need for fault source and vibration transmission path information, and then diagnose the composite fault using the single fault diagnosis method.

[0030] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0031] In summary, the present invention uses an advanced blind source separation method to separate and decouple the composite fault observation signal, and then uses an improved impact pulse method to perform fault diagnosis on the estimated signal. It can accurately and automatically complete the bearing composite fault diagnosis, improve the accuracy, convenience and automation of bearing composite fault diagnosis, reduce the fault false alarm rate, and meet the needs of engineering applications.

[0032] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A method flow chart is provided for the present invention; Figure 2 It is a bearing failure test bench; Figure 3 shows the test bearing, where (a) is normal, (b) is inner ring crack, and (c) is outer ring crack; Figure 4 shows the time domain waveform of the observed signal and its envelope spectrum, where (a) is the estimated channel 1 and (b) is the estimated channel 2; FIG5 is the estimated signal time domain waveform and its envelope spectrum, where (a) is the estimated channel 1 and (b) is the estimated channel 2; Figure 6 shows the shock pulse value calculated from the estimated signal, where (a) is the estimated channel 1 and (b) is the estimated channel 2; FIG7 is a comparison diagram of the impulse pulse value of the directly calculated observation signal, where (a) is the estimation channel 1 and (b) is the estimation channel 2; Figure 8 A schematic diagram of a computer device provided by an embodiment of the present invention; Fig. 9 The block diagram of a chip provided according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0036] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0037] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0038] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0039] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0040] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0041] The present invention provides a bearing composite fault diagnosis method, which utilizes an effective signal processing method to extract target impact components from a vibration signal, constructs an effective impact index to improve a traditional impact pulse method, and can combine the advantages of the signal processing method and the impact pulse method to simply and effectively complete the task of accurate bearing fault diagnosis through vibration signals; utilizes an advanced blind source separation method to separate and decouple the composite fault observation signal to obtain an independent single fault estimation signal, and then uses the improved impact pulse method to perform fault diagnosis, which can improve the accuracy of accurate diagnosis of bearing composite faults and reduce the false alarm rate of faults.

[0042] See also Figure 1 The present invention provides a bearing composite fault diagnosis method, comprising the following steps: S1. Obtaining bearing composite fault vibration observation signal; S2, using short-time Fourier transform to transform the filtered time-domain observation signal into the time-frequency domain and take the logarithm to obtain the time-frequency domain logarithmic spectrum, and using the cepstrum threshold method to enhance the harmonic structure of the observation signal; In the time-frequency domain, the observed signal The absolute value of , the cepstrum of the time-frequency logarithmic spectrum of the observed signal Written as: (1) (2) in, n is the signal channel index, t For time, f is the frequency, is the inverse frequency index, is the Fourier transform in the frequency direction.

[0043] By introducing the Fourier threshold: (3) Get the inverse spectrum threshold: (4) In formula (3), is the inverse Fourier transform in the frequency direction: (5) In formula (5), It is a general sparse promotion threshold operator, and a soft threshold can be selected.

[0044] The logarithmic amplitude spectrum of the observed signal is converted into a cepstrum by Fourier transform, and the enhanced logarithmic amplitude spectrum is obtained by thresholding the cepstrum coefficients and performing inverse Fourier transform. Cepstrum thresholding enhances the dominant signal with harmonic structure and weakens other components.

[0045] S3, constructing a two-dimensional time-frequency masking function similar to Wiener, and performing blind source separation on the observation signal with enhanced harmonic structure obtained in step S2; For the form The signal time-frequency domain convolution mixing process, and its unmixing process is , for dimensional mixing matrix, is the source signal, is the separation matrix to be solved, To estimate the signal.

[0046] The key to this step is to find the unmixing matrix To recover the source signal, the solution is based on the fact that the source signals are statistically independent of each other. The measure of independence can be reduced to the following form of minimization problem: (6) This type of minimization problem is solved using the Primal-dual Splitting (PDS) algorithm: (7) in, is the amount to be optimized, I and J is the objective function, L is a bounded linear operator.

[0047] because I and J They are all non-differentiable functions, so the following form of proximity operator is used instead of the gradient method to solve: (8) Express request Minimum variable value. is a temporary variable for the iteration process. is the variable to be optimized, is the step length.

[0048] Since the proximity operator of the sparsity-induced penalty function shown in formula (8) can be expressed by a threshold operator, the proximity operator of the 1-2 mixed norm is expressed by a group threshold operator as follows: (9) in, is the threshold parameter, means to replace negative values ​​with zero. express The first elements.

[0049] Furthermore, the group threshold operator is time-frequency masked with the following mask: (10) Therefore, the group threshold operator shown in formula (10) is expressed as: (11) in, is an input dependent A nonnegative scalar.

[0050] So far, an implicit source model has been defined using general time-frequency masking to achieve blind source separation of the observed signal.

[0051] S4, use inverse short-time Fourier transform to transform the separated signal into the time domain, and perform bandpass filtering to obtain an independent time domain estimation signal ; S5, the estimated signal obtained in step S4 Perform Hilbert envelope demodulation and then Fourier transform to obtain the envelope spectrum of the filtered signal ; S6. Calculate the bearing fault characteristic frequency based on vehicle speed and bearing parameters and its higher harmonics ,by As the center frequency, is the radius (a is a fixed coefficient), and the characteristic frequency bands of each order of the target fault are searched in the envelope spectrum obtained in step S4 ; S7. Select the maximum value in each characteristic frequency band and calculate the square sum to construct a harmonic energy impact index. , and then calculate the shock pulse value, compare the calculated shock pulse value with the threshold, and diagnose whether the bearing has a target type fault.

[0052] The calculation method of the constructed harmonic energy impact index is as follows: (12) in, For the iCharacteristic frequency band.

[0053] The calculation method of the shock pulse value is: (13) in, is the shaft speed (r / min), and D is the bearing inner diameter (mm).

[0054] For general industrial bearings, some international industry standards stipulate: , the bearing should be replaced.

[0055] In another embodiment of the present invention, a bearing composite fault diagnosis system is provided, which can be used to implement the above-mentioned bearing composite fault diagnosis method. Specifically, the bearing composite fault diagnosis system includes a signal module, a processing module, a transformation module, a construction module and a judgment module.

[0056] Among them, the signal module obtains the vibration observation signal of the bearing composite fault; The processing module performs short-time Fourier transform and cepstrum threshold processing on the observed signal, and two-dimensional time-frequency masking blind source separation, and performs inverse short-time Fourier transform and bandpass filtering on the obtained time-frequency domain separation signal to obtain an independent time domain estimation signal; A transform module performs Hilbert envelope demodulation and Fourier transform on the estimated signal to obtain the envelope spectrum of the filtered signal; The construction module searches for characteristic frequency bands of each order of the target fault and constructs a harmonic energy impact index. , and calculate the shock pulse value; The judgment module compares the calculated impact pulse value with the threshold value to diagnose whether the bearing has a target type fault.

[0057] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the bearing composite fault diagnosis method, including: First, the vibration observation signal of the bearing composite fault is obtained; then the observation signal is short-time Fourier transformed and subjected to cepstrum threshold processing and two-dimensional time-frequency masking blind source separation, and the obtained time-frequency domain separation signal is subjected to inverse short-time Fourier transform and bandpass filtering to obtain an independent time domain estimation signal; then the estimated signal is Hilbert envelope demodulated and Fourier transformed to obtain the envelope spectrum of the filtered signal; finally, the characteristic frequency bands of each order of the target fault are searched to construct a harmonic energy impact index , and calculate the impact pulse value; compare the calculated impact pulse value with the threshold value to diagnose whether the bearing has a target type fault.

[0058] See also Figure 8 , the terminal device is a computer device. The computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the composition of the fluid in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the bearing composite fault diagnosis system in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0059] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 8This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0060] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0061] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0062] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0063] See also Fig. 9 The terminal device is an electronic device 600, which is in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0064] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0065] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0066] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0067] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0068] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0069] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It can be understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs. It should be noted that the computer-readable storage medium here can be a high-speed RAM memory, or a non-volatile memory (Non-VolatileMemory), such as at least one disk memory.

[0070] The processor may load and execute one or more instructions stored in a computer-readable storage medium to implement the corresponding steps of the bearing composite fault diagnosis method in the above embodiment; the processor may load and execute the following steps: First, the vibration observation signal of the bearing composite fault is obtained; then the observation signal is short-time Fourier transformed and subjected to cepstrum threshold processing and two-dimensional time-frequency masking blind source separation, and the obtained time-frequency domain separation signal is subjected to inverse short-time Fourier transform and bandpass filtering to obtain an independent time domain estimation signal; then the estimated signal is Hilbert envelope demodulated and Fourier transformed to obtain the envelope spectrum of the filtered signal; finally, the characteristic frequency bands of each order of the target fault are searched to construct a harmonic energy impact index , and calculate the impact pulse value; compare the calculated impact pulse value with the threshold value to diagnose whether the bearing has a target type fault.

[0071] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0072] Application Examples Step 1: Use the bearing fault test bench to conduct a bearing fault test to obtain the vibration signal of the bearing fault. The sensor is a three-axis acceleration sensor with a sampling frequency of 25.6kHz. The test bench is as follows: Figure 2 shown.

[0073] The test bearing is shown in Figure 3. There are two types of faults: outer ring cracks and inner ring cracks. The bearing parameters are shown in Table 1. The test condition is 1300rpm. It can be calculated that the characteristic frequency of the inner ring fault is 116Hz and the characteristic frequency of the outer ring fault is 76Hz.

[0074] Table 1 Bearing parameters

[0075] The two types of fault signals are convoluted and mixed to obtain the composite fault observation signals of the two channels. The time domain waveform and its envelope spectrum are shown in Figure 4. It can be seen that due to the convolution effect, the different fault characteristic frequencies in the envelope spectrum of the observation signal overlap with each other; and due to the influence of noise, the amplitude of the fault characteristic frequency is low, the amplitude of the noise component is high, and the fault feature extraction effect is not good.

[0076] Step 2: Use short-time Fourier transform to transform the filtered time-domain observation signal into the time-frequency domain and take the logarithm to obtain the time-frequency domain logarithmic spectrum, and further calculate the cepstrum. The absolute value of , by introducing the Fourier threshold , get the inverse spectrum threshold ,in, is the inverse Fourier transform in the frequency direction , It is a general sparse promotion threshold operator, and a soft threshold can be selected.

[0077] By thresholding the cepstral coefficients and performing an inverse Fourier transform, an enhanced logarithmic amplitude spectrum is obtained. Cepstral thresholding enhances the dominant signal with harmonic structure and weakens other components.

[0078] Step 3: Construct a two-dimensional time-frequency masking function similar to Wiener to perform blind source separation on the harmonic structure enhanced observation signal obtained in step 2. The key to this step is to find the unmixing matrix To recover the source signal, the solution is based on the statistical independence of the source signals. The group threshold operator is used to represent the proximity operator, and the final group threshold operator is obtained. The implicit source model is defined using general time-frequency masking to achieve blind source separation of the observed signal.

[0079] Step 4: Use inverse short-time Fourier transform to transform the separated signal into the time domain and perform bandpass filtering to obtain an independent time domain estimation signal. .

[0080] Step 5: Estimated signal obtained in step 4 Perform Hilbert envelope demodulation and then Fourier transform to obtain the envelope spectrum of the filtered signal .

[0081] The time domain waveform of the estimated signal obtained from step 3 to step 5 and its envelope spectrum are shown in Figure 5. It can be seen that after separation and decoupling, the inner ring fault estimation signal and the outer ring fault estimation signal are obtained respectively, and obvious fault characteristic frequencies, sidebands and high-order harmonics are extracted from their envelope spectra, and the noise is also weakened.

[0082] Step 6: Calculate the bearing fault characteristic frequency based on vehicle speed and bearing parameters and its higher harmonics ,by As the center frequency, is the radius (a is a fixed coefficient), and the characteristic frequency bands of each order of the target fault are searched in the envelope spectrum obtained in step 5 .

[0083] Step 7: Select the maximum value in each characteristic frequency band and calculate the square sum to construct a harmonic energy impact index , and then calculate the shock pulse value.

[0084] The alarm threshold is 50 dB, and the calculated impact pulse value is compared with the threshold to diagnose whether the bearing has a target type fault. The impact pulse value calculated by the estimated signal is shown in Figure 6. It can be seen that the method proposed in the present invention accurately detects the composite fault of the inner ring and the outer ring, and prevents the false alarm of other faults.

[0085] In summary, the present invention provides a bearing composite fault diagnosis method and system. In an application example of bearing inner and outer ring composite fault diagnosis, high signal-to-noise ratio inner ring fault and outer ring fault estimation signals are separated by feature enhancement of semi-physical simulation observation signals and two-dimensional time-frequency masking blind source separation. Then, the target impact component is extracted from the estimated signal, the characteristic frequency band is searched, and an impact energy index is constructed to accurately detect the inner and outer ring composite faults and prevent false alarms of other faults.

[0086] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0087] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0088] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0089] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0090] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0091] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0092] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0093] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices, and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0094] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0095] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0096] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A bearing composite fault diagnosis method, characterized in that: The following steps are involved: Obtain bearing composite fault vibration observation signal; The observed signal is subjected to short-time Fourier transform and cepstrum threshold processing, and two-dimensional time-frequency masking blind source separation. The obtained time-frequency domain separation signal is subjected to inverse short-time Fourier transform and bandpass filtering to obtain an independent time domain estimation signal. Perform Hilbert envelope demodulation and Fourier transform on the estimated signal to obtain the envelope spectrum of the filtered signal; Search and obtain the characteristic frequency bands of each order of the target fault, and construct a harmonic energy impact index , and calculate the shock pulse value; The calculated shock pulse value is compared with the threshold value to diagnose whether the bearing has a target type fault.

2. The bearing composite fault diagnosis method according to claim 1, characterized in that: The short-time Fourier transform and cepstrum threshold processing of the observed signal are as follows: In the time-frequency domain, the observed signal The absolute value of , determine the cepstrum of the time-frequency logarithmic spectrum of the observed signal ; The inverse spectrum threshold is obtained by introducing the Fourier threshold ; Determine the logarithmic amplitude spectrum.

3. The bearing composite fault diagnosis method according to claim 2, characterized in that: Inverse Short-Time Fourier Transform for: in, t For time, f is the frequency, for, is the inverse frequency index, n is the signal channel index, For the n The observed signal of the channel.

4. The bearing composite fault diagnosis method according to claim 2, characterized in that: Cepstrum of the time-frequency logarithmic spectrum of the observed signal for: in, is the Fourier transform in the frequency direction, is the frequency scale.

5. The bearing composite fault diagnosis method according to claim 1, characterized in that: The specific two-dimensional time-frequency masking blind source separation is: For the form The signal time-frequency domain convolution mixing process, and its unmixing process is , for dimensional mixing matrix, is the source signal, is the separation matrix to be solved, To estimate the signal; by solving the unmixing matrix Restore the source signal and use the mask to perform time-frequency shielding on the group threshold operator to achieve blind source separation of the observed signal.

6. The bearing composite fault diagnosis method according to claim 5, characterized in that: Solving the Mixed Matrix The objective function used is: in, is the number of observation signal channels, is the frequency scale of the Fourier transform, is the loss function.

7. The bearing composite fault diagnosis method according to claim 5, characterized in that: The group threshold operator is as follows: in, is an input dependent A nonnegative scalar.

8. The bearing composite fault diagnosis method according to claim 1, characterized in that: Shock pulse value for: in, is the shaft speed, It is the harmonic energy impact index. is the bearing inner diameter.

9. The bearing composite fault diagnosis method according to claim 8, characterized in that: Harmonic energy impact index for: in, For the i The characteristic frequency band.

10. A bearing composite fault diagnosis system, characterized in that: include: Signal module, to obtain vibration observation signal of bearing composite fault; The processing module performs short-time Fourier transform and cepstrum threshold processing on the observed signal, and two-dimensional time-frequency masking blind source separation, and performs inverse short-time Fourier transform and bandpass filtering on the obtained time-frequency domain separation signal to obtain an independent time domain estimation signal; A transform module performs Hilbert envelope demodulation and Fourier transform on the estimated signal to obtain the envelope spectrum of the filtered signal; The construction module searches for characteristic frequency bands of each order of the target fault and constructs a harmonic energy impact index. , and calculate the shock pulse value; The judgment module compares the calculated impact pulse value with the threshold value to diagnose whether the bearing has a target type fault.

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