Bearing fault detection method, device and electronic equipment

Abstract

The application provides a bearing fault detection method and device and electronic equipment. The method comprises: obtaining a vibration signal of a bearing to be detected; transforming the vibration signal to a support domain of a normalized Fourier spectrum; segmenting the Fourier spectrum of the support domain according to a preset iterative forward-backward search algorithm to obtain a plurality of continuous frequency segment ranges; determining a mode component corresponding to a maximum fault characteristic energy index from the plurality of continuous frequency segment ranges as an optimal mode component sensitive to a fault characteristic according to a preset maximum fault characteristic energy index strategy; performing envelope demodulation analysis on the optimal mode component to obtain a detection result of the bearing to be detected, wherein when an envelope demodulation spectrum obtained by envelope demodulation exists, a detection result indicating that the bearing to be detected has a fault is obtained. The application is beneficial to improving the accuracy and reliability of bearing fault detection.

Description

Technical Field

[0001] This invention relates to the field of equipment fault detection technology, and more specifically, to a bearing fault detection method, apparatus, and electronic equipment. Background Technology

[0002] Bearings are one of the key basic components of rotating machinery. Bearing condition monitoring and fault diagnosis are crucial for the safe and reliable operation of rotating machinery. Bearing vibration signals are highly susceptible to noise interference from harsh operating conditions, sensor testing, and electromagnetic fields, exhibiting strong background noise interference. Wavelet transform theory, as one method for extracting fault features in rotating machinery, is widely used in the early fault feature extraction of rolling bearings. The essence of wavelet analysis is to optimize and select the wavelet basis function most similar to the fault feature waveform. However, once the wavelet basis function is determined, it remains fixed and unchanging, thus lacking adaptability to signal decomposition and making it difficult to extract effective signal feature components. Therefore, the accuracy of bearing fault detection needs to be improved. Summary of the Invention

[0003] In view of this, the purpose of the embodiments of this application is to provide a bearing fault detection method, device and electronic device that can improve the accuracy and reliability of bearing fault detection.

[0004] To achieve the above technical objectives, the technical solution adopted in this application is as follows:

[0005] In a first aspect, embodiments of this application provide a bearing fault detection method, the method comprising:

[0006] Acquire the vibration signal of the bearing under test;

[0007] Transform the vibration signal into the support domain of a normalized Fourier spectrum;

[0008] The support domain is divided into Fourier spectra according to a preset iterative forward-backward search algorithm to obtain multiple continuous frequency segment ranges.

[0009] Based on the preset fault feature energy index maximization strategy, the mode component corresponding to the maximum fault feature energy index is determined from the multiple consecutive frequency segment ranges, and is used as the optimal mode component sensitive to fault features.

[0010] Envelope demodulation analysis is performed on the optimal mode components to obtain the detection result of the bearing under test. When the envelope demodulation spectrum obtained by envelope demodulation contains frequency and overtone peak spectral lines that characterize the fault, the detection result indicating that the bearing under test has a fault is obtained.

[0011] In conjunction with the first aspect, in some alternative embodiments, transforming the vibration signal to the support domain of a normalized Fourier spectrum includes:

[0012] The vibration signal is transformed to a normalized Fourier spectrum [0,π] as the initial support domain.

[0013] In conjunction with the first aspect, in some optional implementations, the support domain is segmented into Fourier spectra according to a preset iterative forward-backward search algorithm to obtain multiple continuous frequency segment ranges, including:

[0014] The initial support region [0,π] is divided into and in, It is obtained through the fault characteristic energy index maximization strategy;

[0015] by Starting from the Fourier frequency domain interval and Perform successive forward iterations and successive backward iterations respectively. The first forward iteration includes: Two into and in, The Fourier frequency band is obtained through the fault characteristic energy index maximization strategy. The corresponding candidate empirical wavelet frequency response function ψ n (ω) is represented as:

[0016]

[0017]

[0018] Where ω represents frequency; fcf represents the fault characteristic frequency; fs represents the sampling frequency. This refers to the optimal cutoff frequency boundary value. We obtain it from the following formula:

[0019]

[0020]

[0021] Among them, FCE refers to the Fault Characteristic Energy Index. Refers to the empirical wavelet transform coefficients; Q represents the number of Fourier spectrum segments;

[0022] The first backward iteration includes: Two into and It is obtained through the fault characteristic energy index maximization strategy;

[0023] Through the first forward iteration search and the first backward iteration search, the Fourier spectral support [0,π] is divided into four consecutive frequency domain supports.

[0024] Through the nth forward and backward iterations, the Fourier spectrum support [0,π] is segmented into...

[0025] To obtain the multiple consecutive frequency segment ranges.

[0026] In conjunction with the first aspect, in some optional implementations, based on a preset fault characteristic energy index maximization strategy, the mode component corresponding to the maximum fault characteristic energy index is determined from the multiple consecutive frequency segment ranges as the optimal mode component sensitive to fault characteristics, including:

[0027] Based on the frequency response function and decomposition formula in the fault characteristic energy index maximization strategy, empirical wavelet transform decomposition is performed on the multiple continuous frequency segment ranges to obtain the mode components of the empirical wavelet transform decomposition, wherein the frequency response function includes an empirical scaling function and an empirical wavelet function.

[0028] The empirical scaling function is expressed as:

[0029]

[0030] The empirical wavelet function is expressed as:

[0031]

[0032] Where, φ n (ω) refers to the empirical scaling function of the empirical wavelet transform; ψ n (ω) refers to the empirical wavelet function; ω represents the frequency; ω n Represents the frequency boundary value; β represents the value used to construct the scaling function φ. n (ω) and empirical wavelet function ψ n The polynomial function β(x) of ω, where x represents the independent variable of the function β(x); β(x) and the constant γ satisfy the following conditions:

[0033] β(x)=x 4 (35-84x+70x 2 -20x 3 )

[0034]

[0035] The decomposition formula is expressed as follows:

[0036]

[0037]

[0038] in, The detail coefficients in the mode components. The approximation coefficients in the model components; τ represents the integral variable; ε refers to a constant; F -1 This is the inverse Fourier transform operator;

[0039] Among the obtained mode components, the mode component corresponding to the maximum fault characteristic energy index is determined as the optimal mode component.

[0040] In conjunction with the first aspect, in some optional implementations, the formula for calculating the Fault Characteristic Energy Index (FCE) is expressed as follows:

[0041]

[0042]

[0043]

[0044] Where x(t) refers to the input real signal; the Hilbert operator is used to calculate the imaginary part of the real signal; the FFT operator represents the Fast Fourier Transform; and N represents the sequence length of the signal x(t); N c The highest order of the fault characteristic frequency harmonic is represented by Δ, and Δ represents the permissible frequency error between the search frequency and the higher order harmonic.

[0045] In conjunction with the first aspect, in some optional implementations, envelope demodulation analysis is performed on the optimal mode components to obtain the detection results of the bearing under test, including:

[0046] Envelope demodulation analysis is performed on the optimal mode components to obtain the envelope demodulation spectrum;

[0047] Determine whether the obtained envelope demodulation spectrum contains frequency and overtone peak spectral lines that characterize fault features;

[0048] When characteristic fault frequencies and octave peak spectral lines are present, the detection results indicating that the bearing under test has a fault are obtained.

[0049] Secondly, this application also provides a bearing fault detection device, the device comprising:

[0050] The acquisition unit is used to acquire the vibration signal of the bearing under test;

[0051] A transformation unit is used to transform the vibration signal to the support domain of a normalized Fourier spectrum.

[0052] The segmentation unit is used to segment the support domain into Fourier spectra according to a preset iterative forward-backward search algorithm to obtain multiple continuous frequency segment ranges.

[0053] The determining unit is used to determine the mode component corresponding to the maximum fault feature energy index from the multiple consecutive frequency segment ranges according to a preset fault feature energy index maximization strategy, as the optimal mode component sensitive to fault features.

[0054] The detection unit is used to perform envelope demodulation analysis on the optimal mode components to obtain the detection result of the bearing under test. When the envelope demodulation spectrum obtained by envelope demodulation contains frequency and overtone peak spectral lines that characterize the fault characteristics, the detection result characterizing the bearing under test has a fault is obtained.

[0055] In conjunction with the second aspect, in some optional embodiments, the transformation unit is further configured to:

[0056] The vibration signal is transformed to a normalized Fourier spectrum [0,π] as the initial support domain.

[0057] In conjunction with the second aspect, in some optional embodiments, the segmentation unit is further configured to:

[0058] The initial support region [0,π] is divided into and in, It is obtained through the fault characteristic energy index maximization strategy;

[0059] by Starting from the Fourier frequency domain interval and Perform successive forward iterations and successive backward iterations respectively. The first forward iteration includes: Two into and in, The Fourier frequency band is obtained through the fault characteristic energy index maximization strategy. The corresponding candidate empirical wavelet frequency response function ψ n (ω) is represented as:

[0060]

[0061]

[0062] Where ω represents frequency; fcf represents the fault characteristic frequency; fs represents the sampling frequency. This refers to the optimal cutoff frequency boundary value. We obtain it from the following formula:

[0063]

[0064]

[0065] Among them, FCE refers to the Fault Characteristic Energy Index. Refers to the empirical wavelet transform coefficients; Q represents the number of Fourier spectrum segments;

[0066] The first backward iteration includes: Two into and It is obtained through the fault characteristic energy index maximization strategy;

[0067] Through the first forward iteration search and the first backward iteration search, the Fourier spectral support [0,π] is divided into four consecutive frequency domain supports.

[0068] Through the nth forward and backward iterations, the Fourier spectrum support [0,π] is segmented into...

[0069] To obtain the multiple consecutive frequency segment ranges.

[0070] Thirdly, this application also provides an electronic device, which includes a processor and a memory coupled to each other, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the electronic device performs the above-described method.

[0071] The invention employing the above technical solution has the following advantages:

[0072] In the technical solution provided in this application, the support domain is segmented by a preset iterative forward-backward search algorithm to achieve adaptive selection of the empirical wavelet transform cutoff frequency boundary value; then, the preset fault feature energy index maximization strategy is used to extract the mode components sensitive to bearing fault features. This is beneficial for accurately and effectively identifying bearing fault features, thereby improving the accuracy and reliability of fault detection. Attached Figure Description

[0073] This application can be further illustrated by the non-limiting embodiments given in the accompanying drawings. It should be understood that the following drawings only illustrate some embodiments of this application and should not be considered as limiting the scope. For those skilled in the art, other related drawings can be obtained from these drawings without any inventive effort.

[0074] Figure 1 This is a schematic flowchart of the bearing fault detection method provided in the embodiments of this application.

[0075] Figure 2 A schematic diagram of Fourier spectrum segmentation of empirical wavelet transform provided in the embodiments of this application.

[0076] Figure 3 This is a schematic diagram of the time-domain waveform of the simulated signal provided in an embodiment of this application.

[0077] Figure 4 This is a schematic diagram of the direct Hilbert envelope demodulation spectrum of the simulated signal provided in the embodiments of this application.

[0078] Figure 5 A bar chart of the energy index of mode component fault characteristics provided in the embodiments of this application.

[0079] Figure 6 for Figure 5 A schematic diagram of the fourth characteristic mode component and the Hilbert envelope demodulation spectrum. Detailed Implementation

[0080] The present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that similar or identical parts are referred to by the same reference numerals in the drawings or description. Implementations not shown or described in the drawings are forms known to those skilled in the art. In the description of this application, terms such as "first" and "second" are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0081] First Embodiment

[0082] This application provides an electronic device. The electronic device may include a processing module and a storage module. The storage module stores a computer program, which, when executed by the processing module, enables the electronic device to perform the corresponding steps in the bearing fault detection method described below.

[0083] Among them, electronic devices can be, but are not limited to, personal computers, servers, and other devices.

[0084] In this embodiment, the processing module can be an integrated circuit chip with signal processing capabilities. The processing module can be a general-purpose processor. For example, the processor can be a Central Processing Unit (CPU), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application.

[0085] The storage module can be, but is not limited to, random access memory, read-only memory, programmable read-only memory, erasable programmable read-only memory, electrically erasable programmable read-only memory, etc. In this embodiment, the storage module can be used to store iterative forward-backward search algorithms, fault characteristic energy index maximization strategies, vibration signals, and detection results. Of course, the storage module can also be used to store programs, which the processing module executes after receiving execution instructions.

[0086] Understandably, electronic devices can utilize the following shaft-side fault detection method to analyze and process the vibration signals of the bearing under test, thereby achieving fault detection of the bearing. The bearing under test can be, but is not limited to, rolling bearings and sliding bearings. Bearing faults are generally localized, such as spalling, pitting, and cracks. Based on the fault location, the detected bearing fault type can be further subdivided into outer ring faults, inner ring faults, and rolling element faults.

[0087] Please refer to Figure 1 This application also provides a bearing fault detection method, which can be applied to the aforementioned electronic device, and the electronic device executes or implements the steps of the method. The bearing fault detection method may include the following steps:

[0088] Step 110: Obtain the vibration signal of the bearing under test;

[0089] Step 120: Transform the vibration signal to the support domain of the normalized Fourier spectrum;

[0090] Step 130: The support domain is divided into Fourier spectra according to a preset iterative forward-backward search algorithm to obtain multiple continuous frequency segment ranges.

[0091] Step 140: Based on the preset fault feature energy index maximization strategy, determine the mode component corresponding to the maximum fault feature energy index from the multiple consecutive frequency segment ranges, as the optimal mode component sensitive to fault features.

[0092] Step 150: Perform envelope demodulation analysis on the optimal mode components to obtain the detection result of the bearing under test. When the envelope demodulation spectrum obtained by envelope demodulation contains frequency and overtone peak spectral lines that characterize the fault, the detection result characterizing the bearing under test has a fault is obtained.

[0093] In this embodiment, the bearing fault detection method is implemented based on the Adaptive Empirical Wavelet Transform (AEWT) algorithm. The principle of the AEWT algorithm is described in detail in steps 130 to 150 below. The steps of the bearing fault detection method will be elaborated in detail below:

[0094] In step 110, the electronic device can detect the vibration signal of the bearing under test during operation using corresponding sensors. The method for acquiring the vibration signal is conventional and can be flexibly determined according to the actual situation; no specific limitations are imposed here.

[0095] In this embodiment, step 120 may include: transforming the vibration signal to a normalized Fourier spectrum [0,π] as an initial support domain.

[0096] Understandably, in step 120, the vibration signal can be transformed to [0, π] using an empirical wavelet transform algorithm. The implementation principle of the empirical wavelet transform is as follows:

[0097] Using the normalized frequency range of [0, 2π], the Fourier spectrum is limited to [0, π] according to the Shannon criterion; assuming the continuous Fourier spectrum is divided into N frequency intervals corresponding to signal mode components, it contains N+1 frequency boundary values ​​to be determined, where ω0 = 0, ω n =π. An example of Fourier spectrum segmentation using empirical wavelet transform, such as... Figure 2 As shown.

[0098] The frequency domain support of any mode component can be represented as Λ n =[ω n-1 ,ω n Assume that the initial Fourier support [0, π] can be divided into N consecutive Fourier supports Λ. n =[ω n-1 ,ω n The union of ], i.e. To ensure that the empirical wavelet supports Λ in each segment of the frequency domainn All of the above can be physically implemented, defined by the frequency boundary value ω. n The transition zone centered on the center, the width of the transition zone τ n With frequency boundary value ω n Proportional, i.e., τ n =γω n .

[0099] In this embodiment, the electronic device pre-stores an iterative forward-backward search algorithm and a fault feature energy index maximization strategy. The fault feature energy index maximization strategy includes a frequency response function and a decomposition formula. In this embodiment, the empirical scaling function φ of the empirical wavelet transform can be obtained based on the construction principles of Littlewood-Paley wavelets and Meyer's wavelets. n (ω) and empirical wavelet function ψ n The definition of (ω) is as follows:

[0100] The empirical scaling function is expressed as:

[0101]

[0102] The empirical wavelet function is expressed as:

[0103]

[0104] Where, φ n (ω) refers to the empirical scaling function of the empirical wavelet transform; ψ n (ω) refers to the empirical wavelet function; ω represents the frequency; ω n Represents the frequency boundary value; β represents the value used to construct the scaling function φ. n (ω) and empirical wavelet function ψ n The polynomial function β(x) of ω, where x represents the independent variable of the function β(x); β(x) and the constant γ satisfy the following conditions:

[0105] β(x)=x 4 (35-84x+70x 2 -20x 3 )

[0106]

[0107] Formula (3) guarantees that the set composed of the empirical scaling function and the empirical wavelet function can be used. For L 2 (R) is a tightly supported frame.

[0108] In this embodiment, f(t) is assumed to be the vibration signal being analyzed, and f(t) represents the detail coefficients of the empirical wavelet transform. and approximation coefficients It can be obtained by the following inner product operation, that is, the decomposition formula is expressed as:

[0109]

[0110] in, The detail coefficients in the mode components. Approximation coefficients in the mode components; The coefficients of the empirical wavelet transform are represented as a whole; τ represents the integral variable; ε is a constant, which is well known to those skilled in the art. and ψ n Fourier transforms of φ(t) and φ1(t); and ψ n The complex conjugate function of φ(t) and φ1(t); F -1 It is the inverse Fourier transform operator.

[0111] After decomposition, the vibration signal can be reconstructed using empirical wavelet transform. The reconstruction formula is as follows:

[0112]

[0113] Understandably, in formula (4), f(t) refers to the vibration signal of the bearing being analyzed; while in formula (5), f(t) refers to the signal reconstructed by the empirical wavelet transform coefficients. Formulas (4) and (5) are inverse processes of each other, representing empirical wavelet transform (to achieve signal decomposition) and inverse empirical wavelet transform (to achieve signal reconstruction), respectively.

[0114] In this embodiment, the electronic device may pre-store a calculation method for detecting the Fault Characteristic Energy (FCE) index. The FCE is calculated as follows:

[0115] Suppose that in the vibration signal, the instantaneous amplitude and envelope spectrum of the real signal x(t) are A(t) and ES(f), respectively. Using the Hilbert transform and FFT (Fast Fourier Transform) algorithm, the fault characteristic energy index can be defined from the perspective of the Hilbert envelope demodulation spectrum as follows:

[0116]

[0117] Formula (6) is the formula for calculating the fault characteristic energy index (FCE). In the formula, t represents time, x(t) refers to the input real signal; the Hilbert operator is used to calculate the imaginary part corresponding to the real signal; the FFT operator represents the Fast Fourier Transform; N represents the sequence length of the signal x(t). fcf and fs represent the fault characteristic frequency and sampling frequency, respectively, and N... c Δf represents the highest order of the harmonics of the fault characteristic frequency, and Δf represents the permissible frequency error between the search frequency and the higher-order harmonics. The higher the energy proportion of the fault characteristic frequency fcf and the harmonics of the vibration signal in the envelope demodulation spectrum, that is, the more obvious the peaks at the fault characteristic frequency and its harmonics, the larger the value of the fault characteristic energy index FCE of the characteristic mode component.

[0118] Understandably, bearing fault characteristics generally refer to repetitive or periodic impact features, which are often easily obscured by noise or harmonic interference, making direct extraction / detection impossible. This embodiment employs an adaptive empirical wavelet transform algorithm for filtering and a Hilbert envelope demodulation method, and detects bearing fault characteristics by detecting the bearing fault feature frequencies and their octave lines in the envelope spectrum.

[0119] In step 130, a coarse segmentation of the Fourier spectrum is first performed, followed by a precise segmentation based on the Fourier spectrum obtained from the iterative forward-backward search algorithm. The coarse segmentation refers to dividing the normalized Fourier spectrum into two parts along the [0, π] axis. and Achieve a coarse segmentation of the Fourier spectrum. From the normalized discrete candidate cutoff frequency boundary value ω c The values ​​∈{π / Q, 2π / Q, …, (Q-1)π / Q} are obtained through optimization and selection. The optimization and selection method utilizes a strategy to maximize the energy index of fault characteristics.

[0120] It should be noted that ω c Let Q represent the discrete candidate cutoff frequency boundary value, and let Q represent the number of Fourier spectrum segments. The value of Q determines the fineness of the Fourier spectrum segmentation. The formula for determining the value of Q is as follows:

[0121]

[0122] In this embodiment, the highest order of the fault characteristic frequency in the envelope demodulation spectrum is considered to be 3, that is, N in formula (6) c If the value can be 3, then the spectral width corresponding to the mode component during Fourier spectrum segmentation should cover at least 6 times the frequency domain interval of fcf. Therefore, the discrete candidate cutoff frequency boundary value ω c The maximum value can be reduced to:

[0123] ωcmax =π - 2π × 6fcf / fs (8)

[0124] Accordingly, the set of discretized candidate cutoff frequency boundary values ​​can be reduced to {π / Q, 2π / Q, ..., ω}. cmax This reduces the number of searches and improves search efficiency. Fourier band [ω] c The candidate empirical wavelet frequency response function ψ corresponding to π] n (ω) is represented as follows:

[0125]

[0126] in, fcf represents the fault characteristic frequency; fs represents the sampling frequency. Based on the input signal x(t) and the candidate empirical wavelet function ψ... n The inner product transformation of (ω) yields [ω] c Empirical wavelet transform coefficients on the interval [π] Combining the fault characteristic energy index (FCE) maximization strategy, the optimal cutoff frequency boundary value is determined. We obtain it from the following formula:

[0127]

[0128] In this embodiment, step 130 may include:

[0129] The initial support region [0,π] is divided into and in, It is obtained through the fault characteristic energy index maximization strategy;

[0130] by Starting from the Fourier frequency domain interval and Perform successive forward iterations and successive backward iterations respectively.

[0131] The first forward iteration includes: Two into and in, The Fourier frequency band is obtained through the fault characteristic energy index maximization strategy. The corresponding candidate empirical wavelet frequency response function ψ n (ω) is represented as:

[0132]

[0133] Where ω represents frequency; fcf represents the fault characteristic frequency; fs represents the sampling frequency; based on the input signal x(t) and the candidate empirical wavelet function ψn The inner product transformation of (ω) yields Empirical wavelet transform coefficients on the interval This refers to the optimal cutoff frequency boundary value. We obtain it from the following formula:

[0134]

[0135] That is, by using formula (12) in combination with the fault characteristic energy index maximization strategy, we can obtain the normalized discrete candidate cutoff frequency boundary value set. The best option is obtained from the middle selection.

[0136] Among them, FCE refers to the Fault Characteristic Energy Index. The empirical wavelet transform coefficients are referred to as "Q". Q represents the number of Fourier spectrum segments.

[0137] The process of the first backward iteration is similar to the segmentation optimization strategy of the first forward iteration.

[0138] For example, the first backward iteration includes: Two into and Based on the fault characteristic energy index maximization strategy, from the normalized discrete candidate cutoff frequency boundary value set... Optimized Fourier band The corresponding candidate empirical wavelet frequency response function ψ n (ω) is represented as follows:

[0139]

[0140] in, Based on the input signal x(t) and the candidate empirical wavelet function ψ n The inner product transformation of (ω) yields Empirical wavelet transform coefficients on the interval Combining the fault characteristic energy index maximization strategy, the optimal cutoff frequency boundary value We obtain it from the following formula:

[0141]

[0142] The above process is based on the starting point. The first forward and backward searches are completed. Through the first forward and backward iterations, the Fourier spectral support [0,π] can be divided into four consecutive frequency domain supports.

[0143] Then, perform the second forward and backward iteration search, and so on up to the nth forward and backward iteration search.

[0144] For example, for the second forward and backward iterative search, frequency domain support can be used. Adaptive segmentation and Optimal cutoff frequency boundary value The optimization process is similar to the first forward search, the only difference being that in formula (11) Replace with The second backward iteration, based on the fault characteristic energy index maximization strategy, can support frequency domain... Adaptive segmentation and Optimal cutoff frequency boundary value The optimization process is similar to the first backward search, the only difference being that in formula (13) Replace with As a result, the second forward and backward search can adaptively divide the entire Fourier frequency domain support [0,π] into six continuous frequency domain supports.

[0145] Based on the above idea, n forward and backward searches are performed, i.e., an iterative forward-backward search algorithm. According to the fault characteristic energy index maximization strategy, the cutoff frequency boundary values ​​of the empirical wavelet transform filter bank are adaptively optimized. The final result of the filter bank cutoff frequency values ​​is as follows: This yields multiple consecutive frequency segment ranges, where n is a positive integer and can be flexibly determined according to actual conditions. The final result achieves fine segmentation of the Fourier spectrum.

[0146] In this embodiment, the principle of the fault feature energy index maximization strategy can be found in the corresponding description of formula (6) above. Understandably, the essence of bearing fault diagnosis is the extraction of repetitive impact features. When a bearing experiences a local fault, the bearing's vibration signal will contain repetitive impact features. Correspondingly, obvious bearing fault feature frequencies and octave peaks of the vibration signal will appear in the Hilbert envelope spectrum of the vibration signal. Furthermore, according to formula (6), the more obvious the repetitive impact features of the mode component, the higher the energy proportion of the fault feature frequency and the octave of the vibration signal in the Hilbert envelope spectrum of the mode component, and the larger the value of the fault feature energy index corresponding to the mode component.

[0147] In this embodiment, step 140 may include:

[0148] Based on the frequency response function and decomposition formula in the fault characteristic energy index maximization strategy, empirical wavelet transform decomposition is performed on the multiple continuous frequency segment ranges to obtain the mode components of the empirical wavelet transform decomposition, wherein the frequency response function includes an empirical scaling function and an empirical wavelet function.

[0149] Among the obtained mode components, the mode component corresponding to the maximum fault characteristic energy index is determined as the optimal mode component.

[0150] Understandably, electronic devices can utilize the frequency response function and formula (4) of the created empirical wavelet filter bank to achieve empirical wavelet transform decomposition, obtaining the detail coefficients and approximation coefficients (i.e., mode components) of the empirical wavelet transform decomposition. The characteristic mode component corresponding to the largest fault characteristic energy index is the optimal mode component sensitive to fault characteristics.

[0151] In this embodiment, the mode component refers to the decomposition detail coefficients obtained after analyzing the bearing vibration signal using the adaptive empirical wavelet transform algorithm. With approximation coefficient The calculation method is shown in formula (4) above, and will not be repeated here.

[0152] In this embodiment, step 150 may include:

[0153] Envelope demodulation analysis is performed on the optimal mode components to obtain the envelope demodulation spectrum;

[0154] Determine whether the obtained envelope demodulation spectrum contains frequency and overtone peak spectral lines that characterize fault features;

[0155] When characteristic fault frequencies and octave peak spectral lines are present, the detection results indicating that the bearing under test has a fault are obtained.

[0156] Understandably, in this embodiment, the electronic device can perform local bearing fault diagnosis based on whether the bearing fault characteristic frequency and its octave peak spectral line appear in the Hilbert envelope demodulation spectrum of the optimal mode component. More specifically, if the bearing outer ring (inner ring, rolling element) fault characteristic frequency and the octave peak spectral line of the vibration signal appear, then it is determined that the bearing outer ring (inner ring, rolling element) is faulty; otherwise, it is diagnosed as a normal bearing with no fault.

[0157] It should be noted that when bearing failure occurs at different locations (such as the outer ring, inner ring, and rolling elements), the theoretical characteristic frequencies of local failures of the bearing (outer ring, inner ring, and rolling elements) can be calculated based on the bearing size parameters, kinematic relationships, and rotational speed.

[0158] Understandably, the principle of the adaptive empirical wavelet transform algorithm proposed by the inventors can be found in the detailed description of steps 130 to 150. To verify the effectiveness of the proposed adaptive empirical wavelet transform algorithm, the inventors constructed a rolling bearing fault simulation signal h(t) containing strong Gaussian white noise and harmonic interference for simulation testing. The formula for the simulation signal is as follows:

[0159]

[0160] The first fault impact occurred at time T0 = 0.002s, T m Let u(t) be the time of the m-th fault impact, a unit step function. The displacement constant y0 = 1, the damping coefficient ξ = 0.05, and the natural frequency f. n =3000Hz, fault period T f =0.01s, then the fault characteristic frequency of the simulated signal fcf = 100Hz, and the amplitude modulation frequency f c =12Hz, harmonic frequency f1=85Hz, sampling frequency is 20480Hz, number of sampling points is 20480, R noise This represents Gaussian white noise. The signal-to-noise ratio (SNR) of the simulated signal is -15 dB. The time-domain waveform of the simulated signal is shown below. Figure 3 As shown.

[0161] The results of directly performing Hilbert envelope demodulation analysis on the above simulated signals are as follows: Figure 4 As shown. According to Figure 4 The direct Hilbert envelope demodulation spectrum analysis results only clearly show the amplitude modulation frequency f of the harmonic interference signal. c (12Hz) However, the fault characteristic frequency information is submerged by strong noise and interference components, making it impossible to observe the fault characteristic frequency fcf and the harmonics of the vibration signal. Therefore, direct Hilbert envelope spectrum demodulation analysis cannot identify the periodic impact characteristics of rolling bearing faults.

[0162] In this embodiment, the proposed adaptive empirical wavelet transform algorithm is used to analyze the simulated signal h(t). Based on the iterative forward-backward search algorithm guided by maximizing the fault characteristic energy index, the cutoff frequency boundary values ​​of the empirical wavelet transform filter bank and the corresponding frequency response function of the empirical wavelet filter bank are adaptively selected. The simulated signal is analyzed using the adaptive empirical wavelet transform to obtain the fault characteristic energy index of each mode component, such as... Figure 5 As shown.

[0163] In this embodiment, an empirical wavelet filter bank constructed based on a fault feature energy index maximization strategy adaptively extracts signal mode components sensitive to fault impact characteristics. Figure 5It can be seen that the fault characteristic energy index of the fourth characteristic mode component is the largest. Therefore, Hilbert envelope demodulation analysis is performed on the fourth characteristic mode component, and the results are as follows. Figure 6 As shown. Figure 6 A clear impact characteristic can be observed in the time-domain waveform of (a). Figure 6 In the envelope demodulation spectrum of (b), the fault characteristic frequency fcf (100Hz) and its harmonics can be clearly observed. Therefore, the above results demonstrate that the above method can effectively extract weak impulse characteristics under strong noise interference.

[0164] Based on the above design, a novel parameter index—the fault feature energy index—is proposed to quantitatively evaluate the energy significance of fault feature frequencies and their harmonics in the envelope spectrum. Based on this, an iterative forward-backward search algorithm guided by a fault feature energy index maximization strategy is constructed, achieving adaptive and accurate segmentation of the empirical wavelet transform Fourier spectrum, providing a solid guarantee for the extraction of optimal feature mode components. The proposed adaptive empirical wavelet transform algorithm provided in this embodiment can effectively extract the impact feature mode components of the simulated signal, successfully identifying periodic impact features, and exhibiting good fault feature extraction and identification effects. Analysis results of simulation experiments and measured rolling bearing vibration data show that the proposed adaptive empirical wavelet transform algorithm can adaptively optimize the cutoff frequency boundary values ​​of the empirical wavelet filter bank and adaptively extract the optimal mode components sensitive to fault impact features. Furthermore, the envelope demodulation spectrum of the optimal mode components can accurately detect fault feature frequencies and their harmonics. Therefore, the proposed adaptive empirical wavelet transform algorithm can achieve early weak impact feature extraction of rolling bearings under strong background noise interference and has strong noise resistance robustness.

[0165] Second Embodiment

[0166] This application also provides a bearing fault detection device, which includes at least one software function module that can be stored in a storage module or embedded in the operating system (OS) of an electronic device in the form of software or firmware. The processing module is used to execute the executable module stored in the storage module, such as the software function module and computer program included in the bearing fault detection device.

[0167] The bearing fault detection device includes an acquisition unit, a transformation unit, a segmentation unit, a determination unit, and a detection unit. The functions of each unit are as follows:

[0168] The acquisition unit is used to acquire the vibration signal of the bearing under test;

[0169] A transformation unit is used to transform the vibration signal to the support domain of a normalized Fourier spectrum.

[0170] The segmentation unit is used to segment the support domain into Fourier spectra according to a preset iterative forward-backward search algorithm to obtain multiple continuous frequency segment ranges.

[0171] The determining unit is used to determine the mode component corresponding to the maximum fault feature energy index from the multiple consecutive frequency segment ranges according to a preset fault feature energy index maximization strategy, as the optimal mode component sensitive to fault features.

[0172] The detection unit is used to perform envelope demodulation analysis on the optimal mode components to obtain the detection result of the bearing under test. When the envelope demodulation spectrum obtained by envelope demodulation contains frequency and overtone peak spectral lines that characterize the fault characteristics, the detection result characterizing the bearing under test has a fault is obtained.

[0173] Alternatively, the transformation unit can also be used for:

[0174] The vibration signal is transformed to a normalized Fourier spectrum [0,π] as the initial support domain.

[0175] Alternatively, the segmentation unit can also be used for:

[0176] The initial support region [0,π] is divided into and in, It is obtained through the fault characteristic energy index maximization strategy;

[0177] by Starting from the Fourier frequency domain interval and Perform successive forward iterations and successive backward iterations respectively. The first forward iteration includes: Two into and in, The Fourier frequency band is obtained through the fault characteristic energy index maximization strategy. The corresponding candidate empirical wavelet frequency response function ψ n (ω) is represented as:

[0178]

[0179]

[0180] Where ω represents frequency; fcf represents the fault characteristic frequency; fs represents the sampling frequency. This refers to the optimal cutoff frequency boundary value. We obtain it from the following formula:

[0181]

[0182]

[0183] Among them, FCE refers to the Fault Characteristic Energy Index. Refers to the empirical wavelet transform coefficients; Q represents the number of Fourier spectrum segments;

[0184] The first backward iteration includes: Two into and It is obtained through the fault characteristic energy index maximization strategy;

[0185] Through the first forward iteration search and the first backward iteration search, the Fourier spectral support [0,π] is divided into four consecutive frequency domain supports.

[0186] Through the nth forward and backward iterations, the Fourier spectrum support [0,π] is segmented into...

[0187] To obtain the multiple consecutive frequency segment ranges.

[0188] Alternatively, the determining unit can also be used for:

[0189] Based on the frequency response function and decomposition formula in the fault characteristic energy index maximization strategy, empirical wavelet transform decomposition is performed on the multiple continuous frequency segment ranges to obtain the mode components of the empirical wavelet transform decomposition, wherein the frequency response function includes an empirical scaling function and an empirical wavelet function.

[0190] The empirical scaling function is expressed as:

[0191]

[0192] The empirical wavelet function is expressed as:

[0193]

[0194] Where, φ n (ω) refers to the empirical scaling function of the empirical wavelet transform; ψ n (ω) refers to the empirical wavelet function; ω represents the frequency; ω n Represents the frequency boundary value; β represents the value used to construct the scaling function φ. n (ω) and empirical wavelet function ψ n The polynomial function β(x) of ω, where x represents the independent variable of the function β(x); β(x) and the constant γ satisfy the following conditions:

[0195] β(x)=x 4 (35-84x+70x 2 -20x 3 )

[0196] 0 < γ < 1 and

[0197] The decomposition formula is expressed as follows:

[0198]

[0199]

[0200] in, The detail coefficients in the mode components. The approximation coefficients in the model components; τ represents the integral variable; ε refers to a constant; F -1 This is the inverse Fourier transform operator;

[0201] Among the obtained mode components, the mode component corresponding to the maximum fault characteristic energy index is determined as the optimal mode component.

[0202] Optionally, the detection unit can also be used for:

[0203] Envelope demodulation analysis is performed on the optimal mode components to obtain the envelope demodulation spectrum;

[0204] Determine whether the obtained envelope demodulation spectrum contains frequency and overtone peak spectral lines that characterize fault features;

[0205] When characteristic fault frequencies and octave peak spectral lines are present, the detection results indicating that the bearing under test has a fault are obtained.

[0206] It should be noted that those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process of the electronic equipment and bearing fault detection device described above can be referred to the corresponding process of each step in the aforementioned method, and will not be elaborated further here.

[0207] Based on the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by hardware or by using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solution of this application can be embodied in the form of a software product. This software product can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, mobile hard drive, etc.) and includes several instructions to cause a computer device (such as a personal computer, electronic device, or network device, etc.) to execute the methods described in the various implementation scenarios of this application.

[0208] In summary, this application provides a bearing fault detection method, apparatus, and electronic device. In this solution, a preset iterative forward-backward search algorithm is used to segment the Fourier spectrum of the support domain, enabling adaptive selection of the empirical wavelet transform cutoff frequency boundary value. Furthermore, a preset fault feature energy index maximization strategy is used to extract mode components sensitive to bearing fault characteristics. This facilitates accurate and effective identification of bearing fault characteristics, thereby improving the accuracy and reliability of fault detection.

[0209] In the embodiments provided in this application, it should be understood that the disclosed apparatus, systems, and methods can also be implemented in other ways. The apparatus, systems, and methods embodiments described above are merely illustrative. For example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code, which includes one or more executable instructions for implementing a specified logical function. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions. Furthermore, the functional modules in the various embodiments of this application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0210] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A bearing fault detection method characterized by, The method includes: Acquire the vibration signal of the bearing under test; Transform the vibration signal into the support domain of a normalized Fourier spectrum; The support domain is divided into Fourier spectra according to a preset iterative forward-backward search algorithm to obtain multiple continuous frequency segment ranges. Based on the preset fault feature energy index maximization strategy, the mode component corresponding to the maximum fault feature energy index is determined from the multiple consecutive frequency segment ranges, and is used as the optimal mode component sensitive to fault features. Envelope demodulation analysis is performed on the optimal mode components to obtain the detection result of the bearing under test. When the envelope demodulation spectrum obtained by envelope demodulation contains frequency and overtone peak spectral lines that characterize the fault, the detection result indicating that the bearing under test has a fault is obtained. Transforming the vibration signal to the support domain of a normalized Fourier spectrum includes: The vibration signal is transformed to a normalized Fourier spectrum [0,π] as the initial support domain; The support domain is segmented into Fourier spectra using a preset iterative forward-backward search algorithm to obtain multiple continuous frequency segment ranges, including: The initial support domain [0, π] is divided into [0, ] and [ , π], wherein, is obtained by the failure feature energy index maximization strategy. Take as the starting point, in the Fourier frequency domain interval [0, ] and [ , π] respectively, forward iteration and backward iteration are carried out, wherein the first forward iteration includes: dividing [0, ] into [0, ] and [ , ], wherein, The maximum strategy is obtained by the fault feature energy index; the candidate empirical wavelet frequency response function ψ n ( ) corresponding to the Fourier frequency band [ , ] is represented as: ； in, Indicates frequency; fcf represents the fault characteristic frequency; fs represents the sampling frequency; This refers to the optimal cutoff frequency boundary value. We obtain it from the following formula: ； Among them, FCE refers to the Fault Characteristic Energy Index. Refers to the empirical wavelet transform coefficients; Q represents the number of Fourier spectrum segments; The first backward iteration includes: [ , π] is divided into two [ , ]and[ , π]; It is obtained through the fault characteristic energy index maximization strategy; Through the first forward iteration search and the first backward iteration search, the Fourier spectral support [0, π] is divided into four consecutive frequency domain supports [0, π]. ]、[ , ]、[ , ]、[ ,π]; Through the nth forward and backward iterative search, the Fourier spectrum support [0, π] is divided into [0, π]. ]、 [ , ]、[ , ]、[ , ]、[ , ]、[ , ]、[ , ]、 [ ,π], to obtain the multiple consecutive frequency segment ranges; The formula for calculating the Fault Characteristic Energy Index (FCE) is as follows: ； Where x(t) refers to the input real signal; the Hilbert operator is used to calculate the imaginary part of the real signal; the FFT operator represents the Fast Fourier Transform; and N represents the sequence length of the signal x(t). This represents the highest order of the harmonics of the fault characteristic frequency. This represents the permissible frequency error between the search frequency and higher harmonics.

2. The method according to claim 1, characterized in that, Based on a preset fault characteristic energy index maximization strategy, the mode component corresponding to the maximum fault characteristic energy index is determined from the multiple consecutive frequency segment ranges, serving as the optimal mode component sensitive to fault characteristics, including: Based on the frequency response function and decomposition formula in the fault characteristic energy index maximization strategy, empirical wavelet transform decomposition is performed on the multiple continuous frequency segment ranges to obtain the mode components of the empirical wavelet transform decomposition, wherein the frequency response function includes an empirical scaling function and an empirical wavelet function. The empirical scaling function is expressed as: ； The empirical wavelet function is expressed as: ； in, ( ) refers to the empirical scaling function of the empirical wavelet transform; ψ n ( (Refers to) empirical wavelet functions; Indicates frequency; Indicates frequency boundary values; Indicates the use of the scaling function ( ) and empirical wavelet function ψ n ( polynomial function x represents a function The independent variable; and constant The following conditions must be met: ； The decomposition formula is expressed as follows: ； in, The detail coefficients in the mode components. The approximation coefficients in the model components; τ represents the integral variable; Refers to a constant; F -1 This is the inverse Fourier transform operator; Among the obtained mode components, the mode component corresponding to the maximum fault characteristic energy index is determined as the optimal mode component.

3. The method according to claim 1, characterized in that, Envelope demodulation analysis is performed on the optimal mode components to obtain the detection results of the bearing under test, including: Envelope demodulation analysis is performed on the optimal mode components to obtain the envelope demodulation spectrum; Determine whether the obtained envelope demodulation spectrum contains frequency and overtone peak spectral lines that characterize fault features; When characteristic fault frequencies and octave peak spectral lines are present, the detection results indicating that the bearing under test has a fault are obtained.

4. A bearing fault detection device, characterized in that, The device includes: The acquisition unit is used to acquire the vibration signal of the bearing under test; A transformation unit is used to transform the vibration signal to the support domain of a normalized Fourier spectrum. The segmentation unit is used to segment the support domain into Fourier spectra according to a preset iterative forward-backward search algorithm to obtain multiple continuous frequency segment ranges. The determining unit is used to determine the mode component corresponding to the maximum fault feature energy index from the multiple consecutive frequency segment ranges according to a preset fault feature energy index maximization strategy, as the optimal mode component sensitive to fault features. The detection unit is used to perform envelope demodulation analysis on the optimal mode components to obtain the detection result of the bearing under test. When the envelope demodulation spectrum obtained by envelope demodulation contains frequency and overtone peak spectral lines that characterize the fault characteristics, the detection result characterizing the bearing under test has a fault is obtained. The transformation unit is further configured to: transform the vibration signal to a normalized Fourier spectrum [0,π] as an initial support domain; The segmentation unit is also used for: The initial support domain [0,π] is divided into [0,π]. ]and[ , π], where, It is obtained through the fault characteristic energy index maximization strategy; by Starting from [0, ...], in the Fourier frequency domain interval [0, ... ]and[ , π], respectively perform successive forward iterations and successive backward iterations, wherein the first forward iteration includes: setting [0, π] to π] Divide into [0, ]and[ , ],in, The Fourier frequency band is obtained through the fault characteristic energy index maximization strategy. , The corresponding candidate empirical wavelet frequency response function ψ n ( ) is represented as: ； in, Indicates frequency; fcf represents the fault characteristic frequency; fs represents the sampling frequency; This refers to the optimal cutoff frequency boundary value. We obtain it from the following formula: ； Among them, FCE refers to the Fault Characteristic Energy Index. Refers to the empirical wavelet transform coefficients; Q represents the number of Fourier spectrum segments; The first backward iteration includes: [ , π] is divided into two [ , ]and[ , π]; It is obtained through the fault characteristic energy index maximization strategy; Through the first forward iteration search and the first backward iteration search, the Fourier spectral support [0, π] is divided into four consecutive frequency domain supports [0, π]. ]、[ , ]、[ , ]、[ ,π]; Through the nth forward and backward iterative search, the Fourier spectrum support [0, π] is divided into [0, π]. ]、 [ , ]、[ , ]、[ , ]、[ , ]、[ , ]、[ , ]、 [ ,π], to obtain the multiple consecutive frequency segment ranges; The formula for calculating the Fault Characteristic Energy Index (FCE) is as follows: ； Where x(t) refers to the input real signal; the Hilbert operator is used to calculate the imaginary part of the real signal; the FFT operator represents the Fast Fourier Transform; and N represents the sequence length of the signal x(t). This represents the highest order of the harmonics of the fault characteristic frequency. This represents the permissible frequency error between the search frequency and higher harmonics.

5. An electronic device, characterized in that, The electronic device includes a processor and a memory coupled together, the memory storing a computer program that, when executed by the processor, causes the electronic device to perform the method as described in any one of claims 1 to 3.

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