Mechanical transient fault diagnosis method and equipment based on group delay ridge optimization

Abstract

The invention discloses a mechanical transient fault diagnosis method and equipment based on group delay ridge optimization, belongs to the technical field of intelligent diagnosis of mechanical faults, and aims to solve the problems that transient signal features are difficult to extract from vibration signals with complex components, are easily influenced by interference and are not easy to diagnose in the conventional vibration signal processing method. And the fault diagnosis accuracy is low. The method comprises the following steps: performing time-frequency transformation and phase modulation wavelet transformation on a vibration signal to obtain a corresponding time-frequency transformation result; extracting a group delay ridge line of a first mode in the vibration signal; performing group delay band filtering processing on the group delay ridge line to obtain an optimized group delay ridge line; eliminating the time-frequency coefficient corresponding to the optimized group delay ridge line, iteratively extracting a group delay ridge line of a second mode, and carrying out ridge line optimization on the searched group delay ridge lines until the group delay ridge line of each mode is obtained; and obtaining an optimized time-frequency transformation result according to the group delay ridge line of each mode, and carrying out mechanical transient fault diagnosis.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent diagnosis of mechanical faults, and in particular to a mechanical transient fault diagnosis method and device based on group delay ridge optimization. Background Art

[0002] Mechanical signal analysis technology is widely used in aerospace, vehicles, manufacturing and other fields. Since industrial equipment is often in heavy load, fatigue and other complex working conditions for a long time, its core components and key structures will inevitably be damaged to varying degrees. When the rotating parts in the mechanical equipment fail, they are often presented in the form of transient impact in the monitored vibration signal, such as local wear, rotor misalignment, surface corrosion and other problems. By processing and analyzing such signals, it is helpful to find signs of failure in advance and identify the type and location of the fault in the mechanical system, thus providing a strong basis for timely and effective maintenance.

[0003] In engineering practice, the operating status of rotating machinery can be measured and analyzed through various physical signals such as vibration, sound, pressure, and temperature. Vibration signals are easy to collect, sensitive to machine failures, and can separate the parts of interest from other unnecessary strong vibration components. Therefore, vibration signals are very useful in detecting early defects, extracting fault characteristics, and optimizing health indicators, which has led to the widespread research and application of fault diagnosis technology based on vibration signals in mechanical equipment status detection. When industrial equipment fails, its vibration often shows complex modulation changes, which in turn causes a sharp change in the derivative of the phase function. Due to the complex mechanical structure and harsh working environment, the components of the generated monitoring signals are more complex, making it difficult for traditional signal processing methods to meet the requirements of transient signal feature extraction and fault identification. Especially for early mechanical failures, diagnostic signals are often affected by various interferences from the environment, sensors, and the mechanical system itself. The resulting impact characteristics may be overwhelmed by strong background noise, leading to misdiagnosis. Summary of the invention

[0004] The embodiment of the present invention provides a mechanical transient fault diagnosis method and device based on group delay ridge optimization, which is used to solve the following technical problems: the existing vibration signal processing method is difficult to extract transient signal features from vibration signals with complex components, is easily affected by interference, and has a low fault diagnosis accuracy.

[0005] The embodiment of the present invention adopts the following technical solutions: On the one hand, an embodiment of the present invention provides a mechanical transient fault diagnosis method based on group delay ridge optimization, the method comprising: performing time-frequency transformation and phase modulation wavelet transformation on the collected vibration signal to obtain a corresponding time-frequency transformation result; Based on the time-frequency transformation result, extracting the group delay ridge of the first mode in the vibration signal; Performing group delay band filtering on the group delay ridge to remove noise interference and obtain an optimized group delay ridge; After removing the time-frequency coefficients corresponding to the optimized group delay ridge, the group delay ridge of the second mode is iteratively extracted, and the searched group delay ridge is optimized; and so on, until the group delay ridge of each mode is obtained; According to the group delay ridge of each mode, an optimized time-frequency transformation result is obtained, and the optimized time-frequency transformation result is time-frequency compressed to obtain an optimized time-frequency image corresponding to the vibration signal; Based on the group delay ridge of each mode, the transient fault type of the current machinery is diagnosed.

[0006] In a feasible implementation manner, the collected vibration signal is subjected to time-frequency transformation and phase modulation wavelet transformation to obtain the corresponding time-frequency transformation result, which specifically includes: The collected vibration signal is modeled as a superposition of several intrinsic mode functions and Fourier transformed to obtain a time-frequency signal model; The wavelet basis function is phase modulated by the scale factor and the translation factor, and the time-frequency signal model is subjected to wavelet transform by the modulated wavelet basis function to obtain a corresponding time-frequency transform result.

[0007] In a feasible implementation manner, based on the time-frequency transformation result, extracting the group delay ridge of the first mode in the vibration signal specifically includes: Dividing the time-frequency plane where the time-frequency transformation result is located into a plurality of time-frequency segments along the frequency direction; The maximum time-frequency energy point in each time-frequency segment is determined as the search starting point, and the time-frequency energy peak points in the neighborhood are searched to the left and right respectively; the maximum time-frequency energy point in the time-frequency segment is connected with the time-frequency energy peak point to obtain the group delay ridge of each time-frequency segment; The time-frequency energy of each time-frequency segment is obtained, and the time-frequency segment with the largest time-frequency energy is determined as the first mode; and the group delay ridge corresponding to the time-frequency segment with the largest time-frequency energy is determined as the group delay ridge of the first mode.

[0008] In a feasible implementation, performing group delay band filtering on the group delay ridge to remove noise interference and obtain an optimized group delay ridge specifically includes: The time-frequency energy peak of the time-frequency transform result is used as the center of the group delay time width, and the group delay ridge line at the center of the group delay time width is set as an alignment reference value; Based on the alignment reference value, readjust the definition of the effective time width region in the group delay estimation process; Determining a group delay estimator based on the effective time width region definition; Calculating each group delay band width of the group delay ridge in the group delay estimator, counting the number of each group delay band width, and drawing a distribution histogram of the group delay band width; Analyze the distribution histogram to obtain a filtering threshold; Based on the filtering threshold, the group delay band in the group delay ridge is filtered to remove noise interference in the group delay ridge to obtain an optimized group delay ridge.

[0009] In a feasible implementation manner, analyzing the distribution histogram to obtain a filtering threshold specifically includes: Fitting the distribution histogram by the Trust-Region algorithm to analyze the distribution properties of the noise signal and the normal signal; Based on the distribution property, the intersection point of the noise signal distribution area and the normal signal distribution area is obtained, and the group delay bandwidth corresponding to the intersection point is used as the filtering threshold.

[0010] In a feasible implementation manner, based on the filtering threshold, filtering the group delay band in the group delay ridge includes: Among the group delay ridges, group delay bands whose width is less than the filtering threshold are filtered out, and group delay bands whose width is greater than or equal to the filtering threshold are retained to obtain the filtered group delay ridges.

[0011] In a feasible implementation, after the time-frequency coefficients corresponding to the optimized group delay ridge are removed, the group delay ridge of the second mode is iteratively extracted, and the searched group delay ridge is optimized; and so on, until the group delay ridge of each mode is obtained, specifically including: Setting the time-frequency coefficient corresponding to the optimized group delay ridge of the first mode to zero in the time-frequency transform result; Based on the time-frequency transformation result after zeroing, the group delay ridge of the mode with the largest time-frequency energy is further extracted to obtain the group delay ridge of the second mode; Performing group delay band filtering on the group delay ridge of the second mode to remove noise interference and obtain an optimized group delay ridge; Setting the time-frequency coefficient corresponding to the optimized group delay ridge of the second mode to zero in the time-frequency transform result; Based on the time-frequency transformation result after the zeroing process, the group delay ridge of the mode with the largest time-frequency energy continues to be extracted, and so on, until the group delay ridge of each mode is obtained.

[0012] In a feasible implementation manner, according to the group delay ridge of each mode, an optimized time-frequency transformation result is obtained, and the optimized time-frequency transformation result is time-frequency compressed to obtain an optimized time-frequency image corresponding to the vibration signal, specifically including: Substituting the group delay corresponding to the group delay ridge of each mode into the time-frequency transformation result to obtain an optimized time-frequency transformation result; The optimized time-frequency transformation result is subjected to transient compression transformation by means of a group delay compression transformation algorithm to obtain an optimized time-frequency image corresponding to the vibration signal.

[0013] In a feasible implementation, based on the group delay ridge of each mode, the transient fault type of the current machine is diagnosed, specifically including: extracting the impulse interval frequency of the group delay ridge; Compare the impact interval frequency with the theoretical fault frequencies of various transient fault types to determine the transient fault type of the current machine; or, The impact interval frequency and the optimized time-frequency image are input into a classifier to perform fault type diagnosis, thereby obtaining the transient fault type of the current machine.

[0014] On the other hand, an embodiment of the present invention further provides a mechanical transient fault diagnosis device based on group delay ridge optimization, the device comprising: At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor so that the at least one processor can execute the mechanical transient fault diagnosis method based on group delay ridge optimization.

[0015] Compared with the prior art, the mechanical transient fault diagnosis method and equipment based on group delay ridge optimization provided by the embodiment of the present invention has the following beneficial effects: The present invention proposes to enhance the stability of group delay estimation in the time-frequency processing algorithm based on group delay ridge optimization technology, and utilizes the central group delay and group delay bandwidth to achieve dynamic adjustment of the group delay ridge under strong frequency-varying signals and the removal of noise in the time-frequency plane. At the same time, the process still maintains the ability to restore the original signal, achieves the improvement of energy concentration of time-frequency representation and the increase of noise resistance, thereby more accurately representing the impact characteristics of the group delay ridge when the equipment fails, and improving the accuracy of mechanical transient fault diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 A flow chart of a mechanical transient fault diagnosis method based on group delay ridge optimization provided by an embodiment of the present invention; Figure 2 A schematic diagram of a fitting function of a distribution histogram provided by an embodiment of the present invention; Figure 3 A schematic structural diagram of a mechanical transient fault diagnosis device based on group delay ridge optimization provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to enable those skilled in the art to better understand the technical solutions in the present invention, 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 only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of this specification, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0018] The embodiment of the present invention provides a mechanical transient fault diagnosis method based on group delay ridge optimization, such as Figure 1 As shown, the mechanical transient fault diagnosis method based on group delay ridge optimization specifically includes steps S101-S105: S101, performing time-frequency transformation and phase modulation wavelet transformation on the collected vibration signal to obtain a corresponding time-frequency transformation result.

[0019] Specifically, the collected vibration signal is modeled as a superposition of several intrinsic mode functions, and Fourier transform is performed to obtain a time-frequency signal model.

[0020] As a feasible implementation method, the collected vibration signal The signal model is modeled as the superposition of K frequency-varying intrinsic mode functions and Fourier transform is performed to obtain the following signal model: ;in, and They represent the instantaneous amplitude and instantaneous phase of the kth mode in the signal respectively, and they can effectively characterize the energy and delay properties of the signal that change with frequency. Indicates the frequency point, represents a frequency variable, represents the group delay of the signal, For time domain signal s ( t ) is the Fourier transform of .

[0021] Furthermore, the wavelet basis function is phase modulated by the scale factor and the translation factor, and the time-frequency signal model is subjected to wavelet transform by the modulated wavelet basis function to obtain the corresponding time-frequency transform result.

[0022] As a feasible implementation method, based on the above signal model, by combining a series of scale factors and translation factor The modulated wavelet basis function w() is used for phase modulation wavelet transform to obtain the time-frequency transform result: .

[0023] S102. Extracting the group delay ridge of the first mode in the vibration signal based on the time-frequency transformation result.

[0024] Specifically, the time-frequency plane where the time-frequency transform result is located is divided into several time-frequency segments along the frequency direction. Then, the maximum time-frequency energy point in each time-frequency segment is determined as the search starting point, and the time-frequency energy peak points in the neighborhood are searched to the left and right respectively. Finally, the maximum time-frequency energy point in the time-frequency segment is connected with the time-frequency energy peak point to obtain the group delay ridge of each time-frequency segment.

[0025] Furthermore, the time-frequency energy of each time-frequency segment is obtained, and the time-frequency segment with the largest time-frequency energy is determined as the first mode; the group delay ridge corresponding to the time-frequency segment with the largest time-frequency energy is determined as the group delay ridge of the first mode.

[0026] S103, performing group delay band filtering processing on the group delay ridge to remove noise interference and obtain an optimized group delay ridge.

[0027] Specifically, due to the influence of noise on the main components of the signal, the group delay estimation result obtained based on the time-frequency coefficient will produce severe jitter near the ridge line. Therefore, the present invention optimizes and adjusts the group delay ridge line obtained based on the time-frequency coefficient. The specific method is as follows: First, the time-frequency energy peak of the time-frequency transform result is taken as the center of the group delay width, and the group delay ridge at the center of the group delay width is set as the alignment reference value. The center of the group delay width can be expressed as: ; where st represents the constraint condition.

[0028] Further, based on the alignment reference value, the effective time width region in the process of re-adjusting the group delay estimation is defined as: ;in, and They are the left and right boundaries of the effective time width area, R represents the right boundary, L represents the left boundary, and C represents the center of the group delay time width.

[0029] Furthermore, based on the definition of the effective time width region, the group delay estimator is determined: .

[0030] Then, each group delay band width of the group delay ridge in the group delay estimator is calculated, and the number of each group delay band width is counted, and a distribution histogram of the group delay band width is plotted.

[0031] Furthermore, the distribution histogram is analyzed to obtain the filtering threshold: the distribution histogram is fitted by the Trust-Region algorithm to analyze the distribution properties of the noise signal and the normal signal. Based on the distribution properties, the intersection point of the noise signal distribution area and the normal signal distribution area is obtained, and the group delay bandwidth corresponding to the intersection point is used as the filtering threshold.

[0032] As a feasible implementation method, Figure 2 A schematic diagram of a fitting function of a distribution histogram provided by an embodiment of the present invention, such as Figure 2 The figure shows the fitting function corresponding to the distribution histogram after fitting by the Trust-Region algorithm. The blue curve on the left is the noise curve, and the red curve on the right is the normal signal curve. The intersection point of the two curves is S t . Then the time width S t as the filtering threshold.

[0033] Furthermore, based on the filtering threshold, the group delay band in the group delay ridge is filtered to remove the noise interference in the group delay ridge to obtain an optimized group delay ridge, specifically including: In the group delay ridge, the group delay bands whose width is less than the filtering threshold are filtered out, and the group delay bands whose width is greater than or equal to the filtering threshold are retained to obtain the group delay ridge after filtering.

[0034] S104, after removing the time-frequency coefficients corresponding to the optimized group delay ridge, iteratively extract the group delay ridge of the second mode, and perform ridge optimization on the searched group delay ridge; and so on, until the group delay ridge of each mode is obtained.

[0035] Specifically, the time-frequency coefficients corresponding to the optimized group delay ridge of the first mode are set to zero in the time-frequency transform result.

[0036] Then, based on the time-frequency transformation result after the zeroing process, the group delay ridge of the mode with the largest time-frequency energy is further extracted to obtain the group delay ridge of the second mode.

[0037] The group delay band filtering process is continued for the group delay ridge of the second mode to remove noise interference and obtain the optimized group delay ridge. The time-frequency coefficient corresponding to the optimized group delay ridge of the second mode is set to zero in the time-frequency transformation result.

[0038] Based on the time-frequency transformation result after the zeroing process, the group delay ridge of the mode with the largest time-frequency energy continues to be extracted, and so on, the group delay ridge of each mode is extracted cyclically.

[0039] As a feasible implementation, the key issue in the adaptive detection of group delay ridges in each time-frequency segment is how to set appropriate termination conditions in the ridge detection process. For complex signals with significant differences in modal amplitudes and strong noise interference, a group delay extraction operator is introduced: As a binary operator, this operator ignores the influence of the amplitude of the time-frequency coefficients and only Since GDO is a binary result consisting of only 0 and 1, and the group delay is distributed along the frequency direction as a single-valued function of frequency, the termination condition can be defined as: ;in is a constant threshold. According to experience, it is recommended to set it to 0.9, and N is the length of the collected vibration signal.

[0040] S105, according to the group delay ridge of each mode, obtain the optimized time-frequency transformation result, and perform time-frequency compression on the optimized time-frequency transformation result to obtain the optimized time-frequency image corresponding to the vibration signal. Based on the group delay ridge of each mode, diagnose the transient fault type of the current machine.

[0041] Specifically, the group delay corresponding to the group delay ridge of each mode is substituted into the time-frequency transformation result to obtain the optimized time-frequency transformation result. Then, the group delay compression transformation function is used: , perform transient compression transformation on the optimized time-frequency transformation result to obtain the optimized time-frequency image corresponding to the vibration signal; where u represents time.

[0042] Furthermore, the impulse interval frequency of the group delay ridge is extracted. Then the impulse interval frequency is compared with the theoretical fault frequencies of various transient fault types to determine the transient fault type of the current machine; or, the impulse interval frequency and the optimized time-frequency image are input into the classifier for fault type diagnosis to obtain the transient fault type of the current machine.

[0043] As a feasible implementation method, the obtained optimized time-frequency processed image or the group delay slice image that needs to be identified can also be used as the input of the deep learning model to identify the fault problem of the equipment.

[0044] In addition, an embodiment of the present invention further provides a mechanical transient fault diagnosis device based on group delay ridge optimization, such as Figure 3 As shown, the mechanical transient fault diagnosis device based on group delay ridge optimization specifically includes: at least one processor; and a memory in communication with the at least one processor; wherein, The memory stores instructions executable by at least one processor to enable the at least one processor to perform: Perform time-frequency transformation and phase modulation wavelet transformation on the collected vibration signal to obtain the corresponding time-frequency transformation result; Based on the time-frequency transformation result, extracting the group delay ridge of the first mode in the vibration signal; Performing group delay band filtering on the group delay ridge to remove noise interference and obtain an optimized group delay ridge; After removing the time-frequency coefficients corresponding to the optimized group delay ridge, the group delay ridge of the second mode is iteratively extracted, and the searched group delay ridge is optimized; and so on, until the group delay ridge of each mode is obtained; According to the group delay ridge of each mode, an optimized time-frequency transformation result is obtained, and the optimized time-frequency transformation result is time-frequency compressed to obtain an optimized time-frequency image corresponding to the vibration signal; Based on the group delay ridge of each mode, the transient fault type of the current machinery is diagnosed.

[0045] Each embodiment of the present invention is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device, equipment, and non-volatile computer storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiments.

[0046] The above describes specific embodiments of the present invention. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0047] The above description is only an embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the embodiments of the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the embodiments of the present invention should be included in the protection scope of the present invention.

Claims

1. A mechanical transient fault diagnosis method based on group delay ridge optimization, characterized in that: The method comprises: Perform time-frequency transformation and phase modulation wavelet transformation on the collected vibration signal to obtain the corresponding time-frequency transformation result; Based on the time-frequency transformation result, extracting the group delay ridge of the first mode in the vibration signal; Performing group delay band filtering on the group delay ridge to remove noise interference and obtain an optimized group delay ridge; After removing the time-frequency coefficients corresponding to the optimized group delay ridge, the group delay ridge of the second mode is iteratively extracted, and the searched group delay ridge is optimized; and so on, until the group delay ridge of each mode is obtained; According to the group delay ridge of each mode, an optimized time-frequency transformation result is obtained, and the optimized time-frequency transformation result is time-frequency compressed to obtain an optimized time-frequency image corresponding to the vibration signal; Based on the group delay ridge of each mode, the transient fault type of the current machinery is diagnosed.

2. A mechanical transient fault diagnosis method based on group delay ridge optimization according to claim 1, characterized in that: The collected vibration signal is subjected to time-frequency transformation and phase modulation wavelet transformation to obtain the corresponding time-frequency transformation results, including: The collected vibration signal is modeled as a superposition of several intrinsic mode functions and Fourier transformed to obtain a time-frequency signal model; The wavelet basis function is phase modulated by the scale factor and the translation factor, and the time-frequency signal model is subjected to wavelet transform by the modulated wavelet basis function to obtain a corresponding time-frequency transform result.

3. The mechanical transient fault diagnosis method based on group delay ridge optimization according to claim 1 is characterized in that: Based on the time-frequency transformation result, extracting the group delay ridge of the first mode in the vibration signal specifically includes: Dividing the time-frequency plane where the time-frequency transformation result is located into a plurality of time-frequency segments along the frequency direction; The maximum time-frequency energy point in each time-frequency segment is determined as the search starting point, and the time-frequency energy peak points in the neighborhood are searched to the left and right respectively; the maximum time-frequency energy point in the time-frequency segment is connected with the time-frequency energy peak point to obtain the group delay ridge of each time-frequency segment; The time-frequency energy of each time-frequency segment is obtained, and the time-frequency segment with the largest time-frequency energy is determined as the first mode; and the group delay ridge corresponding to the time-frequency segment with the largest time-frequency energy is determined as the group delay ridge of the first mode.

4. The mechanical transient fault diagnosis method based on group delay ridge optimization according to claim 1 is characterized in that: The group delay ridge is subjected to group delay band filtering to remove noise interference and obtain an optimized group delay ridge, specifically comprising: The time-frequency energy peak of the time-frequency transform result is used as the center of the group delay time width, and the group delay ridge line at the center of the group delay time width is set as an alignment reference value; Based on the alignment reference value, readjust the definition of the effective time width region in the group delay estimation process; Determining a group delay estimator based on the effective time width region definition; Calculating each group delay band width of the group delay ridge in the group delay estimator, counting the number of each group delay band width, and drawing a distribution histogram of the group delay band width; Analyze the distribution histogram to obtain a filtering threshold; Based on the filtering threshold, the group delay band in the group delay ridge is filtered to remove noise interference in the group delay ridge to obtain an optimized group delay ridge.

5. The mechanical transient fault diagnosis method based on group delay ridge optimization according to claim 4 is characterized in that: Analyzing the distribution histogram to obtain a filtering threshold specifically includes: Fitting the distribution histogram by the Trust-Region algorithm to analyze the distribution properties of the noise signal and the normal signal; Based on the distribution property, the intersection point of the noise signal distribution area and the normal signal distribution area is obtained, and the group delay bandwidth corresponding to the intersection point is used as the filtering threshold.

6. The mechanical transient fault diagnosis method based on group delay ridge optimization according to claim 4 is characterized in that: Based on the filtering threshold, filtering the group delay band in the group delay ridge includes: Among the group delay ridges, group delay bands whose width is less than the filtering threshold are filtered out, and group delay bands whose width is greater than or equal to the filtering threshold are retained to obtain the filtered group delay ridges.

7. The mechanical transient fault diagnosis method based on group delay ridge optimization according to claim 1 is characterized in that: After removing the time-frequency coefficients corresponding to the optimized group delay ridge, the group delay ridge of the second mode is iteratively extracted, and the searched group delay ridge is optimized; And so on, until the group delay ridge of each mode is obtained, including: Setting the time-frequency coefficient corresponding to the optimized group delay ridge of the first mode to zero in the time-frequency transform result; Based on the time-frequency transformation result after zeroing, the group delay ridge of the mode with the largest time-frequency energy is further extracted to obtain the group delay ridge of the second mode; Performing group delay band filtering on the group delay ridge of the second mode to remove noise interference and obtain an optimized group delay ridge; Setting the time-frequency coefficient corresponding to the optimized group delay ridge of the second mode to zero in the time-frequency transform result; Based on the time-frequency transformation result after the zeroing process, the group delay ridge of the mode with the largest time-frequency energy continues to be extracted, and so on, until the group delay ridge of each mode is obtained.

8. The mechanical transient fault diagnosis method based on group delay ridge optimization according to claim 1 is characterized in that: According to the group delay ridge of each mode, an optimized time-frequency transformation result is obtained, and the optimized time-frequency transformation result is time-frequency compressed to obtain an optimized time-frequency image corresponding to the vibration signal, specifically including: Substituting the group delay corresponding to the group delay ridge of each mode into the time-frequency transformation result to obtain an optimized time-frequency transformation result; The optimized time-frequency transformation result is subjected to transient compression transformation by means of a group delay compression transformation algorithm to obtain an optimized time-frequency image corresponding to the vibration signal.

9. The mechanical transient fault diagnosis method based on group delay ridge optimization according to claim 1, characterized in that: Based on the group delay ridge of each mode, the transient fault type of the current machinery is diagnosed, including: extracting the impulse interval frequency of the group delay ridge; Compare the impact interval frequency with the theoretical fault frequencies of various transient fault types to determine the transient fault type of the current machine; or, The impact interval frequency and the optimized time-frequency image are input into a classifier to perform fault type diagnosis, thereby obtaining the transient fault type of the current machine.

10. A mechanical transient fault diagnosis device based on group delay ridge optimization, characterized in that: The device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, so that the at least one processor can execute the mechanical transient fault diagnosis method based on group delay ridge optimization according to any one of claims 1-9.

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