A method for quantifying the degree of fault in rotating machinery based on the time-frequency energy of impact characteristics

Abstract

The present invention discloses a method for quantifying the degree of rotating machinery fault based on the time-frequency energy of impact characteristics, including: 1. Acquisition of vibration signals; 2. Time-frequency transformation of analysis signals; 3. Calculation of two-dimensional Fourier transform of two-dimensional distribution of signals; 4. Calculation of impact energy interval; 5. Calculation of periodic impact energy ratio factor. The present invention uses two-dimensional Fourier transform to concentrate the time-frequency energy of periodic impacts in the time-frequency distribution, and the generalized transient compression transform algorithm used can more accurately estimate the group delay of the impact signal. At the same time, in order to cope with the non-periodic impact response characteristics generated by impact faults under variable speed conditions, the present invention converts the time-frequency distribution into an angular frequency distribution, and then converts the time-domain non-periodic impact under variable speed conditions into an angular domain periodic impact, which can more effectively quantify the time-frequency energy of the impact characteristics, thereby quantitatively measuring the degree of fault under variable speed conditions.

Description

Technical Field

[0001] The invention belongs to the technical field of mechanical fault diagnosis, and in particular relates to a method for quantifying the degree of rotating mechanical fault based on impact characteristic time-frequency energy. Background Art

[0002] Faults in rotating machinery, especially those in bearings and gears, are usually manifested as impulse responses in vibration signals. These impulse responses contain specific fault characteristics and are an important basis for fault diagnosis. Specifically, when bearings or gears are partially damaged, they interact with rolling elements or adjacent gears during rotation, generating periodic impulse signals. These impulse signals exhibit specific patterns in the time and frequency domains. For example, a fault in the outer ring of a bearing will generate periodic impulses of equal amplitude, while a fault in the inner ring will cause the amplitude of the impulse signal to vary with the shaft rotation frequency.

[0003] However, most of the current fault diagnosis methods are based only on the recognition of impact features, and the main goal is to determine whether a fault exists, but it is difficult to quantify the severity of the damage. This is because most existing methods can only capture the presence or absence of impact signals, but cannot accurately measure key parameters such as their intensity and frequency, and thus cannot provide detailed information about the extent of damage.

[0004] Although indicators such as spectral kurtosis have high sensitivity in identifying shock components in signals and are often used to identify shock-type faults such as bearing faults, they are easily interfered by random large shocks in practical applications. These random large shocks may come from various uncertain factors in the operation of the machine, such as load changes, external vibrations, etc. They will interfere with the calculation results of spectral kurtosis, resulting in inaccurate diagnostic results and even misjudgment. Therefore, indicators such as spectral kurtosis have certain limitations in practical applications.

[0005] In order to overcome the limitations of spectral kurtosis, some indicators for the periodicity of impulse signals in signals have been proposed. These indicators attempt to determine the severity of the fault by measuring the periodicity of the impulse signal. However, these indicators often fail to effectively determine the fault when dealing with non-stationary signals. Non-stationary signals refer to signals whose statistical characteristics change over time, such as the variable speed operation process of rotating machinery. In this case, the periodicity of the impulse signal may change, resulting in the inability of indicators based on periodicity to accurately determine the fault.

[0006] On the other hand, indicators based on vibration response amplitude, such as the root mean square (RMS), can reflect the vibration intensity to a certain extent, but lack the ability to accurately quantify the impact characteristics. The root mean square mainly measures the average energy of the vibration signal, but cannot accurately capture the specific characteristics of the impact signal. Therefore, the persuasiveness of its diagnostic results is relatively weak and cannot meet the needs of high-precision fault diagnosis.

[0007] In summary, current fault diagnosis methods for rotating machinery still have significant limitations in quantifying the extent of damage. Summary of the invention

[0008] Purpose of the invention: The purpose of the present invention is to provide a method for quantifying the degree of fault in rotating machinery based on the time-frequency energy of the impact feature. The purpose is to more accurately quantify the degree of damage by measuring the proportion of periodic energy of the impact feature in the time-frequency distribution. At the same time, the present invention also constructs an impact feature periodicity enhancement algorithm to effectively deal with the problem of impact feature quantification under variable speed conditions and improve the accuracy and reliability of fault diagnosis.

[0009] Technical solution: A method for quantifying the degree of rotating machinery fault based on the time-frequency energy of impact characteristics of the present invention comprises the following steps:

[0010] Step 1: Obtain a vibration signal of a target rotating machine;

[0011] Step 2, using generalized transient compression transform to calculate the time-frequency distribution of the vibration signal;

[0012] Step 3: If the vibration signal is a periodic impact fault signal under a constant speed condition, proceed to step 4; if the vibration signal is a non-periodic impact fault signal under a variable speed condition, proceed to step 5;

[0013] Step 4, calculating the two-dimensional Fourier transform of the time-frequency distribution of the generalized transient compression transform of the signal;

[0014] Step 5: first transform the time-frequency distribution of the signal into an angular frequency distribution, and then calculate the two-dimensional Fourier transform result of the angular frequency distribution of the signal;

[0015] Step 6: Calculate the impact energy interval and the periodic impact energy ratio factor, quantify the time-frequency energy of the impact feature, and thus quantify the fault degree of the rotating machinery.

[0016] Furthermore, step 2 is specifically as follows: the time-frequency distribution can simultaneously characterize the time domain and frequency domain information in the vibration signal, the generalized transient compression transform can accurately estimate the complex group delay characteristics of the transient signal, and obtain a more concentrated time-frequency distribution by multiple compression and rearrangement of the time-frequency distribution energy. The generalized transient compression transform has the following representation:

[0017]

[0018] In the formula, S(τ,ω) represents the time-frequency distribution of the short-time Fourier transform of the signal, δ(·) represents the Dirac function, is the M-iteration result of the generalized group delay estimation operator, and its N-order form is as follows:

[0019]

[0020] In the formula, is the generalized group delay estimate, and its explicit expression is as follows:

[0021]

[0022] In the formula, Indicates taking the imaginary part of the function, B 1,k is the algebraic cofactor of the generalized time-frequency matrix B, β=σ 2 is a fixed parameter of the frequency domain Gaussian window function, and a generalized time-frequency matrix B is constructed.

[0023] Furthermore, the construction process of the generalized time-frequency matrix B is as follows:

[0024]

[0025] In the formula, is the time-frequency distribution obtained by short-time Fourier transform based on different Gaussian window functions. The calculation process is as follows:

[0026]

[0027] In the formula, h(t=-j) k-1 g (k-1) t,k∈N + is the Gaussian window function, x(τ) is the analysis signal, h * It means taking the complex conjugate of a function.

[0028] Furthermore, step 4 is specifically as follows: calculating the two-dimensional Fourier transform of the time-frequency distribution of the generalized transient compression transform, and the two-dimensional Fourier transform calculation formula of the time-frequency distribution is as follows:

[0029] F(u,z)=∫∫GTST(t,ω)e -j2π(ut+zω) dtdω (6)

[0030] Where GTST(t,ω) is the result of the generalized transient compression transformation in formula (1).

[0031] Furthermore, step 5 is specifically as follows: first, transform the time-frequency distribution into the angular frequency distribution. The transformation formula from the time-frequency distribution to the angular frequency distribution is as follows:

[0032]

[0033] In the formula, is the angular frequency distribution corresponding to time t, and then the two-dimensional Fourier transform result of the angular frequency distribution is calculated according to formula (6), and IF(t) represents the instantaneous frequency conversion of the signal.

[0034] Furthermore, in step 5, the calculation method of the impact energy interval is:

[0035] If the signal is a periodic impact fault signal under constant speed conditions, the impact time interval is calculated as follows:

[0036]

[0037] In the formula, f r is the switching frequency, O is the fault order;

[0038] If the signal is a non-periodic impact fault signal under variable speed conditions, the impact angle interval is calculated as follows:

[0039]

[0040] Where O is the fault order.

[0041] Furthermore, in step 5, the calculation formula of the periodic impact energy ratio factor is as follows:

[0042]

[0043] Where Δ is Δt in equation (8) or The impact interval, F, is the two-dimensional Fourier transform result in equation (6).

[0044] The present invention also discloses a computer device, comprising a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the method of the present invention.

[0045] The present invention also discloses a computer-readable storage medium on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the method of the present invention are implemented.

[0046] The present invention also discloses a computer program product, comprising a computer program / instruction, which implements the steps of the method of the present invention when executed by a processor.

[0047] Beneficial effects: Compared with the prior art, the present invention has the following significant advantages:

[0048] The present invention adopts a generalized transient compression transform to obtain more concentrated time-frequency energy of the impact feature, and obtains the periodicity of the impact feature in the time-frequency distribution through a two-dimensional Fourier transform, and proposes a periodic impact energy ratio factor to quantify and measure the periodic impact feature. In order to obtain a more concentrated time-frequency distribution of the impact signal, the generalized transient compression transform algorithm adopted by the present invention can more accurately estimate the group delay of the impact signal. In order to effectively quantify and measure the impact component in the fault signal under the variable speed condition, the present invention converts the time-frequency distribution into an angular frequency distribution through a resampling and interpolation algorithm, converts the non-periodic impact caused by the variable speed condition into a periodic impact in the angle domain, and then obtains the periodicity of the impact energy in the angular frequency distribution through a two-dimensional Fourier transform, and then converts the time-domain non-periodic impact under the variable speed condition into an angle-domain periodic impact, which can more effectively quantify the time-frequency energy of the impact feature, so that the non-periodic impact fault feature under the variable speed condition can also be quantified and measured. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a flow chart of the method of the present invention.

[0050] Figure 2 The vibration response time domain signals of different degrees of impact faults under constant speed conditions. (a) The vibration response time domain waveform of no impact fault, (b) The vibration response time domain waveform of no impact fault, (c) The vibration response time domain waveform of no impact fault, (d) The vibration response time domain waveform of no impact fault.

[0051] Figure 3 The short-time Fourier transform time-frequency distribution results of vibration signals with different degrees of impact faults under constant speed conditions. (a) Short-time Fourier transform time-frequency distribution of vibration signals with no impact faults, (b) Short-time Fourier transform time-frequency distribution of vibration signals with no impact faults, (c) Short-time Fourier transform time-frequency distribution of vibration signals with no impact faults, (d) Short-time Fourier transform time-frequency distribution of vibration signals with no impact faults.

[0052] Figure 4 The results of the generalized transient compression transform time-frequency distribution of vibration signals with different degrees of impact faults under constant speed conditions. (a) The generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (b) The generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (c) The generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (d) The generalized transient compression transform time-frequency distribution of vibration signals without impact faults.

[0053] Figure 5The two-dimensional Fourier transform results of the generalized transient compression transform time-frequency distribution of vibration signals with different degrees of impact faults under constant speed conditions. (a) The two-dimensional Fourier transform result of the generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (b) The two-dimensional Fourier transform result of the generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (c) The two-dimensional Fourier transform result of the generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (d) The two-dimensional Fourier transform result of the generalized transient compression transform time-frequency distribution of vibration signals without impact faults.

[0054] Figure 6 The following are partial enlarged images of the two-dimensional Fourier transform results of the generalized transient compression transform time-frequency distribution of vibration signals with different degrees of impact faults under constant speed conditions. (a) A partial enlarged image of the two-dimensional Fourier transform result of the generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (b) A partial enlarged image of the two-dimensional Fourier transform result of the generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (c) A partial enlarged image of the two-dimensional Fourier transform result of the generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (d) A partial enlarged image of the two-dimensional Fourier transform result of the generalized transient compression transform time-frequency distribution of vibration signals without impact faults.

[0055] Figure 7 The vibration response time domain signals of different degrees of impact faults under variable speed conditions. (a) The vibration response time domain waveform of no impact fault, (b) The vibration response time domain waveform of no impact fault, (c) The vibration response time domain waveform of no impact fault, (d) The vibration response time domain waveform of no impact fault.

[0056] Figure 8 The short-time Fourier transform time-frequency distribution results of vibration signals with different degrees of impact faults under variable speed conditions. (a) Short-time Fourier transform time-frequency distribution of vibration signals with no impact faults, (b) Short-time Fourier transform time-frequency distribution of vibration signals with no impact faults, (c) Short-time Fourier transform time-frequency distribution of vibration signals with no impact faults, (d) Short-time Fourier transform time-frequency distribution of vibration signals with no impact faults.

[0057] Fig. 9 The results of the generalized transient compression transform time-frequency distribution of vibration signals with different degrees of impact faults under variable speed conditions. (a) The generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (b) The generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (c) The generalized transient compression transform time-frequency distribution of vibration signals without impact faults, (d) The generalized transient compression transform time-frequency distribution of vibration signals without impact faults.

[0058] Fig.10The angular frequency distribution results of vibration signals with different degrees of impact faults under variable speed conditions. (a) Angular frequency distribution of vibration signals with no impact faults, (b) Angular frequency distribution of vibration signals with no impact faults, (c) Angular frequency distribution of vibration signals with no impact faults, (d) Angular frequency distribution of vibration signals with no impact faults.

[0059] Fig.11 The two-dimensional Fourier transform results of the angular frequency distribution of vibration signals with different degrees of impact faults under variable speed conditions. (a) The two-dimensional Fourier transform result of the angular frequency distribution of vibration signals without impact faults, (b) The two-dimensional Fourier transform result of the angular frequency distribution of vibration signals without impact faults, (c) The two-dimensional Fourier transform result of the angular frequency distribution of vibration signals without impact faults, (d) The two-dimensional Fourier transform result of the angular frequency distribution of vibration signals without impact faults.

[0060] Fig.12 The following are partial enlarged images of the 2D Fourier transform results of the angular frequency distribution of vibration signals with different degrees of impact faults under variable speed conditions. (a) A partial enlarged image of the 2D Fourier transform results of the angular frequency distribution of vibration signals without impact faults, (b) A partial enlarged image of the 2D Fourier transform results of the angular frequency distribution of vibration signals without impact faults, (c) A partial enlarged image of the 2D Fourier transform results of the angular frequency distribution of vibration signals without impact faults, (d) A partial enlarged image of the 2D Fourier transform results of the angular frequency distribution of vibration signals without impact faults. DETAILED DESCRIPTION

[0061] The technical solution of the present invention is further described below in conjunction with the accompanying drawings.

[0062] Although traditional signal processing methods can identify the fault form of a mechanical system by identifying the impact features in the fault signal, they cannot quantify the degree of the fault. To this end, the present invention uses a generalized transient compression transform to obtain more concentrated time-frequency energy of the impact features, and obtains the periodicity of the impact features in the time-frequency distribution through a two-dimensional Fourier transform, and proposes a periodic impact energy ratio factor to quantitatively measure the periodic impact features. In order to effectively quantify and measure the impact components in the fault signal under variable speed conditions, the present invention converts the time-frequency distribution into an angular frequency distribution through a resampling and interpolation algorithm, converts the non-periodic impact caused by the variable speed condition into a periodic impact in the angle domain, and then obtains the periodicity of the impact energy in the angular frequency distribution through a two-dimensional Fourier transform, so that the non-periodic impact fault features under the variable speed condition can also be quantified and measured.

[0063] like Figure 1 As shown, the specific technical solutions of the present invention are as follows:

[0064] A method for quantifying the fault degree of rotating machinery based on the time-frequency energy of impact characteristics.

[0065] The first step is to obtain vibration signals.

[0066] The fault manifestation of rotating machinery is usually in the form of impulse response in the vibration signal. By obtaining the vibration signal of rotating machinery, the fault information of the mechanical system can be effectively characterized.

[0067] The second step is to perform time-frequency transformation on the analysis signal.

[0068] The time-frequency distribution can simultaneously characterize the time domain and frequency domain information in the vibration signal. The present invention uses the generalized transient compression transform to calculate the time-frequency distribution of the impact signal. The generalized transient compression transform can accurately estimate the complex group delay characteristics of the transient signal, so it is more suitable for processing the transient impact characteristics caused by faults. At the same time, the generalized transient compression transform can obtain a more concentrated time-frequency distribution by compressing and rearranging the time-frequency distribution energy multiple times. The generalized transient compression transform method is derived as follows:

[0069] The generalized transient compression transform has the following representation:

[0070]

[0071] In the formula, is the M-iteration result of the generalized group delay estimation operator. Its N-order form is shown below:

[0072]

[0073] In the formula, is the generalized group delay estimate, and its explicit expression is as follows:

[0074]

[0075] In the formula, Indicates taking the imaginary part of the function, B 1,k is the algebraic cofactor of the generalized time-frequency matrix B, β-σ 2 is the fixed parameter of the frequency domain Gaussian window function. The construction process of the generalized time-frequency matrix B is as follows:

[0076]

[0077] In the formula, is the time-frequency distribution obtained by short-time Fourier transform based on different Gaussian window functions. The calculation process is as follows:

[0078]

[0079] In the formula, h(t)=(-j) k-1 g (k-1) (t),k∈N + is the Gaussian window function, and x(τ) is the analysis signal.

[0080] The third step is to calculate the two-dimensional Fourier transform of the two-dimensional distribution of the signal.

[0081] 1. If the signal is a periodic impact fault signal under constant speed conditions, directly calculate the two-dimensional Fourier transform of its generalized transient compression transform time-frequency distribution. The calculation formula for the two-dimensional Fourier transform of the time-frequency distribution is as follows:

[0082] F(u,z)=∫∫GTST(t,ω)e -j2π(ut+zω) dtdω (6)

[0083] Where GTST(t,ω) is the result of the generalized transient compression transformation in formula (1).

[0084] 2. If the signal is a non-periodic impact fault signal under variable speed conditions, it is necessary to first transform the time-frequency distribution into an angular frequency distribution. The transformation formula from the time-frequency distribution to the angular frequency distribution is as follows:

[0085]

[0086] In the formula, is the angular frequency distribution corresponding to time t. Then the same method is used to calculate the two-dimensional Fourier transform result of the angular frequency distribution.

[0087] The fourth step is to calculate the impact energy interval.

[0088] 1. If the signal is a periodic impact fault signal under constant speed conditions, the impact time interval is calculated as follows:

[0089]

[0090] In the formula, f r is the switching frequency, O is the fault order.

[0091] 2. If the signal is a non-periodic impact fault signal under variable speed conditions, the impact angle interval is calculated as follows:

[0092]

[0093] Where O is the fault order.

[0094] The fifth step is to calculate the periodic impact energy ratio factor.

[0095]

[0096] Where Δ is Δt in equation (8) or The impact interval, F, is the two-dimensional Fourier transform result in equation (6).

[0097] Example 1

[0098] The first step is to obtain the vibration response signal.

[0099] This example simulates the periodic impact signal generated by the outer ring fault of the bearing under constant speed conditions. The speed is set to 480r / min and the fault order is set to 1. Gaussian white noise is used to simulate the vibration response signal under no fault, such as Figure 2 (a) shown. Figure 2 (b)- Figure 2 (d) are the time domain waveforms of the periodic impact signals generated by mild faults, moderate faults and severe faults, respectively. It can be found that as the fault becomes more severe, the impact component in the signal becomes more obvious.

[0100] The second step is to perform time-frequency transformation on the analysis signal.

[0101] Figure 3 The short-time Fourier transform results of the four impact signals are shown in Figure 2. From the time-frequency distribution, it can be seen that the more serious the fault, the more obvious the periodic impact energy in the time-frequency distribution of the signal. In order to obtain a more concentrated time-frequency distribution energy, the generalized transient compression transform is used to rearrange the time-frequency distribution energy, such as Figure 4 shown.

[0102] The third step is to calculate the two-dimensional Fourier transform result of the time-frequency distribution.

[0103] Figure 5 The two-dimensional Fourier transform results of the generalized transient extraction transform time-frequency distribution of the four signals are: Figure 6 This is a partial enlarged view of the two-dimensional Fourier transform results of each time-frequency distribution. It can be found that the more obvious the periodic impact energy in the time-frequency distribution, the stronger the periodicity of its two-dimensional Fourier transform result. Therefore, the periodic impact energy in the signal can be quantified by the periodicity of the energy distribution in the two-dimensional Fourier transform of the time-frequency distribution, thereby quantifying the degree of fault of the rotating machinery.

[0104] The fourth step is to calculate the time interval of the impact characteristics.

[0105] For a constant speed bearing fault, the time interval of the impact feature can be calculated as follows:

[0106]

[0107] In the formula, f r is the switching frequency, O is the fault order.

[0108] The fifth step is to calculate the periodic center energy ratio factor.

[0109] According to formula (10), Figure 6 The periodic impact energy ratio factor of each two-dimensional Fourier transform result in . The results are shown in Table 1. It can be clearly found that as the fault severity increases, the periodic impact energy ratio factor increases.

[0110] Table 1 Periodic impact energy ratio factors of vibration signals with different fault degrees under constant speed conditions

[0111]

[0112] Example 2

[0113] The first step is to obtain the vibration response signal.

[0114] This example simulates the non-periodic impact signal generated by the outer ring fault of the bearing under variable speed conditions. The speed is set to 120t 3 +120t+300r / min, the fault order is set to 1. Gaussian white noise is also used to simulate the vibration response signal without fault, and non-periodic impact signals of different amplitudes are used to simulate mild faults, moderate faults and severe faults respectively. The signal time domain waveform is shown in Figure 7 As shown in Figure 2, it can be found that as the fault severity increases, the impact component in the signal becomes more obvious.

[0115] The second step is to perform time-frequency transformation on the analysis signal.

[0116] Figure 8 The short-time Fourier transform results of the fault impulse signal of four damage degrees can be seen from the time-frequency distribution. It can be seen that with the increase of the speed, the impulse signal features become denser. At the same time, it can be seen that the more serious the fault, the more obvious the periodic impulse energy in the time-frequency distribution of the signal. In order to obtain a more concentrated time-frequency distribution energy, the generalized transient compression transform is also used to rearrange the time-frequency distribution energy, such as Fig. 9 shown.

[0117] The third step is to convert the time-frequency distribution into angular frequency distribution, and then calculate the two-dimensional Fourier transform result of the angular frequency distribution.

[0118] Fig.10 The angular frequency distribution is the result of the time-frequency distribution of the generalized transient extraction transformation of each signal. It can be found that the non-periodic impact energy in the time domain caused by the rotation speed is converted into periodic impact energy in the angle domain. Therefore, in the angular frequency distribution, the impact energy is displayed at equal angle intervals.

[0119] Fig.11 is the two-dimensional Fourier transform result of the angular frequency distribution of each signal, Fig.12 It is a partial enlarged view of the two-dimensional Fourier transform results of each angular frequency distribution. It can also be found that the more obvious the impact energy in the angular frequency distribution, the stronger the periodicity of its two-dimensional Fourier transform result in the angle domain. Therefore, the non-periodic impact energy in the fault signal can also be quantified by the periodicity of the energy distribution in the two-dimensional Fourier transform of the angular frequency distribution, thereby quantifying the fault degree of the rotating machinery under variable speed conditions.

[0120] The fourth step is to calculate the time interval of the impact characteristics.

[0121] For variable speed bearing faults, the angular interval of the impact signature can be calculated as follows:

[0122]

[0123] Where O is the fault order.

[0124] The fifth step is to calculate the periodic center energy ratio factor.

[0125] Also calculated according to formula (10) Fig.12 The periodic center energy proportion factor of the two-dimensional Fourier transform results of each angular frequency distribution is shown in Table 2. It can also be found that the more serious the fault is, the greater the periodic impact energy proportion factor of the angular frequency distribution of its signal is.

[0126] Table 2 Periodic impact energy ratio factors of vibration signals with different fault degrees under variable speed conditions

[0127]

Claims

1. A method for quantifying the degree of rotating machinery fault based on the time-frequency energy of impact characteristics, characterized in that: The steps include: Step 1: Obtain a vibration signal of a target rotating machine; Step 2, using generalized transient compression transform to calculate the time-frequency distribution of the vibration signal; Step 2 is as follows: the time-frequency distribution can simultaneously characterize the time domain and frequency domain information in the vibration signal. The generalized transient compression transform can accurately estimate the complex group delay characteristics of the transient signal. By multiple compressions and rearrangements of the time-frequency distribution energy, a more concentrated time-frequency distribution is obtained. The generalized transient compression transform has the following representation: In the formula, S(τ,ω) represents the time-frequency distribution of the short-time Fourier transform of the signal, δ(·) represents the Dirac function, is the M-iteration result of the generalized group delay estimation operator, and its N-order form is as follows: In the formula, is the generalized group delay estimate, and its explicit expression is as follows: In the formula, Indicates taking the imaginary part of the function, B 1,k is the algebraic cofactor of the generalized time-frequency matrix B, β=σ 2 is the fixed parameter of the frequency domain Gaussian window function, B is the generalized time-frequency matrix; Step 3: If the vibration signal is a periodic impact fault signal under a constant speed condition, proceed to step 4; if the vibration signal is a non-periodic impact fault signal under a variable speed condition, proceed to step 5; Step 4, calculating the two-dimensional Fourier transform of the time-frequency distribution of the generalized transient compression transform of the signal; Step 4 is specifically: calculating the two-dimensional Fourier transform of the time-frequency distribution of the generalized transient compression transform. The calculation formula of the two-dimensional Fourier transform of the time-frequency distribution is as follows: F(u,z)=∫∫GTST(t,ω)e -j2π(ut+zω) dadω (6) Where GTST(t,ω) is the result of the generalized transient compression transformation in formula (1); Step 5: first transform the time-frequency distribution of the signal into an angular frequency distribution, and then calculate the two-dimensional Fourier transform result of the angular frequency distribution of the signal; Step 5 is specifically as follows: first transform the time-frequency distribution into the angular frequency distribution. The transformation formula from the time-frequency distribution to the angular frequency distribution is as follows: In the formula, is the angular frequency distribution corresponding to time t, and then the two-dimensional Fourier transform result of the angular frequency distribution is calculated according to formula (6), IF(t) represents the instantaneous frequency conversion of the signal; Step 6: Calculate the impact energy interval and the periodic impact energy ratio factor, quantify the time-frequency energy of the impact feature, and thus quantify the fault degree of the rotating machinery.

2. The method for quantifying the degree of rotating machinery fault based on the time-frequency energy of impact characteristics according to claim 1 is characterized in that: The construction process of the generalized time-frequency matrix B is as follows: In the formula, is the time-frequency distribution obtained by short-time Fourier transform based on different Gaussian window functions. The calculation process is as follows: In the formula, h(t)=(-j) k-1 g (k-1) (t),k∈N + is the Gaussian window function, x(τ) is the analysis signal, h * It means taking the complex conjugate of a function.

3. The method for quantifying the degree of rotating machinery fault based on the time-frequency energy of impact characteristics according to claim 1 is characterized in that: In step 5, the calculation method of the impact energy interval is: If the signal is a periodic impact fault signal under constant speed conditions, the impact time interval is calculated as follows: In the formula, f r is the switching frequency, O is the fault order; If the signal is a non-periodic impact fault signal under variable speed conditions, the impact angle interval is calculated as follows: Where O is the fault order.

4. The method for quantifying the degree of rotating machinery fault based on the time-frequency energy of impact characteristics according to claim 3 is characterized in that: In step 5, the calculation formula of the periodic impact energy ratio factor is as follows: Where Δ is Δt in equation (8) or The impact interval, F, is the two-dimensional Fourier transform result in equation (6).

5. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the method of claim 1.

6. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

7. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the steps of the method according to claim 1 are implemented.

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