A Roots compressor fault diagnosis method and system

Abstract

The present invention provides a Roots compressor fault diagnosis method, comprising obtaining an original vibration signal transmitted from the Roots compressor; constructing a weighted filter signal based on the original vibration signal using a preset weighted cepstrum editing method, taking the inverse of the smoothness index of the filter signal as the objective function and finding an optimal solution; and further updating the weight values ​​in the filter signal using the found optimal solution; plotting the Hilbert envelope spectrum of the updated filter signal, and performing characteristic frequency analysis on the Hilbert envelope spectrum to diagnose the Roots compressor fault. The present invention effectively improves the signal-to-noise ratio to extract fault characteristic information, allowing for precise diagnosis of the fault type and improving diagnostic accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of mechanical equipment fault diagnosis, and in particular to a Roots-type compressor fault diagnosis method and system. Background Art

[0002] Roots compressors are rotary machines that rely on the relative rotation of two impellers to continuously compress and transport gas. Due to their stable operating characteristics, high volumetric efficiency, and strong adaptability, they are widely used in evaporation processes in industries such as electronics, petroleum, steel, building materials, chemicals, food, medical treatment, and papermaking.

[0003] As core equipment in the evaporation process, Roots compressors operate under complex conditions such as high speed and high temperature for extended periods, making them highly susceptible to damage and failure. Common Roots compressor failure modes include damage to key, vulnerable parts such as the impeller, bearings, gears, and drive shaft; loose anchor bolts; motor winding shorts, winding grounding or interphase shorts; broken rotor bars; and abnormal leakage.

[0004] Under harsh operating conditions, bearings, gears, impellers, and other components on Roots compressors often wear, crack, or even break. If these faults are not discovered and addressed promptly, they can hinder the compressor's operation, potentially causing significant economic losses or even serious safety incidents. Therefore, real-time monitoring and diagnosis of the operating status and performance of bearings, gears, and impellers in Roots compressors is crucial.

[0005] When Roots compressor components (such as bearings, gears, and impellers) are damaged, they typically generate periodic shocks, which often include these periodic shocks in the collected vibration signals. However, the early signs of Roots compressor failures are subtle and easily masked by noise. Furthermore, due to the compressor's complex structure and the interplay between its components, the signal-to-noise ratio of the collected vibration signals is low.

[0006] Currently, fault signature extraction for vibration signals in Roots compressors relies solely on cepstrum pre-whitening. This method suppresses low-frequency interference and enhances fault signatures by setting the amplitudes of all cepstrum frequencies in the real cepstrum to zero and then reconstructing the filtered signal with the phase spectrum. Cepstrum pre-whitening always sets the amplitudes of all cepstrum frequencies to zero, which typically enhances both noise and fault signatures in the signal, failing to effectively filter out noise. Fault signature information is often unclear in the envelope spectrum of the filtered signal, leading to significant errors in diagnostic results and making it difficult to use in engineering practice.

[0007] Therefore, it is necessary to use a new method in the fault diagnosis of Roots compressors to extract fault feature information by effectively improving the signal-to-noise ratio to accurately diagnose the fault type and thus improve the diagnostic accuracy. Summary of the Invention

[0008] The technical problem to be solved by the embodiments of the present invention is to provide a Roots compressor fault diagnosis method and system, which effectively improves the signal-to-noise ratio to extract fault feature information to accurately diagnose the fault type, thereby improving the diagnostic accuracy.

[0009] In order to solve the above technical problems, an embodiment of the present invention provides a Roots compressor fault diagnosis method, which includes the following steps:

[0010] Obtain the original vibration signal transmitted from the Roots compressor;

[0011] According to the original vibration signal, a preset weighted cepstrum editing method is used to construct a filter signal containing weights, and the inverse of the smoothness index of the filter signal is used as the objective function to find the optimal solution, and the optimal solution is used to update the weight value in the filter signal;

[0012] The Hilbert envelope spectrum of the updated filtered signal is plotted, and characteristic frequency analysis is performed on the Hilbert envelope spectrum to diagnose a fault condition of the Roots-type compressor.

[0013] The specific steps of constructing a weighted filter signal based on the original vibration signal using a preset weighted cepstrum editing method, taking the inverse of the smoothness index of the filter signal as the objective function and finding the optimal solution, and further updating the weight value in the filter signal using the found optimal solution include:

[0014] The first step is to set the original vibration signal x(n) with a signal length of N;

[0015] The second step is to transform the original vibration signal x(n) into The filtered signal x constructed based on the weighted cepstrum editing method is w (n,w) is expressed as shown in the following formula (1):

[0016]

[0017] Among them, F -1 represents the inverse Fourier transform, A(f) is the amplitude, is the phase, w represents the weight;

[0018] The third step is to determine the inverse of the smoothness index of the filtered signal, which is expressed as shown in the following formula (2):

[0019]

[0020] Wherein, SE(n) is the square envelope of the original vibration signal x(n), and SE(n)=|x(n)+j·Hilbert{x(n)}| 2 , Hilbert is the Hilbert transform;

[0021] The fourth step is to construct the objective function, as shown in the following formula (3):

[0022]

[0023] Among them, SE w (n) is the filtered signal x w The squared envelope of (n,w);

[0024] Step 5: Calculate the optimal solution w of the objective function through the preset genetic algorithm best ;

[0025] Step 6: The optimal solution w of the objective function best Input formula (1) to update the filtered signal x w The value of weight w in (n,w) to obtain the updated filtered signal x w (n,w best );in,

[0026] The specific steps of calculating the optimal solution of the objective function by using a preset genetic algorithm include:

[0027] 1) Initial population:

[0028] Genetic algorithm is used to encode the initial value of weight w to obtain the initial population; the boundary of weight w is set to [-2, 2], and the population size is 200;

[0029] 2) Calculate fitness:

[0030] Calculate the fitness value of each individual in the population; the fitness value is the standard for judging the quality of the solution, and the corresponding fitness function is set as shown in the following formula (4):

[0031]

[0032] 3) Select:

[0033] Use roulette wheel method to select good individuals;

[0034] 4) Crossover:

[0035] Exchange certain genes between two paired chromosomes to form two new individuals; the crossover fraction is set to 0.8;

[0036] 5) Mutation:

[0037] Mutation generates new individuals; the crossover fraction is set to 0.02;

[0038] 6) Calculate the fitness value and check whether the conditions are met. If not, go to step 3) and continue the next round of genetic operation;

[0039] 7) If the conditions are met, the optimal solution output is the optimal weight value w in the weighted inverse spectrum editing method best .

[0040] The fault conditions of the Roots compressor include bearing inner ring fault, bearing outer ring fault, rolling element fault, impeller meshing and gear fault.

[0041] An embodiment of the present invention further provides a Roots compressor fault diagnosis system, comprising:

[0042] A signal acquisition unit is used to acquire the original vibration signal transmitted from the Roots compressor;

[0043] a signal feature extraction unit, configured to construct a weighted filtered signal based on the original vibration signal using a preset weighted cepstrum editing method, and to use the inverse of the smoothness index of the filtered signal as an objective function to find an optimal solution, and further update the weight value in the filtered signal using the found optimal solution;

[0044] The fault diagnosis unit is used to draw the Hilbert envelope spectrum of the updated filtered signal and perform characteristic frequency analysis on the Hilbert envelope spectrum to diagnose the fault of the Roots compressor.

[0045] The fault conditions of the Roots compressor include bearing inner ring fault, bearing outer ring fault, rolling element fault, impeller meshing and gear fault.

[0046] The implementation of the embodiments of the present invention has the following beneficial effects:

[0047] On the one hand, the present invention uses the inverse of the smoothness index of the filtered signal as the objective function. Based on its insensitivity to outliers, it can accurately measure the signal-to-noise ratio of the signal; on the other hand, it uses a genetic algorithm to adaptively weight the weighted inverse spectrum editing method, fully suppressing the interference of noise and enhancing the fault characteristics, thereby achieving the purpose of extracting fault feature information by effectively improving the signal-to-noise ratio to accurately diagnose the fault type, thereby improving the diagnostic accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] 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 description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, without paying any creative work, other drawings obtained based on these drawings still fall within the scope of the present invention.

[0049] Figure 1 A flow chart of a Roots compressor fault diagnosis method provided by an embodiment of the present invention;

[0050] Figure 2 A diagram showing the results of analyzing a Roots compressor impeller fault signal using an envelope analysis method in an application scenario of a Roots compressor fault diagnosis method provided by an embodiment of the present invention;

[0051] Figure 3 A diagram showing the results of analyzing a Roots compressor impeller fault signal using a traditional cepstrum pre-whitening method in an application scenario of a Roots compressor fault diagnosis method provided by an embodiment of the present invention;

[0052] Figure 4 A diagram showing the results of analyzing a Roots compressor impeller fault signal using the method of the embodiment of the present invention in an application scenario of a Roots compressor fault diagnosis method provided by an embodiment of the present invention;

[0053] Figure 5 A schematic structural diagram of a Roots compressor fault diagnosis system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in further detail below with reference to the accompanying drawings.

[0055] like Figure 1 As shown in the figure, a Roots compressor fault diagnosis method is provided in an embodiment of the present invention, and the method includes the following steps:

[0056] Step S1: obtaining an original vibration signal transmitted from a Roots compressor;

[0057] The specific process is to fix the acceleration sensor on the Roots compressor and connect it to the multi-channel data acquisition and analyzer, so that the multi-channel data acquisition and analyzer can obtain the original vibration signal transmitted by the Roots compressor in real time.

[0058] Step S2: constructing a weighted filter signal based on the original vibration signal using a preset weighted cepstrum editing method, taking the inverse of the smoothness index of the filter signal as the objective function and finding an optimal solution, and further updating the weight value in the filter signal using the found optimal solution;

[0059] The specific process is as follows: Step 1, set the original vibration signal x(n) with a signal length of N;

[0060] The second step is to transform the original vibration signal x(n) into The filtered signal x constructed based on the weighted cepstrum editing method is w (n,w) is expressed as shown in the following formula (1):

[0061]

[0062] Among them, F -1 represents the inverse Fourier transform, A(f) is the amplitude, is the phase, w represents the weight;

[0063] The third step is to determine the inverse of the smoothness index of the filtered signal, which is expressed as shown in the following formula (2):

[0064]

[0065] Where SE(n) is the square envelope of the original vibration signal x(n), and SE(n) = |x(n) + j·Hilbert{x(n)}| 2 , Hilbert is the Hilbert transform;

[0066] The fourth step is to construct the objective function, as shown in the following formula (3):

[0067]

[0068] Among them, SE w (n) is the filtered signal x w The squared envelope of (n,w);

[0069] Step 5: Calculate the optimal solution w of the objective function through the preset genetic algorithm best ;

[0070] Step 6: The optimal solution w of the objective function best Input formula (1) to update the filtered signal x w The value of weight w in (n,w) to obtain the updated filtered signal x w (n,w best );in,

[0071] It should be noted that the specific steps for calculating the optimal solution of the objective function through the preset genetic algorithm include:

[0072] 1) Initial population:

[0073] Genetic algorithm is used to encode the initial value of weight w to obtain the initial population; the boundary of weight w is set to [-2, 2], and the population size is 200;

[0074] 2) Calculate fitness:

[0075] Calculate the fitness value of each individual in the population; the fitness value is the standard for judging the quality of the solution, and the corresponding fitness function is set as shown in the following formula (4):

[0076]

[0077] 3) Select:

[0078] Use the roulette wheel method to select excellent individuals; it should be noted that individuals with larger fitness values ​​are more likely to be selected;

[0079] 4) Crossover:

[0080] Exchange certain genes between two paired chromosomes to form two new individuals; the crossover fraction is set to 0.8; it should be noted that crossover refers to exchanging certain genes between two paired chromosomes to form two new individuals; crossover is the main method of generating new individuals in genetic algorithms;

[0081] 5) Mutation:

[0082] Mutation generates new individuals; the crossover fraction is set to 0.02; it should be noted that mutation, i.e. replacing the gene values ​​of certain loci in the coding chain of an individual's chromosome with other alleles of the loci, leads to the formation of new individuals; mutation is an auxiliary method for generating new individuals;

[0083] 6) Calculate the fitness value and check whether the conditions are met. If not, go to step 3) and continue the next round of genetic operation;

[0084] 7) If the conditions are met, the optimal solution output is the optimal weight value w in the weighted inverse spectrum editing method best .

[0085] Step S3: plotting the Hilbert envelope spectrum of the updated filtered signal, and performing characteristic frequency analysis on the Hilbert envelope spectrum to diagnose a fault condition of the Roots compressor.

[0086] The specific process is to update the filtered signal x in the multi-channel data acquisition analyzer. w (n,wbest ) to perform Hilbert envelope demodulation to diagnose gear tooth faults in Roots-type compressors. Roots-type compressor faults include, but are not limited to, bearing inner race faults, bearing outer race faults, rolling element faults, impeller meshing, and gear faults.

[0087] It should be noted that the diagnosis of the gear tooth fault of the Roots compressor is carried out by analyzing the filtered signal x after Hilbert envelope demodulation. w (n,w best ) appears at a frequency that matches the theoretical fault characteristic frequency; if so, a fault conclusion is drawn; otherwise, a normal conclusion is drawn.

[0088] Among them, the fault characteristic frequencies of the Roots compressor are: bearing inner ring fault frequency, outer ring fault frequency, rolling element fault frequency, impeller meshing frequency and gear fault frequency.

[0089] The outer race fault characteristic frequency BPFO is The inner race fault characteristic frequency BPFI is The rolling element fault characteristic frequency BSF is The impeller meshing frequency IEF is IEF=N IEF f shaft ; Gear fault characteristic frequency GF is GF = f shaft .

[0090] Where N r Indicates the number of rolling elements, f shaft Indicates the shaft speed, R d Indicates the roller diameter, P d represents the pitch diameter, α represents the contact angle, N IEF Indicates the number of impeller teeth.

[0091] like Figures 2 to 4 As shown, the application scenario of a Roots compressor fault diagnosis method provided in an embodiment of the present invention is further described as follows:

[0092] The faulty impeller of a Roots compressor is taken as an example for analysis. The number of teeth on the compressor impeller is 3, the number of teeth on the gear is 24, and the rotation frequency of the drive shaft is 30 Hz. Based on the relevant parameters of the compressor structure and the compressor bearings, the theoretical fault characteristic frequency of the compressor can be calculated, as shown in Table 1.

[0093] Table 1 Compressor fault characteristic frequency

[0094] Fault type BPFO BPFI BSF IEF GF Frequency value (Hz) 183.09 296.9 119.41 90 30

[0095] The envelope analysis of the Roots compressor impeller fault signal is performed, and its envelope spectrum is as follows: Figure 2 As shown. Figure 2The characteristic frequency of the impeller fault cannot be clearly identified. Figure 3 The result diagram of the traditional application of the cepstrum pre-whitening method to the fault signal of the Roots compressor impeller is given. It can be seen from the figure that the characteristic frequency of the impeller fault is not displayed. Figure 4 The result of applying the present invention to the fault signal of the impeller of a Roots compressor is shown in the figure. It can be seen from the figure that the characteristic frequency of the impeller fault (90.6Hz) and its double frequency (181.3Hz) and quadruple frequency (359.4Hz) are clearly displayed, so it can be judged that the impeller of the Roots compressor has a fault. Figure 2 、 Figure 3 and Figure 4 The comparison verifies the effectiveness of the method of the present invention.

[0096] like Figure 5 As shown in the figure, a Roots compressor fault diagnosis system is provided in an embodiment of the present invention, comprising:

[0097] The signal acquisition unit 110 is used to acquire the original vibration signal transmitted from the Roots compressor;

[0098] The signal feature extraction unit 120 is configured to construct a weighted filtered signal based on the original vibration signal using a preset weighted cepstrum editing method, and to use the inverse of the smoothness index of the filtered signal as an objective function to find an optimal solution, and further update the weight value in the filtered signal using the found optimal solution;

[0099] The fault diagnosis unit 130 is configured to plot the Hilbert envelope spectrum of the updated filtered signal and perform characteristic frequency analysis on the Hilbert envelope spectrum to diagnose a fault condition of the Roots compressor.

[0100] The fault conditions of the Roots compressor include bearing inner ring fault, bearing outer ring fault, rolling element fault, impeller meshing and gear fault.

[0101] The implementation of the embodiments of the present invention has the following beneficial effects:

[0102] On the one hand, the present invention uses the inverse of the smoothness index of the filtered signal as the objective function. Based on its insensitivity to outliers, it can accurately measure the signal-to-noise ratio of the signal; on the other hand, it uses a genetic algorithm to adaptively weight the weighted inverse spectrum editing method, fully suppressing the interference of noise and enhancing the fault characteristics, thereby achieving the purpose of extracting fault feature information by effectively improving the signal-to-noise ratio to accurately diagnose the fault type, thereby improving the diagnostic accuracy.

[0103] It is worth noting that in the above system embodiment, the various units included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0104] Those skilled in the art will understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0105] The above disclosure is merely a preferred embodiment of the present invention and certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A Roots compressor fault diagnosis method, characterized in that: The method comprises the following steps: Obtain the original vibration signal transmitted from the Roots compressor; According to the original vibration signal, a preset weighted cepstrum editing method is used to construct a filter signal containing weights, and the inverse of the smoothness index of the filter signal is used as the objective function to find the optimal solution, and the optimal solution is used to update the weight value in the filter signal; plotting a Hilbert envelope spectrum of the updated filtered signal and performing characteristic frequency analysis on the Hilbert envelope spectrum to diagnose a fault condition of the Roots-type compressor; The specific steps of constructing a weighted filter signal based on the original vibration signal using a preset weighted cepstrum editing method, taking the inverse of the smoothness index of the filter signal as the objective function and finding an optimal solution, and further updating the weight value in the filter signal using the found optimal solution include: The first step is to set the original vibration signal , the signal length is ; The second step is to convert the original vibration signal The Fourier transform is , then the filtered signal constructed based on the weighted cepstrum editing method is It is expressed as the following formula (1): （1）； in, represents the inverse Fourier transform, is the amplitude, is the phase, represents weight; The third step is to determine the inverse of the smoothness index of the filtered signal, which is expressed as shown in the following formula (2): （2）； in, is the original vibration signal The square envelope of , is the Hilbert transform; The fourth step is to construct the objective function, as shown in the following formula (3): （3）； in, is the filtered signal The square envelope of Step 5: Calculate the optimal solution of the objective function through the preset genetic algorithm ; Step 6: The optimal solution of the objective function Input formula (1) to update the filtered signal Medium Weight The value of , to get the updated filtered signal ;in, .

2. The Roots compressor fault diagnosis method according to claim 1, wherein: The specific steps of calculating the optimal solution of the objective function by using a preset genetic algorithm include: 1) Initial population: Using genetic algorithm to weight The initial value of is encoded to obtain the initial population; among them, the weight The boundary of is set to [-2, 2] and the population size is 200; 2) Calculate fitness: Calculate the fitness value of each individual in the population; the fitness value is the standard for judging the quality of the solution, and the corresponding fitness function is set as shown in the following formula (4): （4）； 3) Select: Use roulette wheel method to select good individuals; 4) Crossover: Exchange certain genes between two paired chromosomes to form two new individuals; the crossover fraction is set to 0.8; 5) Mutation: Mutation generates new individuals; the crossover fraction is set to 0.02; 6) Calculate the fitness value and check whether the conditions are met. If not, go to step 3) and continue the next round of genetic operation; 7) If the conditions are met, the optimal solution output is the optimal weight value in the weighted inverse spectrum editing method .

3. The Roots compressor fault diagnosis method according to claim 1, wherein: The failure conditions of the Roots-type compressor include bearing inner race failure, bearing outer race failure, rolling element failure, impeller meshing and gear failure.

4. A Roots compressor fault diagnosis system, characterized in that: include: A signal acquisition unit is used to acquire the original vibration signal transmitted from the Roots compressor; a signal feature extraction unit, configured to construct a weighted filtered signal based on the original vibration signal using a preset weighted cepstrum editing method, and to use the inverse of the smoothness index of the filtered signal as an objective function to find an optimal solution, and further update the weight value in the filtered signal using the found optimal solution; a fault diagnosis unit, configured to plot a Hilbert envelope spectrum of the updated filtered signal and perform characteristic frequency analysis on the Hilbert envelope spectrum to diagnose a fault condition of the Roots-type compressor; The specific steps of constructing a weighted filter signal based on the original vibration signal using a preset weighted cepstrum editing method, taking the inverse of the smoothness index of the filter signal as the objective function and finding an optimal solution, and further updating the weight value in the filter signal using the found optimal solution include: The first step is to set the original vibration signal , the signal length is ; The second step is to convert the original vibration signal The Fourier transform is , then the filtered signal constructed based on the weighted cepstrum editing method is It is expressed as the following formula (1): （1）； in, represents the inverse Fourier transform, is the amplitude, is the phase, represents weight; The third step is to determine the inverse of the smoothness index of the filtered signal, which is expressed as shown in the following formula (2): （2）； in, is the original vibration signal The square envelope of , is the Hilbert transform; The fourth step is to construct the objective function, as shown in the following formula (3): （3）； in, is the filtered signal The square envelope of Step 5: Calculate the optimal solution of the objective function through the preset genetic algorithm ; Step 6: The optimal solution of the objective function Input formula (1) to update the filtered signal Medium Weight The value of , to get the updated filtered signal ;in, .

5. The Roots compressor fault diagnosis system according to claim 4, characterized in that: The failure conditions of the Roots-type compressor include bearing inner race failure, bearing outer race failure, rolling element failure, impeller meshing and gear failure.

Images