CN115130508A - Rotating machine fault diagnosis method based on time amplitude frequency product entropy - Google Patents
Rotating machine fault diagnosis method based on time amplitude frequency product entropy Download PDFInfo
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Abstract
The invention provides a rotating machinery fault diagnosis method based on time-amplitude-frequency product entropy, which comprises the following steps: step S1, signal acquisition: acquiring a rotary mechanical vibration signal x (t); step S2, signal decomposition: VMD decomposing the signal x (t) to obtain n Intrinsic Mode Function (IMF) components u 1 (t)，u 2 (t)，…，u n (t); step S3, calculating a time-amplitude-frequency product: computing Hilbert spectra of each IMF component, and computing time-amplitude-frequency products sigma of each IMF component 1 ，σ 2 ，…，σ n (ii) a Step S4, normalization processing: for each time amplitude product value sigma j Carrying out normalization processing; step S5, calculating time-amplitude product entropy: calculating the time-amplitude product entropy P of the signal x (t) TEF And based on this, the fault of the rotating machine is diagnosed. The inventionThe method is simple and convenient to operate, the extraction of the correlation characteristics is comprehensive and reliable, and the influence of the factors of the signal is small; the time-frequency resolution capability is good; the method has more accurate signal feature extraction capability.
Description
Technical Field
The invention relates to the technical field of signal decomposition, processing and fault diagnosis of rotating machinery, in particular to a fault diagnosis method of rotating machinery based on time-amplitude-frequency product entropy.
Background
In the field of fault diagnosis of rotary machines, fault feature extraction and selection are always the key points of diagnosis, and the accuracy of fault diagnosis results is directly influenced. As is known, vibration signals acquired by mechanical equipment vary widely and are limited by the influence of factors such as transmission equipment and background noise, and the vibration signals of the mechanical equipment are represented as non-stationary signals, and particularly, the non-stationary characteristics of the signals are more obvious when the equipment fails. Therefore, it is necessary to adopt a method suitable for processing non-stationary signals in the fault diagnosis process, and the Variational Modal Decomposition (VMD) is different from Empirical Modal Decomposition (EMD) or Local Mean Decomposition (LMD), and is a non-recursive signal decomposition method, which essentially solves the optimal solution for the variational constraint.
In the nonlinear signal analysis process, three most important parameters of the signal are time scale and energy and frequency distributed along the time scale. When a rotating machine fails, the energy of the signals in the same frequency band may differ significantly, and the energy of the failure signal may decrease in some frequency bands and increase in some frequency bands. Therefore, the energy and frequency components changing along with time are effective representatives of signal faults, and fault analysis can be carried out according to the change relation among the time, the energy and the frequency of the vibration signals of the rotary machine.
The Hilbert spectrum of the signal can effectively represent the time-frequency-energy change relation in the signal, highlight the local characteristics of the signal, have good time-frequency resolution capability and are less influenced by the signal sampling frequency and background noise factors, so that the method for diagnosing the fault of the rotary machine based on the time-amplitude product entropy is provided for the parameters capable of effectively changing the time-frequency distribution characteristic difference of the signal.
Disclosure of Invention
In view of the above, the invention provides a method for diagnosing faults of a rotating machine based on time-amplitude-frequency product entropy, which not only considers the time-frequency distribution characteristics of signals, but also considers the non-stationary characteristic factors of the analysis signals, and has higher accuracy of feature extraction.
The invention provides a rotating machinery fault diagnosis method based on time-amplitude-frequency product entropy, which comprises the following steps of:
step S1, signal acquisition: acquiring a rotating mechanical vibration signal x (t);
step S2, signal decomposition: VMD decomposing the signal x (t) to obtain n Intrinsic Mode Function (IMF) components u 1 (t)，u 2 (t)，…，u n (t)；
Step S3, calculating a time-amplitude-frequency product: computing Hilbert spectra of each IMF component, and computing time-amplitude-frequency products sigma of each IMF component 1 ，σ 2 ，…，σ n ；
Step S4, normalization processing: for each time amplitude frequency product value sigma j Carrying out normalization processing;
step S5, calculating time-amplitude product entropy: calculating the time-amplitude product entropy P of the signal x (t) TEF And based on this, the fault of the rotating machine is diagnosed.
Further, in step S3, a Hilbert spectrum of each IMF component is calculated, and a time-amplitude product σ of each IMF component is calculated according to formula (1) 1 ，σ 2 ，…，σ n ；
In the formula, σ j (j ═ 1,2, …, n) is the time-amplitude-frequency product of IMF1 to IMFn; s i (i ═ 1,2, …, m) is the energy of each block of the Hilbert spectrum (the whole time-frequency plane is divided equally into m blocks).
Further, in the step S4, the frequency product value σ is obtained for each time amplitude according to the formula (2) j Carrying out normalization processing;
in the formula, E-sigma 1 +σ 2 +…σ n And is and
E k the time-amplitude-frequency product value representing the kth IMF component is a percentage of the total signal time-amplitude-frequency product value.
Further, in the step S5, the time-amplitude product entropy P of the signal x (t) is calculated according to the formula (3) TEF And diagnosing the fault of the rotating machine based on the fault;
the technical scheme of the invention at least comprises the following beneficial effects:
1. due to the influence of factors such as background noise and an acquisition system, signals acquired by the sensor often contain a large amount of noise and have a non-stationary characteristic, the effectiveness of the VMD method for processing the signals is proved, and the accuracy of signal processing can be effectively improved by adopting the non-stationary signal processing method in the rotary fault diagnosis process;
2. the time-amplitude-frequency product can effectively reflect the change relation among time, energy and frequency, highlight the local characteristics of signals, and has good time-frequency resolution capability, the time-amplitude-frequency product entropy fully utilizes the advantages of the entropy value in signal information evaluation, and simultaneously avoids the influence of similarity among fault characteristics, so that the internal characteristics of the signals can be more effectively described, and the more accurate signal characteristic extraction capability is realized;
3. compared with a signal fault characteristic diagnosis method which needs a large amount of priori knowledge, the method provided by the invention is simple and convenient to operate, comprehensive and reliable in relevant characteristic extraction, and is slightly influenced by the factors of the signal.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a diagram of a PQZZ-II type mechanical failure comprehensive simulation experiment table employed in the embodiment of the present invention;
FIG. 3 is a gear fault signal map collected in an embodiment of the present invention at different states;
fig. 4 shows VMD decomposition results of the tooth-broken fault signal and frequency spectrums of the IMF components according to the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to fig. 1 to 4 of the embodiments of the present invention. It should be apparent that the described embodiments are only some of the embodiments of the present invention, and not all of them. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
Gears and rolling bearings are the most common parts in rotating machinery. Because the working environment is severe, the above parts are easy to damage, if they fail and cannot be checked in time, the working performance of the whole equipment will be affected, and huge economic loss is caused. Statistically, among the causes of failure of the rotary machine, the failure due to the gears and the rolling bearings accounts for 70% or more. Therefore, how to rapidly and accurately extract the fault characteristics of the parts and further perform effective fault diagnosis is always a key point and a difficulty point of research in the field of fault diagnosis of rotary machines.
Example 1
As shown in fig. 1, the present embodiment provides a method for diagnosing a fault of a rotating machine based on time-amplitude-frequency product entropy, which includes the following steps:
step S1, signal acquisition: a rotating mechanical vibration signal x (t) is acquired.
Step S2, signal decomposition: VMD decomposing the signal x (t) to obtain n Intrinsic Mode Function (IMF) components u 1 (t)，u 2 (t)，…，u n (t)。
Step S3, calculating a time-amplitude-frequency product: computing Hilbert spectra of IMF components, andcalculating the time-amplitude-frequency product sigma of each IMF component according to the formula (1) 1 ，σ 2 ，…，σ n ；
In the formula, σ j (j ═ 1,2, …, n) is the time-amplitude-frequency product of IMF1 to IMFn; s i (i ═ 1,2, …, m) is the energy of each time-frequency plane of the Hilbert spectrum (the whole time-frequency plane is divided into m blocks on average).
Step S4, normalization processing: for each time amplitude product value sigma according to formula (2) j Carrying out normalization processing;
wherein E ═ σ 1 +σ 2 +…σ n And is and
E k the time-amplitude-frequency product value representing the kth IMF component is a percentage of the total signal time-amplitude-frequency product value.
Step S5, calculating time-amplitude product entropy: calculating the time-amplitude product entropy P of the signal x (t) according to equation (3) TEF And diagnosing the fault of the rotating machine based on the fault;
the steps of this embodiment are scientific and reasonable, and it is safe convenient to use.
To verify the effectiveness of this embodiment, a validation experiment was conducted to simulate a fault in a PQZZ-II type mechanical fault simulation integrated test rig of the prior art that simulates gear tooth breakage, pitting, wear faults, and faults in the inner and outer rings and the rolling elements of a rolling bearing, as shown in fig. 2.
During fault simulation, one tooth of the driven gear is broken, and a broken line is positioned near a reference circle and used for simulating the fault of the broken tooth; the tooth surface of the other driven gear is subjected to spot welding to form a pit for simulating medium-level pitting failure; the drive gear flanks were ground to simulate flank wear failure.
In the experimental process, the rotation frequency of the motor is 50Hz, the data acquisition system is built on the basis of an ADA16-8/2(LPCI) type high-speed multifunctional acquisition card, a KD1001L type piezoelectric acceleration sensor is adopted as the sensor, and the acceleration sensor is arranged in the X direction of a bearing of an output shaft of a gearbox respectively. The signal synchronous sampling is carried out, the sampling frequency is 5120Hz, the sampling time length is 15s, and the analysis time length is 1 s. The rotating speed of the motor is 1470 r/min. FIG. 3 is a graph of measured gear failure signals for different conditions.
VMD decomposition is carried out on the signals to obtain different IMF components, and the VMD decomposition result of the gear vibration signal with the tooth breakage fault and the frequency spectrum of each IMF component are listed in figure 4. As can be seen from fig. 4, the VMD decomposition result is relatively reasonable, each IMF component is mainly concentrated near the center frequency, and no obvious frequency dispersion phenomenon occurs, which indicates that the proposed parameter selection method can effectively suppress the problems of end effect, mode aliasing, and the like generated in the decomposition process, reduce information leakage among the mode components, and provide effective guarantee for extracting subsequent signal features.
The time-amplitude product entropy of the gear vibration signal under different conditions is calculated according to the steps S3-S5 of the embodiment, and the result is shown in Table 1.
TABLE 1 time amplitude frequency entropy of gear vibration signal (sampling frequency 5120Hz)
Example 2
The procedure of this example is the same as example 1.
During the course of the validation experiment, the only difference from example 1 was: the sampling frequency is 10240 Hz.
The time-amplitude product entropy of the gear vibration signal in different states is calculated according to steps S3-S5, and the result is shown in Table 2.
TABLE 2 time amplitude product entropy of gear vibration signal (sampling frequency 10240Hz)
Example 3
The procedure of this example is the same as example 1.
During the course of the validation experiment, the only difference from example 1 was: the sampling frequency is 15360 Hz.
The time-amplitude product entropy of the gear vibration signal in different states is calculated according to steps S3-S5, and the result is shown in Table 3.
TABLE 3 time amplitude frequency entropy of the gear vibration signal (sampling frequency 10240Hz)
As can be seen from the data in the tables 1,2 and 3, the time-amplitude-frequency product entropy is very sensitive to various faults of the gear, and the entropy value of the same fault is basically unchanged under different sampling frequencies and at the same rotating speed; under the same rotating speed, when the sampling frequency is 5120Hz, 10240Hz and 15360Hz respectively, the time amplitude frequency value entropy sequence of the gear vibration signal under 6 states is also consistent, namely normal, pitting, abrasion, tooth breakage, abrasion-pitting and abrasion-tooth breakage. The fault type of the gear can be accurately judged through the comparison of the magnitude of the time amplitude frequency product entropy, so that the fault type can be used for fault diagnosis of the gear as a characteristic quantity of the fault diagnosis of the gear.
The foregoing is a preferred embodiment of the present invention, and it should be noted that it would be apparent to those skilled in the art that various modifications and enhancements can be made without departing from the principles of the invention, and such modifications and enhancements are also considered to be within the scope of the invention.
Claims (4)
1. A rotating machinery fault diagnosis method based on time amplitude frequency product entropy is characterized by comprising the following steps:
step S1, signal acquisition: acquiring a rotary mechanical vibration signal x (t);
step S2, signal decomposition: VMD decomposition is carried out on the signal x (t) to obtain n Intrinsic Mode Function (IMF) components u 1 (t)，u 2 (t)，…，u n (t)；
Step S3, calculating a time-amplitude-frequency product: computing Hilbert spectra of each IMF component, and computing time-amplitude-frequency products sigma of each IMF component 1 ，σ 2 ，…，σ n ；
Step S4, normalization processing: for each time amplitude frequency product value sigma j Carrying out normalization processing;
step S5, calculating time-amplitude product entropy: calculating the time-amplitude product entropy P of the signal x (t) TEF And based on this, the fault of the rotating machine is diagnosed.
2. A method for diagnosing faults of a rotating machine based on time-amplitude-frequency product entropy as claimed in claim 1, wherein in step S3, Hilbert spectrum of each IMF component is calculated, and time-amplitude-frequency product σ of each IMF component is calculated according to formula (1) 1 ，σ 2 ，…，σ n ；
In the formula, σ j (j ═ 1,2, …, n) is the time-amplitude-frequency product of IMF1 to IMFn; s. the i (i ═ 1,2, …, m) is the energy of each block of the Hilbert spectrum (the whole time-frequency plane is divided equally into m blocks).
3. A method for diagnosing a malfunction of a rotary machine based on time-amplitude-frequency product entropy as claimed in claim 1, wherein in step S4, the time-amplitude-frequency product value σ is calculated for each time-amplitude-frequency product value according to equation (2) j Carrying out normalization processing;
wherein E ═ σ 1 +σ 2 +…σ n And is and
E k the time-amplitude-frequency product value representing the kth IMF component is a percentage of the total signal time-amplitude-frequency product value.
4. A method for diagnosing faults of rotating machinery based on time-amplitude product entropy as claimed in claim 1, wherein in step S5, the time-amplitude product entropy P of signal x (t) is calculated according to formula (3) TEF And diagnosing the fault of the rotating machine based on the fault;
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| CN117668623A (en) * | 2024-02-02 | 2024-03-08 | 中国海洋大学 | Multi-sensor cross-domain fault diagnosis method for leakage of ship pipeline valve |
| CN117668623B (en) * | 2024-02-02 | 2024-05-14 | 中国海洋大学 | Multi-sensor cross-domain fault diagnosis method for leakage of ship pipeline valve |
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