CN112378633A - Mechanical fault diagnosis method - Google Patents
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Abstract
The invention discloses a mechanical fault diagnosis method, which comprises the steps of firstly, collecting vibration signals of a monitored object, and carrying out time-frequency distribution processing on the collected vibration signals to obtain vibration signal time-frequency distribution results; then, on the frequency domain, dividing the vibration signal time-frequency distribution result into a plurality of frequency domain segments, and on the time domain, dividing the vibration signal time-frequency distribution result into a plurality of time domain segments according to the running speed of the monitored object, so that the vibration signal time-frequency distribution result forms a plurality of grids; then calculating the amplitude density in each grid of the vibration signal time-frequency distribution result; and if the amplitude density in at least one grid exceeds a preset threshold value, judging that the monitored object is abnormal. The mechanical fault diagnosis method provided by the invention can be used for carrying out mechanical fault diagnosis based on time-frequency analysis, can reflect the real mechanical fault condition of equipment or a structure in time, and ensures the use safety of the equipment or the structure.
Description
Technical Field
The invention relates to an automatic detection technology, in particular to a mechanical fault diagnosis method.
Background
Mechanical failure diagnosis of a device or structure is an important issue concerning life and production safety. In order to analyze the state of equipment or structures before a fault and to reduce the risk and maintenance costs associated with the fault, methods of signal acquisition and analysis are increasingly used for operating state monitoring.
The traditional signal analysis method mainly adopts a Fourier transform method to obtain frequency components in signals, however, most of the signals in the using process of equipment or a structure are time-varying signals, the Fourier transform cannot reflect the rule that the frequency of the signals changes along with time, and the method can describe the time-varying frequency characteristics of the signals and obtain the state of the equipment or the structure through analyzing the time-frequency distribution of the signals. Chinese patent document CN10681552A discloses a method for analyzing a time-frequency analysis result of a signal by a full-width-at-half-maximum energy ratio of a main frequency, so as to obtain a detection result of an object to be detected. However, in the using process of the equipment or the structure, the main frequency may be generated in the normal using process, and the real fault or abnormal information is hidden in other non-main frequency characteristics, in such a case, the full-width half maximum energy ratio of the main frequency cannot reflect the real mechanical fault condition of the equipment or the structure, and when the fault condition is degraded to be presented in the main frequency form, the dangerous stage of rapid damage is often reached, which is extremely disadvantageous to the guarantee of the use safety.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a mechanical fault diagnosis method, which is used for carrying out mechanical fault diagnosis based on time-frequency analysis, can reflect the real mechanical fault condition of equipment or a structure in time and ensures the use safety of the equipment or the structure.
In order to solve the technical problem, the mechanical fault diagnosis method provided by the invention comprises the following steps:
firstly, acquiring vibration signals of a monitored object, and performing time-frequency distribution processing on the acquired vibration signals to obtain vibration signal time-frequency distribution results;
on the frequency domain, dividing the vibration signal time-frequency distribution result into a plurality of frequency domain sections; in the time domain, dividing the vibration signal time-frequency distribution result into a plurality of time domain sections according to the running speed of the monitored object, so that the vibration signal time-frequency distribution result forms a plurality of grids;
calculating the amplitude density in each grid of the vibration signal time-frequency distribution result;
if the amplitude density in at least one grid exceeds a preset threshold value, performing a fifth step; otherwise, judging that the monitored object is normal, and ending;
and fifthly, judging that the object is abnormal, and ending.
Preferably, in the second step, in the frequency domain, according to the theoretical fault frequency obtained by theoretical calculation, the signal time-frequency distribution result in the effective frequency range is divided into a plurality of frequency domain segments according to the set proportion of the theoretical fault frequency bandwidth, and the remaining frequency domain segments which are not completely divided are merged into the previous frequency domain segment or are independently used as one frequency domain segment.
Preferably, in the second step, in the frequency domain, a set multiple of 0hz to the maximum theoretical fault frequency obtained by theoretical calculation is used as a first frequency domain segment, the time-frequency distribution result is equally divided by the width of the first frequency domain segment in the effective frequency range of the signal, the frequency domain segment is used as a last frequency domain segment if the frequency distribution result cannot be completely divided and the bandwidth of the remaining frequency domain segment exceeds 50% of the maximum theoretical fault frequency, and the last frequency domain segment is merged into the last frequency domain segment if the frequency distribution result does not exceed 50% of the maximum theoretical fault frequency;
the set multiple is greater than 1 and less than 1.8.
Preferably, the set multiple is 1.1, 1.5 or 1.7.
Preferably, the theoretical fault frequency is calculated by using basic operation information.
Preferably, the operation basic information includes at least one of a rotation speed, a rated operation speed, and a load size.
Preferably, in the second step, the time-frequency distribution results of the acceleration operation segment, the constant speed segment and the deceleration operation segment of the monitored object are divided in the time domain.
Preferably, the constant speed section is a constant speed running section or a static section.
Preferably, in the third step, the amplitude density in each grid of the vibration signal time-frequency distribution result is calculated, and the total amplitude is calculated:
wherein, TijIs the total amplitude of the ith time domain segment and the jth frequency domain segment, Aij(t, f) is the amplitude of the time-frequency distribution in the ith time domain segment and the jth frequency domain segment, the variable in the time domain is represented by t, and the variation range in the ith time domain segment is ti+1 to ti+1The variation in the frequency domain is denoted by f, and the variation range in the j-th frequency domain segment is fj+1 to fj+1。
Then calculating the amplitude density D in each divided gridij：
Preferably, in the fifth step, for the case that the abnormality is determined to exist, searching a local maximum value of the amplitude value in the time-frequency distribution result, recording frequency information corresponding to the local maximum value, and determining a spatial position corresponding to the maximum value of the amplitude value according to the time information and the running speed information;
and then, analyzing the mechanical fault condition according to the recorded frequency information corresponding to the local maximum value and the spatial position corresponding to the maximum value of the amplitude.
Preferably, in the first step, the adopted signal time-frequency distribution processing method is continuous wavelet transform, discrete wavelet transform, wavelet packet transform, wigner distribution, pseudo wigner distribution, smooth pseudo wigner distribution, hilbert-yellow transform or S-transform.
Preferably, the object to be monitored is an elevator hoisting machine.
The mechanical fault diagnosis method provided by the invention has the advantages that the mechanical fault diagnosis is carried out based on time-frequency analysis, a plurality of grids are formed on the vibration signal time-frequency distribution result by dividing the time domain and the frequency domain, the state of the monitored object is judged according to the amplitude density in each grid of the vibration signal time-frequency distribution result, the real mechanical fault condition of the equipment or the structure can be reflected in time, and the use safety of the equipment or the structure is ensured.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the present invention are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of one embodiment of a method of diagnosing a mechanical fault of the present invention;
fig. 2 is a vibration signal time-frequency distribution diagram of a signal of the first monitored object;
fig. 3 is a schematic diagram of a grid divided on a vibration signal time-frequency distribution diagram of a signal of a first monitored object;
fig. 4 is a vibration signal time-frequency distribution diagram of a signal of a second monitored object;
fig. 5 is a schematic diagram of a grid divided on a vibration signal time-frequency distribution diagram of a signal of a second monitored object;
FIG. 6 is a schematic diagram of the distribution of the maximum amplitude value and the peak value close to the maximum value in the time-frequency distribution result.
Detailed Description
The technical solutions in the present invention will be described clearly and completely with reference to the accompanying drawings, and it should be understood that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
As shown in fig. 1, the mechanical failure diagnosis method includes the steps of:
firstly, acquiring vibration signals of a monitored object, and performing time-frequency distribution processing on the acquired vibration signals to obtain vibration signal time-frequency distribution results;
on the frequency domain, dividing the vibration signal time-frequency distribution result into a plurality of frequency domain sections; in the time domain, dividing the vibration signal time-frequency distribution result into a plurality of time domain sections according to the running speed of the monitored object, so that the vibration signal time-frequency distribution result forms a plurality of grids;
calculating the amplitude density in each grid of the vibration signal time-frequency distribution result;
if the amplitude density in at least one grid exceeds a preset threshold value, performing a fifth step; otherwise, judging that the monitored object is normal, and ending;
and fifthly, judging that the object is abnormal, and ending.
The mechanical fault diagnosis method according to the first embodiment performs mechanical fault diagnosis based on time-frequency analysis, divides the time domain and the frequency domain to form a plurality of grids on the vibration signal time-frequency distribution result, and determines the state of the monitored object according to the amplitude density in each grid of the vibration signal time-frequency distribution result, so that the real mechanical fault condition of the equipment or the structure can be reflected in time, and the use safety of the equipment or the structure is ensured.
Example two
In the second step of the mechanical fault diagnosis method according to the first embodiment, in the frequency domain, according to the theoretical fault frequency obtained by theoretical calculation, the signal time-frequency distribution result in the effective frequency range is divided into a plurality of frequency domain segments according to the set proportion of the theoretical fault frequency bandwidth, and the remaining frequency domain segments which are not completely divided are merged into the previous frequency domain segment or are independently used as one frequency domain segment.
EXAMPLE III
Based on the mechanical fault diagnosis method of the first embodiment, in the second step, on the frequency domain, a set multiple from 0Hz to the maximum theoretical fault frequency obtained by theoretical calculation is used as a first frequency domain segment, the time-frequency distribution result of the signal is equally divided by the width of the first frequency domain segment in the effective frequency range of the signal, the frequency domain segment is used as a last frequency domain segment if the bandwidth of the remaining frequency domain segment is over 50% of the maximum theoretical fault frequency and is merged into the last frequency domain segment if the bandwidth of the remaining frequency domain segment is not over 50% of the maximum theoretical fault frequency;
the set multiple is greater than 1 and less than 1.8 (e.g., may be 1.1, 1.5, or 1.7, etc.).
Preferably, the theoretical fault frequency is calculated by using basic operation information (including at least one of a rotating speed, a rated operation speed and a load size).
Example four
In the second step, the time-frequency distribution results of the acceleration operation segment, the constant speed segment, and the deceleration operation segment of the monitored object are divided in the time domain.
Preferably, the constant speed section can be a constant speed running section or a static section.
EXAMPLE five
In the third step, the amplitude density in each grid of the vibration signal time-frequency distribution result is calculated, and the total amplitude is calculated:
wherein, TijIs the total amplitude of the ith time domain segment and the jth frequency domain segment, Aij(t, f) is the amplitude of the time-frequency distribution in the ith time domain segment and the jth frequency domain segment, the variable in the time domain is represented by t, and the variation range in the ith time domain segment is ti+1 to ti+1The variation in the frequency domain is denoted by f, and the variation range in the j-th frequency domain segment is fj+1 to fj+1。
Then calculating the amplitude density D in each divided gridij：
EXAMPLE six
Based on the mechanical fault diagnosis method of the first embodiment, in the fifth step, for the case that the abnormality is determined to exist, searching a local maximum value of the amplitude value in the time-frequency distribution result, recording frequency information corresponding to the local maximum value, and determining a spatial position corresponding to the maximum value of the amplitude value according to the time information and the operation speed information;
and then, analyzing the mechanical fault condition according to the recorded frequency information corresponding to the local maximum value and the spatial position corresponding to the maximum value of the amplitude.
Preferably, in the first step, the adopted signal time-frequency distribution processing method is continuous wavelet transform, discrete wavelet transform, wavelet packet transform, wigner distribution, pseudo wigner distribution, smooth pseudo wigner distribution, hilbert-yellow transform or S transform.
Preferably, the object to be monitored is an elevator hoisting machine.
EXAMPLE seven
According to the mechanical fault diagnosis method based on the first embodiment, in the first step, an acceleration sensor is adopted to collect vibration acceleration signals of a first monitored object in a constant-speed running section, and the signal sampling frequency is 4000 Hz; performing time-frequency distribution processing on the vibration acceleration signal by adopting a smooth pseudo-wigner distribution method to obtain a signal time-frequency distribution result, as shown in fig. 2;
in the second step, the theoretical fault frequency of the monitored object parts is calculated, the maximum theoretical fault frequency of each type of bearing under the equipment rotation speed is 69.9Hz, the maximum theoretical fault frequency of the gear train under the equipment rotation speed is 139Hz, 1.1 times of 139Hz with the maximum theoretical fault frequency higher is selected, namely 153Hz is used as a frequency domain segment division standard, 0Hz to 153Hz is used as a first frequency domain segment, then 153Hz segments are equally divided on the frequency domain, the remaining 11Hz frequency domain segments are divided on the frequency domain, but the 11Hz frequency domain segments are shorter, so that the frequency domain segments are combined with the previous 153Hz frequency domain segment into the last frequency domain segment, namely 13 frequency domain segments are divided on the frequency domain in total, the schematic diagram of the divided frequency domain segments on the time-frequency distribution diagram is shown in fig. 3, and the time domain is an integral body without division because only data of the equipment constant-speed operation segment is collected.
In the third step, the amplitude density in each grid of the vibration signal time-frequency distribution result is calculated, and the calculation result is shown in table 1.
Table 1 also lists threshold values of the amplitude density of each frequency domain segment, and it can be seen from table 1 that the calculated amplitude density of each frequency domain segment is lower than the corresponding threshold value, that is, it does not satisfy that the amplitude density in at least one frequency domain segment exceeds the threshold value, so it is determined as a normal condition.
TABLE 1
Example eight
According to the mechanical fault diagnosis method of the first embodiment, in the first step, an acceleration sensor is adopted to collect vibration acceleration signals of a second monitored object in a constant-speed running section, and the signal sampling frequency is 4000 Hz; performing time-frequency distribution processing on the vibration acceleration signal by using a Hilbert-Huang transform method to obtain a signal time-frequency distribution result, as shown in FIG. 4;
in the second step, the theoretical fault frequency of the monitored object parts is calculated, the maximum theoretical fault frequency of each model of bearing under the equipment rotation speed is 69.9Hz, the maximum theoretical fault frequency of the gear train under the equipment rotation speed is 102Hz, 1.5 times of 102Hz with the higher maximum theoretical fault frequency is selected, namely 153Hz is used as a frequency domain segment division standard, 0Hz to 153Hz is used as a first frequency domain segment, then 153Hz segments are equally divided on the frequency domain, the remaining 11Hz frequency domain segments are divided on the frequency domain, but the 11Hz frequency domain segments are shorter, so that the frequency domain segments are combined with the previous 153Hz frequency domain segment into the last frequency domain segment, namely 13 frequency domain segments are divided on the frequency domain in total, the schematic diagram of the divided frequency domain segments on the time-frequency distribution diagram is shown in fig. 5, and the frequency domain is an integral body without division because only the data of the time domain constant-speed operation segment of the monitored object is collected.
In the third step, the amplitude density in each grid of the vibration signal time-frequency distribution result is calculated, and the calculation result is shown in table 2.
Table 2 also lists threshold values of the amplitude density of each frequency domain segment, and compares the threshold values of the amplitude density of each frequency domain segment, and it can be seen from table 2 that the amplitude density of at least one frequency domain segment is higher than the corresponding threshold value, and it is determined that the condition is abnormal.
TABLE 2
The local maxima in fig. 4 are calculated, as indicated by the "●" legend in fig. 6, the corresponding frequency information is recorded, and the device operating space position corresponding to the maximum amplitude value is determined from the time information and the operating speed information for subsequent inspection and maintenance.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (12)
1. A method of diagnosing a mechanical fault, comprising the steps of:
firstly, acquiring vibration signals of a monitored object, and performing time-frequency distribution processing on the acquired vibration signals to obtain vibration signal time-frequency distribution results;
on the frequency domain, dividing the vibration signal time-frequency distribution result into a plurality of frequency domain sections; in the time domain, dividing the vibration signal time-frequency distribution result into a plurality of time domain sections according to the running speed of the monitored object, so that the vibration signal time-frequency distribution result forms a plurality of grids;
calculating the amplitude density in each grid of the vibration signal time-frequency distribution result;
if the amplitude density in at least one grid exceeds a preset threshold value, performing a fifth step; otherwise, judging that the monitored object is normal, and ending;
and fifthly, judging that the object is abnormal, and ending.
2. The mechanical failure diagnosis method according to claim 1,
in the second step, on the frequency domain, according to the theoretical fault frequency obtained by theoretical calculation, the signal time frequency distribution result in the effective frequency range is divided into a plurality of frequency domain sections according to the set proportion of the theoretical fault frequency bandwidth, and the remaining frequency domain sections which are not completely divided are merged into the previous frequency domain section or are independently used as one frequency domain section.
3. The mechanical failure diagnosis method according to claim 1,
in the frequency domain, a set multiple of the maximum theoretical fault frequency obtained from 0Hz to theoretical calculation is used as a first frequency domain section, the time-frequency distribution result of the signal is equally divided by the width of the first frequency domain section in the effective frequency range of the signal, the frequency domain section is used as a last frequency domain section if the frequency domain section cannot be completely divided and the bandwidth of the residual frequency domain section exceeds 50 percent of the maximum theoretical fault frequency, and the last frequency domain section is merged into the last frequency domain section if the frequency domain section does not exceed 50 percent of the maximum theoretical fault frequency;
the set multiple is greater than 1 and less than 1.8.
4. The mechanical failure diagnosis method according to claim 3,
the set multiple is 1.1, 1.5 or 1.7.
5. The mechanical failure diagnosis method according to claim 2,
the theoretical fault frequency is calculated by using the basic operation information.
6. The mechanical failure diagnosis method according to claim 5,
the operation basic information comprises at least one of rotating speed, rated operation speed and load size.
7. The mechanical failure diagnosis method according to claim 5,
and in the second step, dividing time-frequency distribution results of an acceleration operation section, a constant speed section and a deceleration operation section of the monitored object in a time domain.
8. The mechanical failure diagnosis method according to claim 7,
the constant speed section is a constant speed running section or a static section.
9. The mechanical failure diagnosis method according to claim 1,
in the third step, the amplitude density in each grid of the vibration signal time-frequency distribution result is calculated, and the total amplitude is firstly calculated:
wherein, TijIs the total amplitude of the ith time domain segment and the jth frequency domain segment, Aij(t, f) is the amplitude of the time-frequency distribution in the ith time domain segment and the jth frequency domain segment, the variable in the time domain is represented by t, and the variation range in the ith time domain segment is ti+1 to ti+1The variation in the frequency domain is denoted by f, and the variation range in the j-th frequency domain segment is fj+1 to fj+1。
Then calculating the amplitude density D in each divided gridij：
10. The mechanical failure diagnosis method according to claim 1,
step five, searching a local maximum value of the amplitude in the time-frequency distribution result under the condition that the abnormal condition exists, recording frequency information corresponding to the local maximum value, and determining a spatial position corresponding to the maximum value of the amplitude according to the time information and the running speed information;
and then, analyzing the mechanical fault condition according to the recorded frequency information corresponding to the local maximum value and the spatial position corresponding to the maximum value of the amplitude.
11. The mechanical failure diagnosis method according to claim 1,
in the first step, the adopted signal time-frequency distribution processing method is continuous wavelet transform, discrete wavelet transform, wavelet packet transform, wigner distribution, pseudo wigner distribution, smooth pseudo wigner distribution, Hilbert-Huang transform or S transform.
12. The mechanical failure diagnosis method according to claim 1,
the monitored object is an elevator traction machine.
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