CN110595764B - Helicopter transmission system fault diagnosis method based on vibration feature extraction - Google Patents
Helicopter transmission system fault diagnosis method based on vibration feature extraction Download PDFInfo
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Abstract
The invention discloses a helicopter transmission system fault diagnosis method based on vibration feature extraction, wherein the helicopter transmission system comprises a gear box and a bearing; the method comprises the following steps of S1, diagnosing faults of the gearbox; and S2, extracting vibration characteristics of the bearing. The advantages are that: non-professional vibration analysts can quickly and accurately judge the health state of the gear of the helicopter maneuvering component; and the time domain polar coordinate graph is displayed for the vibration signal of the gear, so that the health state of the gear is displayed and judged more obviously and visually. Meanwhile, the signal-to-noise ratio of a weak signal of a bearing fault can be improved; by applying the resonance frequency of the vibration sensor to carry out resonance demodulation analysis on the bearing fault weak signal, the signal-to-noise ratio of the bearing fault signal of the vibration signal can be effectively improved, and the fault characteristic index of the bearing is extracted. The bearing fault early diagnosis is convenient to carry out.
Description
Technical Field
The invention relates to the field of vibration health monitoring of helicopter moving parts in the field of aviation, in particular to a helicopter transmission system fault diagnosis method based on vibration feature extraction.
Background
The helicopter maneuvering components mainly comprise a main rotor, a tail rotor, a speed reducer, an engine, a transmission system and the like, and the flight safety of the helicopter can be seriously and directly influenced by the structural damage of various maneuvering components, so that the monitoring and the evaluation of the structural health condition of the helicopter maneuvering components have very important significance. Structural damage or abnormality of helicopter moving parts can be generally expressed as vibration abnormality of each moving part of the helicopter, and the structural health condition of the moving parts can be accurately evaluated through vibration monitoring.
The failure diagnosis of the helicopter gearbox mainly adopts a time domain map and a frequency domain map, a professional technician is required to analyze the gear failure, and the failure display of the gear is not visual. In addition, for vibration monitoring of a helicopter bearing, because the mounting position (through a bolt mounting bracket) and the frequency response characteristic (linear frequency band) of a sensor easily cause that the helicopter bearing fault (especially the inner ring fault) is weak, a fault signal is difficult to extract through a signal processing technology.
Disclosure of Invention
The invention aims to provide a helicopter transmission system fault diagnosis method based on vibration feature extraction, so that the problems in the prior art are solved.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a helicopter transmission system fault diagnosis method based on vibration feature extraction is disclosed, wherein the helicopter transmission system comprises a gear box and a bearing; comprises the following steps of (a) carrying out,
s1, diagnosing the fault of the gearbox;
and S2, extracting vibration characteristics of the bearing.
Preferably, the step S1 includes the following detailed steps,
s101, setting a first vibration sampling rate and first sampling time for a gear box;
s102, synchronously acquiring a gear box vibration signal and a gear box rotating speed signal;
s103, according to the rotating speed signal of the gearbox, carrying out time domain synchronous average processing on the vibration signal of the gearbox, so as to obtain a time domain synchronous average signal;
s104, performing polar coordinate conversion on the time domain synchronous average signal to obtain a polar coordinate graph of the gearbox;
and S105, carrying out fault diagnosis on the gearbox according to the polar coordinate graph of the gearbox.
Preferably, the step S103 of performing the time domain synchronous averaging process on the gearbox vibration signal by using the time domain synchronous averaging method includes the following steps,
establishing a frequency conversion time scale according to the rotating speed signal of the gear box;
segmenting the vibration signal according to the frequency conversion time scale, and taking 16 frequency conversion time scales;
and superposing and averaging the signals after each section of resampling interpolation to obtain a time domain synchronous average signal.
Preferably, the step S2 includes the following detailed steps,
s201, selecting a vibration sensor with fixed resonance frequency;
s202, setting a second vibration sampling rate and second sampling time for the vibration sensor;
s203, collecting a bearing vibration signal, and carrying out high-pass filtering processing on the bearing vibration signal;
s204, performing symptom strengthening on the bearing vibration signal subjected to the high-pass filtering processing;
s205, performing Hilbert transform on the bearing vibration signal subjected to symptom enhancement, and performing low-pass processing;
s206, calculating the fault characteristic frequency of the bearing according to the bearing vibration signal and the bearing size parameter;
and S207, extracting vibration characteristics of the bearing vibration signals.
The invention has the beneficial effects that: 1. a non-professional vibration analyzer quickly and accurately judges the health state of the gear of the helicopter maneuvering part; and the time domain polar coordinate graph is displayed for the vibration signal of the gear, so that the health state of the gear is displayed and judged more obviously and visually. 2. Improving the signal-to-noise ratio of the bearing fault weak signal; by applying the resonance frequency of the vibration sensor to carry out resonance demodulation analysis on the bearing fault weak signal, the signal-to-noise ratio of the bearing fault signal of the vibration signal can be effectively improved, and the fault characteristic index of the bearing is extracted. The bearing fault early diagnosis is convenient to carry out.
Drawings
FIG. 1 is a flow chart of a fault diagnosis method in an embodiment of the invention;
FIG. 2 is a flow chart of a fault diagnosis of a gearbox in an embodiment of the present invention;
FIG. 3 is a graph showing the polar effect of a gearbox in an embodiment of the present invention;
fig. 4 is a flow chart of the vibration feature extraction performed by the bearing in the embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Example one
As shown in fig. 1 to 4, the invention provides a failure diagnosis method for a helicopter transmission system based on vibration feature extraction, wherein the helicopter transmission system comprises a gearbox and a bearing; comprising the steps of, as shown in figure 1,
s1, diagnosing the fault of the gearbox;
and S2, extracting vibration characteristics of the bearing.
In this embodiment, as shown in fig. 2, the step S1 includes the following detailed steps,
s101, setting a first vibration sampling rate and first sampling time for a gear box;
s102, synchronously acquiring a gear box vibration signal and a gear box rotating speed signal;
s103, according to the rotating speed signal of the gearbox, carrying out time domain synchronous average processing on the vibration signal of the gearbox, so as to obtain a time domain synchronous average signal;
s104, performing polar coordinate conversion on the time domain synchronous average signal to obtain a polar coordinate graph of the gearbox;
and S105, carrying out fault diagnosis on the gearbox according to the polar coordinate graph of the gearbox.
In this embodiment, as shown in fig. 3, the time domain synchronous averaging processing on the gearbox vibration signal in step S103 by using the time domain synchronous averaging method includes the following steps,
establishing a frequency conversion time scale according to the rotating speed signal of the gear box;
segmenting the vibration signal according to the frequency conversion time scale, and taking 16 frequency conversion time scales;
and superposing and averaging the signals after each section of resampling interpolation to obtain a time domain synchronous average signal.
Preferably, the step S2 includes the following detailed steps,
s201, selecting a vibration sensor with fixed resonance frequency;
s202, setting a second vibration sampling rate and second sampling time for the vibration sensor;
s203, collecting a bearing vibration signal, and carrying out high-pass filtering processing on the bearing vibration signal;
s204, performing symptom strengthening on the bearing vibration signal subjected to the high-pass filtering processing;
s205, performing Hilbert transform on the bearing vibration signal subjected to symptom enhancement, and performing low-pass processing;
s206, calculating the fault characteristic frequency of the bearing according to the bearing vibration signal and the bearing size parameter;
and S207, extracting vibration characteristics of the bearing vibration signals.
Example two
In this embodiment, a process of diagnosing a fault of a gearbox is specifically described. Firstly, obtaining the rated rotating shaft rotating speed N and the tooth number N1 of the helicopter gearbox to be measured. Setting single sampling time T as 100 times of rated rotation speed of the rotating shaft, wherein T is (100 multiplied by 60)/N; setting a sampling rate to be more than 3.25 multiplied by 2.56 multiplied by the gear meshing frequency, wherein the gear meshing frequency is (N/60) multiplied by N1; the sampling rate is one of the three numbers 8192, 16384 and 32768.
In this embodiment, the time domain synchronous averaging is performed on the vibration signal of the gearbox according to the time scale of the rotation speed signal.
In this embodiment, the time domain synchronous average signal is converted into a polar coordinate system, as shown in fig. 4. The polar coordinate amplitude is the vibration signal time domain amplitude, and the polar coordinate phase: the length of the number of sampling points of the vibration signal corresponding to the Phase which is 1/1 rotation speed time scales is multiplied by 360;
in the embodiment, the vibration signal generated by each tooth of the gear box can be visually and accurately checked according to the polar coordinate graph. Faults such as gear breakage, gear abrasion and the like are quickly judged. The extraction process adopts a time domain synchronous averaging method, can effectively eliminate signal components irrelevant to the rotation frequency of the shaft, including noise and irrelevant periodic signals, and improves the signal-to-noise ratio of the known periodic signals.
EXAMPLE III
In this embodiment, a process of extracting vibration characteristics of a bearing is specifically described, and a resonant frequency of a vibration sensor is selected to be 25 kHz; setting a vibration sampling frequency of 65536Hz and sampling time of 10 s; carrying out band-pass filtering on the original vibration signal of the bearing (the low-pass cut-off frequency is 0.8 times of the resonance frequency, and the high-pass cut-off frequency is 1.2 times of the resonance frequency), carrying out Hilbert transform, and carrying out low-pass filtering to obtain a noise-eliminating signal;
in the embodiment, the vibration signal is segmented into 10 segments by sampling at equal intervals. Performing FFT (fast Fourier transform) on each section of signal, converting the signal into a spectrum signal, and superposing and averaging each section of spectrum signal to obtain an averaged spectrum signal; calculating the fault characteristic frequency of the bearing according to the original mechanical vibration signal and the size parameter of the bearing; and extracting vibration fault characteristics of the bearing.
In the embodiment, the bearing resonant frequency which is 5-7 times larger than the bearing linear frequency can be easily seen through the frequency characteristic of the vibration sensor, so that the vibration sensor with the fixed resonant frequency is selected, and the weak bearing fault signal modulated by the resonant frequency is demodulated in a resonant envelope mode. The bearing fault signal can be amplified, the signal-to-noise ratio of the bearing fault signal is improved, and the bearing fault can be detected in an early stage. The weak signal (especially the inner ring fault signal) of the bearing can be amplified, and the signal-to-noise ratio of the bearing fault signal is improved.
In this embodiment, after the original vibration signal is envelope-demodulated by the vibration signal through hilbert transform, a corresponding vibration signal is obtained, so that useful information of the vibration signal is not easily submerged by noise, and signal features related to bearing faults are more easily extracted from the useful information.
In this embodiment, the bearing fault signal is calculated as follows, including FBPFI(inner ring failure signal), FBPFO(outer ring failure signal) FBSF(Rolling element failure signal), FFTF(cage failure signal).
Characteristic frequency of the bearing outer ring: fBPFI＝nN(1+DWcosα/D)/120；
Characteristic frequency of the bearing inner ring: fBPFO＝nN(1-DWcosα/D)/120；
Bearing rolling element characteristic frequency: fBSF＝DN[1-[(DW-cosα)/D]2]/60DW；
Bearing cage characteristic frequency: fFTF＝N(1-DWcosα/D)/120；
Wherein DWThe diameter of the rolling body, D is the diameter of the pitch circle of the bearing, alpha is the contact angle, N is the number of the rolling bodies, and N is the rotating speed of the bearing.
In this embodiment, calculation of SYM _ BPFI (bearing inner race fault index), SYM _ BPFO (outer race fault index), SYM _ BSF (rolling element fault index), and SYM _ FTF (cage fault index) is as follows:
SYM_BPFI＝(1_FBPFI+2_FBPFI+3_FBPFI+4_FBPFI)/4；
SYM_BPFO＝(1_FBPFO+2_FBPFO+3_FBPFO+4_FBPFO)/4；
SYM_BSF＝(1_FBSF+2_FBSF+3_FBSF+4_FBSF)/4；
SYM_FTF＝(1_FFTF+2_FFTF+3_FFTF+4_FFTF)/4；
wherein, 1_ is a 1-order bearing fault signal in the bearing frequency spectrum signal; 2_ is a 2-order bearing fault signal in the bearing frequency spectrum signal; 3_ is a 3-order bearing fault signal in the bearing frequency spectrum signal; and 4_ is a 4-step bearing fault signal in the bearing frequency spectrum signal.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the invention provides a helicopter transmission system fault diagnosis method based on vibration feature extraction, so that a non-professional vibration analyzer can quickly and accurately judge the health state of a helicopter maneuvering part gear; and the time domain polar coordinate graph is displayed for the vibration signal of the gear, so that the health state of the gear is displayed and judged more obviously and visually. Meanwhile, the signal-to-noise ratio of a weak signal of a bearing fault can be improved; by applying the resonance frequency of the vibration sensor to carry out resonance demodulation analysis on the bearing fault weak signal, the signal-to-noise ratio of the bearing fault signal of the vibration signal can be effectively improved, and the fault characteristic index of the bearing is extracted. The bearing fault early diagnosis is convenient to carry out.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (1)
1. A helicopter transmission system fault diagnosis method based on vibration feature extraction is disclosed, wherein the helicopter transmission system comprises a gear box and a bearing; the method is characterized in that: comprises the following steps of (a) carrying out,
s1, diagnosing the fault of the gearbox;
s2, extracting vibration characteristics of the bearing;
the step S1 includes the following detailed steps,
s101, setting a first vibration sampling rate and first sampling time for a gear box;
s102, synchronously acquiring a gear box vibration signal and a gear box rotating speed signal;
s103, according to the rotating speed signal of the gearbox, carrying out time domain synchronous average processing on the vibration signal of the gearbox, so as to obtain a time domain synchronous average signal;
s104, performing polar coordinate conversion on the time domain synchronous average signal to obtain a polar coordinate graph of the gearbox;
s105, carrying out fault diagnosis on the gear box according to the polar coordinate graph of the gear box;
the step S103 of performing the time domain synchronous averaging process on the gearbox vibration signal by adopting the time domain synchronous averaging method comprises the following steps,
establishing a frequency conversion time scale according to the rotating speed signal of the gear box;
segmenting the vibration signal according to the frequency conversion time scale, and taking 16 frequency conversion time scales;
superposing and averaging the signals after each section of resampling interpolation to obtain a time domain synchronous average signal;
the step S2 includes the following detailed steps,
s201, selecting a vibration sensor with fixed resonance frequency;
s202, setting a second vibration sampling rate and second sampling time for the vibration sensor;
s203, collecting a bearing vibration signal, and carrying out high-pass filtering processing on the bearing vibration signal;
s204, performing symptom strengthening on the bearing vibration signal subjected to the high-pass filtering processing;
s205, performing Hilbert transform on the bearing vibration signal subjected to symptom enhancement, and performing low-pass processing;
s206, calculating the fault characteristic frequency of the bearing according to the bearing vibration signal and the bearing size parameter;
s207, extracting vibration characteristics of the bearing vibration signals;
the bearing fault characteristic frequency is calculated as follows, including the inner ring fault signal FBPFIOuter ring fault signal FBPFORolling element fault signal FBSFCage fault signal FFTF；
Characteristic frequency of the bearing outer ring: fBPFI＝nN(1+DWcosα/D)/120；
Characteristic frequency of the bearing inner ring: fBPFO＝nN(1-DWcosα/D)/120；
Bearing rolling element characteristic frequency: fBSF＝DN[1-[(DW-cosα)/D]2]/60DW；
Bearing cage characteristic frequency: fFTF＝N(1-DWcosα/D)/120；
Wherein DWThe diameter of the rolling body, D is the diameter of a pitch circle of the bearing, alpha is a contact angle, N is the number of the rolling bodies, and N is the rotating speed of the bearing;
calculating the fault characteristic indexes of the bearing, wherein the fault characteristic indexes comprise a bearing inner ring fault index SYM _ BPFI, an outer ring fault index SYM _ BPFO, a rolling element fault index SYM _ BSF and a retainer fault index SYM _ FTF;
SYM_BPFI＝(1_FBPFI+2_FBPFI+3_FBPFI+4_FBPFI)/4；
SYM_BPFO＝(1_FBPFO+2_FBPFO+3_FBPFO+4_FBPFO)/4；
SYM_BSF＝(1_FBSF+2_FBSF+3_FBSF+4_FBSF)/4；
SYM_FTF＝(1_FFTF+2_FFTF+3_FFTF+4_FFTF)/4；
wherein, 1_ is a 1-order bearing fault signal in the bearing frequency spectrum signal; 2_ is a 2-order bearing fault signal in the bearing frequency spectrum signal; 3_ is a 3-order bearing fault signal in the bearing frequency spectrum signal; 4_ is a 4-order bearing fault signal in the bearing frequency spectrum signal;
and performing early diagnosis on the bearing fault through the obtained bearing fault characteristic index.
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| CN112781709A (en) * | 2020-12-24 | 2021-05-11 | 上海核工程研究设计院有限公司 | Method for analyzing early failure and extracting characteristics of equipment vibration signal under variable speed working condition |
| CN113048220B (en) * | 2021-03-12 | 2022-11-08 | 中煤科工集团重庆研究院有限公司 | Mining elevator gear box hidden danger identification method and monitoring device |
| CN113776836B (en) * | 2021-10-25 | 2024-01-02 | 长沙理工大学 | Self-adaptive synchronous average bearing fault quantitative diagnosis method |
| CN115597871B (en) * | 2022-10-24 | 2023-10-31 | 中国人民解放军93208部队 | Onboard health diagnosis device for mechanical system of military turbofan engine |
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