CN115683617A - Improved variable working condition fault diagnosis method and system - Google Patents
Improved variable working condition fault diagnosis method and system Download PDFInfo
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Abstract
The invention discloses an improved variable working condition fault diagnosis method and system, belonging to the field of fault diagnosis of mechanical equipment.
Description
Technical Field
The invention belongs to the field of fault diagnosis of mechanical equipment, and particularly relates to an improved variable working condition fault diagnosis method and system.
Background
At present, with the continuous development of intelligent manufacturing, the intelligent degree of an industrial system is higher and higher, the industrial system is also more and more complex, the loss caused by equipment damage is more and more large, and an accurate mathematical model is difficult to establish due to the complexity and nonlinearity of the industrial system. Due to the rapid development of information technology, a large amount of operation data is generated in an industrial system, wherein the operation data comprises a large amount of valuable equipment state information, and for a complex system with high integration level, a fault diagnosis method based on data driving is proved to be more effective than a manual model based on manual experience.
The existing GIS fault diagnosis system and method based on vibration signal frequency spectrum analysis, with the patent number of CN201210194724.5, comprises a vibration acceleration sensor, a charge amplifier, a data acquisition instrument and a PC (personal computer) which are sequentially connected in series, wherein the vibration acceleration sensor comprises three vibration sensors which are respectively fixed on the outer surface of a GIS box body in the X-axis direction, the Y-axis direction and the Z-axis direction. The diagnosis method of the system comprises the steps of collecting GIS vibration signals in the X-axis direction, the Y-axis direction and the Z-axis direction by using three vibration sensors, carrying out wavelet denoising on the vibration signals intercepted in the whole period, carrying out spectrum analysis, respectively calculating the energy sum at 50Hz and the energy sum at 100Hz, 200Hz and 300Hz, comparing the calculated value with the normal state, and judging the GIS fault by combining a threshold value according to the comparison result. The method has simple engineering realization and obvious characteristics, and can effectively diagnose the GIS fault.
Although acceleration and spectrum analysis are carried out on vibration signals, frequency components of the analysis signals in the whole process can be effectively revealed, the rule of frequency transformation along with time cannot be reflected, for the variable speed working condition of some equipment, particularly for the vibration signals needing to be collected for a long time at the low speed end, if the traditional frequency domain analysis method is still adopted for the signals collected for a long time, spectrum energy dispersion and spectral line fuzzy phenomenon occur, and order analysis can be effectively applied to the variable working condition signals.
Disclosure of Invention
Problems to be solved
The invention provides an improved variable working condition fault diagnosis method and system, aiming at the problem that the conventional frequency spectrum analysis cannot reveal the change rule of a signal frequency spectrum along with time, so that a fuzzy phenomenon can occur in the frequency spectrum of a vibration signal once the rotating speed is unstable.
Technical scheme
In order to solve the above problems, the present invention adopts the following technical solutions.
An improved variable working condition fault diagnosis method comprises the following steps:
step 1: synchronously acquiring vibration signal data and rotating speed signal data under a steady-state working condition by using a sensor;
step 2: taking the rotating speed signal data as discrete points, fitting a functional relation between a rotating speed corner and time by adopting a curve fitting technology, and calculating to obtain time domain waveform signal data;
and 3, step 3: performing fast Fourier transform and Hilbert transform on the time domain waveform signal data to obtain frequency spectrum data and envelope waveform data, and performing fast Fourier transform on the envelope waveform data to obtain envelope spectrum data;
and 4, step 4: calculating a time value of angular domain resampling of the vibration signal by combining the time domain waveform signal data with the vibration signal data, and taking the time value as a phase discrimination time scale;
and 5: resampling is carried out according to the phase discrimination time scale under the speed change working condition, synchronously collected vibration signal data and equiangular sampling signal data are obtained, and the equiangular sampling signal data are used as order signal data;
step 6: performing fast Fourier transform and Hilbert transform on the order signal data to obtain order spectrum data and order envelope waveform data, and performing fast Fourier transform on the order spectrum data to obtain order envelope spectrum data;
and 7: and taking the frequency spectrum data and the envelope spectrum data under the steady-state working condition as reference signal data, and performing fault analysis and diagnosis on the order spectrum data and the order envelope spectrum data under the variable-speed working condition to obtain a signal fault analysis result.
Preferably, the curve fitting technology is to obtain the rotation speed curve of the reference axis by adopting a first-order, second-order and multi-order polynomial piecewise fitting mode.
Preferably, the resampling according to the phase-discrimination time scale under the variable-speed condition is to perform interpolation resampling on the vibration signal by using the rotation speed signal, and convert the vibration signal of the equal time sequence into the vibration signal of the equal angle sequence.
Further, when the maximum analysis order Omax of the equiangular sampling signal data is =1, the sampling is performed by using Nyquist sampling theorem, and the sampling order FOrder is not less than 2.56 Omax, namely, the number of sampling points in one 2-pi period is FOrder.
Further, the angle that the reference axis rotates between two adjacent sampling points is delta _ theta, and the calculation formula is as follows:
delta_theta＝2*pi/FOrder
furthermore, when resampling is performed according to a phase discrimination time scale under the variable speed working condition and the instantaneous power frequency change of the rotating speed is a linear function of time, the equal-angle sampling signal data calculation formula is as follows:
ft＝5*t+2
X＝cos(2*pi*ft.*t)
wherein: x is equal angle sampling signal data, and t is sampling duration.
Further, the phase detection time scale tn is calculated as follows:
ft(tn)*tn＝n/FOrder,for n＝1,2,...K
wherein K is the total sampling point number of the resampling.
Further, when resampling is performed according to the phase discrimination time scale under the variable speed working condition and the instantaneous working frequency change of the rotating speed is a quadratic function of time, the equation for calculating the equal-angle sampling signal data X is as follows:
ft＝5*t. 2 +2
AM＝cos(2*pi*m*ft.*t)
SM1＝1*cos(2*pi*i*ft.*t)
SM2＝2*cos(2*pi*n*ft.*t)
X＝AM+SM1+SM2
wherein m, i and n are all the analytical orders of the signal data X, and the maximum analytical order Omax is the maximum analytical order value.
Further, resampling is carried out according to a phase discrimination time scale under the working condition of speed change, the instantaneous power frequency change of the rotating speed is a quadratic function of time, the gear meshing signal is modulated on a carrier, and when a side frequency band is generated, the equal-angle sampling signal data X has the following calculation formula:
ft＝5*t. 2 +2
AM＝1+cos(2*pi*m*ft.*t)
SM＝cos(2*pi*i*ft.*t)
X＝AM.*SM
where m and i are both the analytical order of the signal data X.
An improved variable condition fault diagnosis system comprising:
the steady-state signal acquisition module is used for synchronously acquiring vibration signal data and rotating speed signal data under a steady-state working condition;
the curve fitting module is used for fitting a functional relation between a rotating speed corner and time by taking the rotating speed signal data as discrete points to obtain time domain waveform signal data;
the steady-state data conversion module is used for converting the time domain waveform signal data to obtain frequency spectrum data and envelope waveform data and converting the envelope waveform data to obtain envelope spectrum data;
the time calculation module is used for calculating a phase discrimination time scale by using the time domain waveform signal data and the vibration signal data;
the variable speed signal acquisition module is used for synchronously acquiring vibration signal data and equal angle sampling signal data under the variable speed working condition according to the phase discrimination time scale;
the variable speed data conversion module is used for converting the equiangular sampling signal data to obtain order spectrum data and order envelope waveform data and rotating the order envelope waveform data to obtain order envelope spectrum data;
and the transformation analysis module is used for carrying out fault analysis diagnosis on the order spectrum data and the order envelope spectrum data under the variable speed working condition to obtain a signal fault analysis result.
An improved fault diagnosis method and system under variable working conditions comprises the steps of obtaining vibration signal data and rotating speed signal data under the stable working conditions, obtaining time domain waveform signal data through calculation, converting the time domain waveform signal data to obtain frequency spectrum data and envelope waveform data, converting the envelope waveform data to obtain envelope spectrum data, calculating a phase discrimination time scale, resampling under the variable working conditions according to the phase discrimination time scale to obtain vibration signal data and order signal data, converting the order signal data to obtain order spectrum data and order envelope waveform data, converting the order spectrum data to obtain order envelope spectrum data, performing fault analysis and diagnosis on the order spectrum data and the order envelope spectrum data under the variable working conditions to obtain signal fault analysis results, establishing a bridge for signal analysis under the stable working conditions and signal analysis under the non-stable working conditions, and improving accuracy of the fault analysis results.
Advantageous effects
Compared with the prior art, the invention has the beneficial effects that:
(1) The invention is applied to the gear box with a determined structure, even if the rotating speed changes, the proportional relation among the characteristic frequencies of all parts in the gear box can not be changed, the time domain waveform is converted into the order waveform through equal angle sampling, the equal time sequence vibration signal is converted into the equal angle sequence vibration signal, namely the number of sampling points in the rotating shaft rotating a specific angle is equal no matter how much the rotating speed is, thus the frequency ambiguity in the frequency spectrum analysis can be effectively avoided, and a bridge for signal analysis under the steady state working condition and signal analysis under the unsteady state working condition is established;
(2) The invention uses fast Fourier transform and Hilbert transform to convert time domain waveform signal data into frequency spectrum data and envelope waveform data, converts equiangular sampling signal data into order spectrum data and order envelope waveform data, uses fast Fourier transform to convert envelope waveform data into envelope spectrum data, converts order spectrum data into order envelope spectrum data, and uses the frequency spectrum data and the envelope spectrum data under a steady-state working condition as reference signal data to analyze and diagnose faults of the order spectrum data and the order envelope spectrum data under a variable-speed working condition to obtain a signal fault analysis result.
Drawings
In order to more clearly illustrate the embodiments or exemplary technical solutions of the present application, the drawings needed to be used in the embodiments or exemplary descriptions will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application and therefore should not be considered as limiting the scope, and it is also possible for those skilled in the art to obtain other drawings according to the drawings without inventive efforts.
FIG. 1 is a schematic diagram of the steps of the present invention;
FIG. 2 is a schematic flow diagram of the present invention;
FIG. 3 is a graph of rotational speed and frequency for example 2;
FIG. 4 is an equal time sampling graph and a spectrogram of example 2;
fig. 5 is a phase detection clock plot of embodiment 2;
FIG. 6 is a graph of an equal time sampling and a graph of an equal angle sampling in example 2;
FIG. 7 is a rank plot of example 2;
FIG. 8 is a graph of rotational speed and frequency for example 3;
FIG. 9 is an equal time sampling chart and a spectrum chart of example 3;
FIG. 10 is a graph of an equal time sampling and a graph of an equal angle sampling in example 3;
FIG. 11 is a rank plot of example 3;
FIG. 12 is a graph of the rotational speed and frequency of example 4;
FIG. 13 is an equal time sampling chart and a spectrum chart of example 4;
FIG. 14 is an isometric sampling chart and an isometric sampling chart of example 4;
FIG. 15 is a rank plot of example 4;
FIG. 16 is a graph of the order envelope signal of example 4;
fig. 17 is the order spectrum and order envelope spectrum of example 4.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be described clearly and completely with reference to the drawings in the embodiments of the present application, it is obvious that the described embodiments are a part of the embodiments of the present application, but not all of the embodiments, and generally, components of the embodiments of the present application described and illustrated in the drawings herein can be arranged and designed in various different configurations.
Therefore, the following detailed description of the embodiments of the present application, as presented in the figures, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application, and all other embodiments that can be derived by one of ordinary skill in the art based on the embodiments in the present application without making creative efforts fall within the scope of the claimed application.
Example 1
As shown in FIG. 1, an improved variable working condition fault diagnosis method comprises the following steps:
the method comprises the steps of firstly, synchronously acquiring vibration signal data and rotation speed signal data under a steady-state working condition by using a sensor, using the rotation speed signal data as discrete points, fitting a function relation between a rotation speed corner and time by adopting a curve fitting technology, calculating time domain waveform signal data, performing fast Fourier transform and Hilbert transform on the time domain waveform signal data to obtain frequency spectrum data and envelope waveform data, performing fast Fourier transform on the envelope waveform data to obtain envelope spectrum data, calculating a moment value of angular domain resampling of the vibration signal by combining the time domain waveform signal data with the vibration signal data, using the moment value as a phase discrimination time scale, resampling under a variable-speed working condition according to the phase discrimination time scale to obtain synchronously acquired vibration signal data and equiangular sampling signal data, using the equiangular sampling signal data as order signal data, performing fast Fourier transform and Hilbert transform on the order signal data to obtain order spectrum data and order envelope waveform data, performing fast Fourier transform on the order spectrum data to obtain order spectrum data and order envelope data, using the frequency spectrum data and the constant-speed envelope data as reference signal data, and performing fault analysis on order spectrum data under the variable-speed working condition, and obtaining fault analysis results, and obtaining order spectrum data.
The method comprises the steps of synchronously acquiring vibration signal data and rotating speed signal data under a steady-state working condition by using a sensor, using the rotating speed signal data as discrete points, fitting a functional relation between a rotating speed corner and time by adopting a curve fitting technology, and calculating to obtain time domain waveform signal data, wherein the curve fitting technology is used for obtaining a rotating speed curve of a reference shaft by adopting a first-order, second-order and multi-order polynomial piecewise fitting mode.
And performing fast Fourier transform and Hilbert transform on the time domain waveform signal data to obtain frequency spectrum data and envelope waveform data, and performing fast Fourier transform on the envelope waveform data to obtain envelope spectrum data.
The time domain waveform signal data is combined with the vibration signal data to calculate the angular domain resampling time value of the vibration signal, the angular domain resampling time value is used as a phase discrimination time scale, and the resampling is carried out under the speed change working condition according to the phase discrimination time scale by utilizing a rotating speed signal to carry out interpolation resampling on the vibration signal, so that the vibration signal of the equal time sequence is converted into the vibration signal of the equal angular sequence.
Resampling is carried out according to a phase discrimination time scale under the variable speed working condition, synchronously acquired vibration signal data and equiangular sampling signal data are obtained, the equiangular sampling signal data are used as order signal data, when the maximum analysis order Omax of the equiangular sampling signal data is =1, sampling is carried out by using a Nyquist sampling theorem, the sampling order FOrder is more than or equal to 2.56 Omax, namely, the number of 2-XPi periodic sampling points is FOrder.
The angle that the reference axis rotates between two adjacent sampling points is delta _ theta, and the calculation formula is as follows:
delta_theta＝2*pi/FOrder
resampling is carried out according to a phase discrimination time scale under the working condition of speed change, and when the instantaneous power frequency change of the rotating speed is a linear function of time, the equal-angle sampling signal data calculation formula is as follows:
ft＝5*t+2
X＝cos(2*pi*ft.*t)
wherein: x is equal angle sampling signal data, and t is sampling duration.
The phase discrimination time scale tn is calculated as follows:
ft(tn)*tn＝n/FOrder,for n＝1,2,...K
wherein K is the total sampling point number of the resampling.
Under the working condition of speed change, resampling is carried out according to a phase discrimination time scale, and when the instantaneous working frequency change of the rotating speed is a quadratic function of time, the calculation formula of the equal-angle sampling signal data X is as follows:
ft＝5*t. 2 +2
AM＝cos(2*pi*m*ft.*t)
SM1＝1*cos(2*pi*i*ft.*t)
SM2＝2*cos(2*pi*n*ft.*t)
X＝AM+SM1+SM2
wherein m, i and n are all the analytical orders of the signal data X, and the maximum analytical order Omax is the maximum analytical order value.
Resampling is carried out according to a phase discrimination time scale under the working condition of speed change, the instantaneous power frequency change of the rotating speed is a quadratic function of time, a gear meshing signal is modulated on a carrier, and when a side frequency band is generated, an equal-angle sampling signal data X calculation formula is as follows:
ft＝5*t. 2 +2
AM＝1+cos(2*pi*m*ft.*t)
SM＝cos(2*pi*i*ft.*t)
X＝AM.*SM
where m and i are both the analytical order of the signal data X.
The method comprises the steps of carrying out fast Fourier transform and Hilbert transform on order signal data to obtain order spectrum data and order envelope waveform data, carrying out fast Fourier transform on the order spectrum data to obtain order envelope spectrum data, taking frequency spectrum data and envelope spectrum data under a steady working condition as reference signal data, carrying out fault analysis and diagnosis on the order spectrum data and the order envelope spectrum data under a variable-speed working condition, and obtaining a signal fault analysis result.
As can be seen from the above description, in this example, time domain waveform signal data is obtained by obtaining vibration signal data and rotation speed signal data under a steady-state condition, time domain waveform signal data is obtained by calculation, the time domain waveform signal data is converted to obtain frequency spectrum data and envelope waveform data, the envelope waveform data is converted to obtain envelope spectrum data, a phase discrimination time scale is calculated, resampling is performed according to the phase discrimination time scale under a variable-speed condition to obtain vibration signal data and order signal data, the order signal data is converted to obtain order spectrum data and order envelope waveform data, the order spectrum data is converted to obtain order envelope spectrum data, and fault analysis and diagnosis are performed on the order spectrum data and the order envelope spectrum data under the variable-speed condition to obtain a signal fault analysis result.
Example 2
As shown in fig. 3-7, the rotating speed is linearly frequency modulated, the linear change of the simulated field rotating speed is changed from low speed 120 to high speed 720, the sampling frequency is 1000, the sampling time is 2 seconds, and the instantaneous power frequency change is a linear function of time:
ft＝5*t+2
X＝cos(2*pi*ft.*t)
when the rotating shaft moves at an increasing speed, the time required for the rotating shaft to pass through an angle of 2 × pi is shorter and shorter along with the increase of the rotating speed frequency, so that the number of sampling points in the same period is correspondingly reduced. Direct sampling of the fft spectrum produces a frequency ambiguity phenomenon, as shown in fig. 4.
In order to perform order analysis, the vibration signals need to be interpolated and resampled by using the rotating speed signals, and the vibration signals with the same time sequence are converted into vibration signals with the same angle sequence, namely the number of sampling points in the rotating shaft rotating a specific angle is equal no matter how many the rotating speed is, so that the frequency ambiguity in the frequency spectrum analysis can be effectively avoided.
Because X = cos (2 × pi ft. T), the maximum analytical order of the signal, omax =1, resembles the Nyquist sampling theorem: taking the sampling order FOrder to be more than or equal to 2.56 x Omax =2.56, and taking the sampling order FOrder to be slightly larger in order to improve the precision: FOrder =20.
I.e. one 2 × pi period sample FOrder =20 points, and the angle delta _ theta =2 × pi/FOrder rotated by the reference axis between two adjacent samples.
Calculating the resampling time value of the angular domain of the vibration signal: phase discrimination time scale tn
ft(tn)*tn＝n/FOrder,for n＝1,2,...K
(5*tn+2)*tn＝n/FOrder,for n＝1,2,...K
The phase detection time scale is shown in fig. 5.
Fig. 6 shows how to interpolate the original equal-time sampling signal according to the phase discrimination time scale tn to obtain an equal-angle signal.
Fft is taken on the order signal to obtain an order spectrum: the horizontal axis is Order (Order), and as shown in fig. 7, the Order spectrum is collected at the Order =1 position regardless of the change in the original rotation speed.
Example 3
As shown in fig. 8-11, the speed is twice modulated, and the simulated field speed signal approximates a quadratic function and includes a plurality of order signals.
ft＝5*t. 2 +2
AM＝cos(2*pi*m*ft.*t)
SM1＝1*cos(2*pi*i*ft.*t)
SM2＝2*cos(2*pi*n*ft.*t)
X＝AM+SM1+SM2
m, i and n are 1,5 and 10 orders respectively, and FOrder =80 is taken, and the maximum is 10 orders.
The variation curves of the rotating speed and the power frequency are shown in figure 8.
The original signal is equally time sampled and fft spectrally as shown in fig. 9, from which it is difficult to obtain frequency information.
The equiangular sampling to obtain the order signal is shown in fig. 10.
Calculation of the order spectrum using the order signal as shown in fig. 11, it can be seen that the energy is concentrated at the 1,5 and 10 orders.
Example 4
As shown in fig. 12-17, the rotational speed is twice modulated and the analog gear mesh signal has a modulation on the carrier, creating a side band.
ft＝5*t. 2 +2
AM＝1+cos(2*pi*m*ft.*t)
SM＝cos(2*pi*i*ft.*t)
X＝AM.*SM
m and i are respectively 1 order and 10 orders, AM is a low-frequency signal, SM is a 10-order carrier signal, and i is 10 teeth in an engagement signal.
The rotation speed signal is shown in fig. 12, the original signal and the frequency spectrum thereof are shown in fig. 13, the original signal and the order signal are shown in fig. 14, the order spectrum is shown in fig. 15, it can be seen that two side frequency signals (9 th order and 11 th order) are generated by taking 10 th order as the center, the order signal takes an envelope as shown in fig. 16, the order spectrum and the order envelope spectrum are shown in fig. 17, and it can be seen that the envelope spectrum mainly contains 1 st order signal.
Example 5
As shown in fig. 2, an improved variable operation condition fault diagnosis system includes:
the steady-state signal acquisition module is used for synchronously acquiring vibration signal data and rotating speed signal data under a steady-state working condition;
the curve fitting module is used for fitting a functional relation between a rotating speed corner and time by taking the rotating speed signal data as discrete points to obtain time domain waveform signal data;
the steady-state data conversion module is used for converting the time domain waveform signal data to obtain frequency spectrum data and envelope waveform data and converting the envelope waveform data to obtain envelope spectrum data;
the time calculating module is used for calculating a phase discrimination time scale by using the time domain waveform signal data and combining the vibration signal data;
the variable speed signal acquisition module is used for synchronously acquiring vibration signal data and equal angle sampling signal data under the variable speed working condition according to the phase discrimination time scale;
the variable speed data conversion module is used for converting the equal angle sampling signal data to obtain order spectrum data and order envelope waveform data, and rotating the order envelope waveform data to obtain order envelope spectrum data;
and the transformation analysis module is used for carrying out fault analysis and diagnosis on the order spectrum data and the order envelope spectrum data under the variable speed working condition to obtain a signal fault analysis result.
As can be seen from the above description, in this example, the steady-state signal acquisition module acquires vibration signal data and rotation speed signal data under a steady-state condition, the curve fitting module uses the rotation speed signal data as discrete points to obtain time-domain waveform signal data, the steady-state data conversion module converts the time-domain waveform signal data to obtain frequency spectrum data, envelope waveform data and envelope spectrum data, the time calculation module calculates to obtain a phase discrimination time scale, the variable-speed signal acquisition module synchronously acquires the vibration signal data and the equal-angle sampling signal data under a variable-speed condition, the variable-speed data conversion module converts the equal-angle sampling signal data to obtain order spectrum data, order envelope waveform data and order envelope spectrum data, and the transformation analysis module performs fault analysis and diagnosis to obtain a signal fault analysis result.
The above examples are merely representative of preferred embodiments of the present invention, and the description thereof is more specific and detailed, but not to be construed as limiting the scope of the present invention. It should be noted that various changes, modifications and substitutions may be made by those skilled in the art without departing from the spirit of the invention, and all are intended to be included within the scope of the invention.
Claims (10)
1. An improved variable working condition fault diagnosis method is characterized by comprising the following steps:
step 1: synchronously acquiring vibration signal data and rotating speed signal data under a steady-state working condition by using a sensor;
step 2: taking the rotating speed signal data as discrete points, fitting a functional relation between a rotating speed corner and time by adopting a curve fitting technology, and calculating to obtain time domain waveform signal data;
and 3, step 3: carrying out fast Fourier transform and Hilbert transform on time domain waveform signal data to obtain frequency spectrum data and envelope waveform data, and carrying out fast Fourier transform on the envelope waveform data to obtain envelope spectrum data;
and 4, step 4: calculating a time value of angular domain resampling of the vibration signal by combining the time domain waveform signal data with the vibration signal data, and taking the time value as a phase discrimination time scale;
and 5: resampling is carried out according to a phase discrimination time scale under the speed change working condition, synchronously collected vibration signal data and equal-angle sampling signal data are obtained, and the equal-angle sampling signal data are used as order signal data;
and 6: performing fast Fourier transform and Hilbert transform on the order signal data to obtain order spectrum data and order envelope waveform data, and performing fast Fourier transform on the order spectrum data to obtain order envelope spectrum data;
and 7: and taking the frequency spectrum data and the envelope spectrum data under the steady-state working condition as reference signal data, and performing fault analysis and diagnosis on the order spectrum data and the order envelope spectrum data under the variable-speed working condition to obtain a signal fault analysis result.
2. The improved variable operation fault diagnosis method according to claim 1, wherein: the curve fitting technology is used for obtaining a rotating speed curve of the reference shaft by adopting a first-order, second-order and multi-order polynomial piecewise fitting mode.
3. The improved variable operating condition fault diagnosis method according to claim 2, characterized in that: the resampling according to the phase discrimination time scale under the variable speed working condition is to perform interpolation resampling on the vibration signal by using the rotating speed signal and convert the vibration signal of the equal time sequence into the vibration signal of the equal angle sequence.
4. The improved variable operating condition fault diagnosis method according to claim 3, characterized in that: when the maximum analysis order Omax of the equiangular sampling signal data is =1, sampling is carried out by using a Nyquist sampling theorem, wherein the sampling order FOrder is more than or equal to 2.56 Omax, namely FOrder is the number of sampling points in one 2-pi period.
5. The improved variable operating condition fault diagnosis method according to claim 4, characterized in that: the angle rotated by the reference axis between two adjacent sampling points is delta _ theta, and the calculation formula is as follows:
delta_theta＝2*pi/FOrder。
6. the improved variable operation fault diagnosis method according to claim 5, wherein: the resampling is carried out according to the phase discrimination time scale under the variable speed working condition, and when the instantaneous power frequency change of the rotating speed is a linear function of time, the equal-angle sampling signal data calculation formula is as follows:
ft＝5*t+2
X＝cos(2*pi*ft.*t)
wherein: x is equal angle sampling signal data, and t is sampling duration.
7. The improved variable operation fault diagnosis method according to claim 6, wherein: the calculation formula of the phase discrimination time scale tn is as follows:
ft(tn)*tn＝n/FOrder,for n＝1,2,...K
wherein K is the total sampling point number of the resampling.
8. The improved variable operating condition fault diagnosis method according to claim 7, characterized in that: under the working condition of speed change, resampling is carried out according to a phase discrimination time scale, and when the instantaneous working frequency change of the rotating speed is a quadratic function of time, the calculation formula of the equal-angle sampling signal data X is as follows:
ft＝5*t. 2 +2
AM＝cos(2*pi*m*ft.*t)
SM1＝1*cos(2*pi*i*ft.*t)
SM2＝2*cos(2*pi*n*ft.*t)
X＝AM+SM1+SM2
wherein m, i and n are all the analytical orders of the signal data X, and the maximum analytical order Omax is the maximum analytical order value.
9. The improved variable operation fault diagnosis method according to claim 8, wherein: the resampling is carried out according to a phase discrimination time scale under the speed change working condition, the instantaneous power frequency change of the rotating speed is a quadratic function of time, the gear meshing signal is modulated on a carrier, and when a side frequency band is generated, the equal-angle sampling signal data X calculation formula is as follows:
ft＝5*t. 2 +2
AM＝1+cos(2*pi*m*ft.*t)
SM＝cos(2*pi*i*ft.*t)
X＝AM.*SM
where m and i are both the analytical order of the signal data X.
10. An improved variable condition fault diagnosis system, comprising:
the steady-state signal acquisition module is used for synchronously acquiring vibration signal data and rotating speed signal data under a steady-state working condition;
the curve fitting module is used for fitting a functional relation between a rotating speed corner and time by taking the rotating speed signal data as discrete points to obtain time domain waveform signal data;
the steady-state data conversion module is used for converting the time domain waveform signal data to obtain frequency spectrum data and envelope waveform data and converting the envelope waveform data to obtain envelope spectrum data;
the time calculating module is used for calculating a phase discrimination time scale by using the time domain waveform signal data and combining the vibration signal data;
the variable speed signal acquisition module is used for synchronously acquiring vibration signal data and equal angle sampling signal data under the variable speed working condition according to the phase discrimination time scale;
the variable speed data conversion module is used for converting the equal angle sampling signal data to obtain order spectrum data and order envelope waveform data, and rotating the order envelope waveform data to obtain order envelope spectrum data;
and the transformation analysis module is used for carrying out fault analysis diagnosis on the order spectrum data and the order envelope spectrum data under the variable speed working condition to obtain a signal fault analysis result.
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| CN116448236A (en) * | 2023-06-20 | 2023-07-18 | 安徽容知日新科技股份有限公司 | Edge-end vibration monitoring system and method, and computer-readable storage medium |
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2022
- 2022-09-28 CN CN202211194140.8A patent/CN115683617A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116448236A (en) * | 2023-06-20 | 2023-07-18 | 安徽容知日新科技股份有限公司 | Edge-end vibration monitoring system and method, and computer-readable storage medium |
| CN116448236B (en) * | 2023-06-20 | 2023-09-12 | 安徽容知日新科技股份有限公司 | Edge-end vibration monitoring system and method, and computer-readable storage medium |
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