CN106872171A - A kind of adaptive learning bearing calibration of Doppler's acoustic signal - Google Patents
A kind of adaptive learning bearing calibration of Doppler's acoustic signal Download PDFInfo
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Abstract
The invention discloses a kind of adaptive learning bearing calibration of Doppler's acoustic signal.The method is in correction parameter adaptive learning, based on rail side acoustic theory, by constructing shift frequency operator and tone operator, realize the pseudo- correction of one kind to distorted signal, obtaining frequency spectrum resonant belt has undistorted and high-energy aggregation transition baseline, portray in theory and reduce rail side acoustics distortion phenomenon, physically enhance self adaptation distortion correction learning mechanic；Compared with other bearing calibrations, the algorithm be capable of self adaptation from original sound signals learning to optimal correction parameter, so as to realize the complete correction to distorted signal.The algorithm has certain robustness and high efficiency, is conducive to the on-line intelligence of follow-up rail side sound system to correct and diagnosis.
Description
Technical Field
The invention belongs to the field of rail-side acoustic Doppler distortion correction, and relates to a Doppler acoustic signal adaptive learning correction method, which can adaptively learn optimal distortion correction parameters, realize adaptive correction learning on Doppler distorted acoustic signals, and is particularly suitable for a wheelset bearing roadside acoustic diagnosis system.
Background
The rail-side acoustic diagnosis is a high-efficiency monitoring diagnosis mode which takes acoustic signals generated by moving parts as analysis objects, has the advantages of non-contact, multiple targets, no need of stopping and the like, and has important application value and significance in train wheel set bearing diagnosis. The acoustic signals acquired by the rail-side measuring equipment contain the current health information of the components, but in practice, because the train runs at a high speed, the acoustic signals generated by the moving components and the roadside fixed measuring equipment move relatively, so that the finally observed acoustic signals are distorted (namely Doppler distortion phenomenon), and difficulty is brought to the identification and extraction of fault sources. Therefore, Doppler correction analysis on the acoustic signals is of great significance for rail-side acoustic bearing fault diagnosis. Currently, two commonly used doppler correction methods are mainly used, one is to obtain a signal receiving angle sequence by using an instantaneous frequency Ridge (instant frequency Ridge Extraction) or a principal component coherence analysis (such as a short-time MUSIC algorithm), and indirectly obtain a doppler distortion parameter through fitting to construct a transmission time sequence; the other method is that a great deal of search is carried out through greedy iterative algorithms such as matching pursuit and the like (such as Doppler transient models based on Laplace wavelets and spectrum correlation estimation) to directly obtain partial or all Doppler distortion parameters and realize correction and analysis of distortion signals.
The invention provides a self-adaptive learning and correcting method of Doppler acoustic signals. The method combines the rail-side acoustic principle, constructs a frequency shift operator and an acoustic modulation operator physical model, and realizes the pseudo correction of Doppler distortion signals through operating the operators. The transition reference term of the distorted signal is obtained based on the pseudo correction, and the corresponding resonance band not only has the distortion-free characteristic, but also has high energy concentration. And the model parameter self-adaptive learning of the Doppler distortion operator is realized by using a global optimization algorithm based on a resonance frequency narrowband energy ratio maximization principle. Finally, the Doppler signal is completely corrected through a sound pressure operator and a resampling technology. The adaptive learning correction method has the characteristic of robust self-adaptation, and has a very remarkable effect on the correction of the track side Doppler distortion acoustic signals.
Disclosure of Invention
The invention aims to: an adaptive learning and correcting method of Doppler acoustic signals is provided, which is used for adaptive learning and correcting of Doppler acoustic distortion acoustic signals.
The technical scheme adopted by the invention is as follows: a method for adaptive learning correction of doppler acoustic signals, the method comprising the steps of:
step (1), obtaining an analytic signal of an acoustic measurement signal through Hilbert transform;
step (2) based on a rail-side acoustic theory, three Doppler distortion parameters (u, M and k are respectively distortion center time, wave velocity ratio and resonance frequency position, and the three parameters are main parameters for distortion correction) are given, and two pseudo correction operation operators, namely a frequency shift operator H and a sound modulation operator G, under the sound field model are constructed;
performing frequency shift operation on the analyzed signals in the step (1) according to a frequency shift operator to centralize the frequency band to a frequency position k, so as to realize frequency pseudo correction of Doppler distortion of the signals and obtain a transition reference item;
performing spectrum analysis on the transition reference item in the step (3), gathering at a resonance frequency position k according to frequency band energy, calculating a resonance frequency narrowband energy ratio at the position, taking the maximum resonance frequency narrowband energy ratio as an evaluation principle (setting a frequency narrowband and marking a bandwidth as B), optimizing pseudo correction signal parameters based on a global optimization algorithm, and adaptively learning optimal distortion correction parameters;
step (5) correcting parameters (u, M, k) through the optimal distortionoptCalculating a tone operator G to realize sound pressure pseudo correction on the original sound pressure signal and recover the sound pressure amplitude of the sound signal;
step (6) correcting parameters (u, M, k) through the optimal distortionoptAnd (5) reconstructing an acoustic signal emission time sequence, and finally realizing complete signal Doppler distortion correction on the pseudo correction sound pressure signal in the step (5) by utilizing a time domain resampling technology.
In the step (2):
two pseudo correction operation operators, namely a frequency shift operator and an acoustic modulation operator, are constructed respectively based on the orbital edge acoustic propagation theory, and the mathematical expressions are as follows:
frequency shift operator:
tone operators:
wherein,n is the signal length, r is the vertical distance (the vertical distance between the roadside microphone and the sound source moving direction), c is the sound wave speed, and u, M and k are the distortion center time, the wave speed ratio and the resonance frequency position respectively.
In the step (4):
the global optimization algorithm is characterized in that a plurality of initial points are generated by a multi-starting-point method, local extreme points in basins where the initial points are located are searched by a local solver, and a global optimal point is finally achieved.
The invention has the advantages and positive effects that:
(1) in correction parameter adaptive learning, based on an orbital acoustic theory, a pseudo correction of a distorted signal is realized by constructing a frequency shift operator and a tone operator, and a transition reference term with a frequency spectrum resonance band having no distortion and high energy aggregation is obtained. The track side acoustic distortion phenomenon is theoretically depicted and restored, and a self-adaptive distortion correction learning mechanism is physically strengthened.
(2) Compared with other correction methods, the algorithm can adaptively learn the optimal correction parameters from the original sound signal, thereby realizing the complete correction of the distorted signal. The algorithm has certain robustness and high efficiency, and is favorable for online intelligent correction and diagnosis of a follow-up rail-side acoustic system.
Drawings
FIG. 1 is a flow chart of a method for adaptive learning and correcting Doppler acoustic signals according to the present invention;
fig. 2 is a time-domain waveform of a simulated signal, a fourier frequency amplitude spectrum (the resonance frequency is spread out into a 100Hz bandwidth) and its corresponding time-frequency distribution (where the white horizontal line corresponds to the resonance frequency of 1000Hz) (set doppler parameters [ u, M, k ] ═ 0.250,0.0588,500 ]); wherein, fig. 2(a) is a time domain waveform of the simulation signal, fig. 2(b) is a frequency spectrum of the simulation signal, and fig. 2(c) is a time-frequency distribution of the simulation signal;
fig. 3 is a time-frequency distribution (where white horizontal lines correspond to a resonance frequency of 1000Hz) (doppler optimum parameters [ u, M, k ] ═ 0.2415,0.0562,502) corresponding to a time-domain waveform, a fourier frequency amplitude spectrum (resonance frequency centered at 998Hz) based on the adaptive doppler corrected signal; wherein, fig. 3(a) is a time domain waveform of the corrected signal, fig. 3(b) is a frequency spectrum of the corrected signal, and fig. 3(c) is a time-frequency distribution of the corrected signal;
fig. 4 is the envelope power spectrum (theoretical value of failure frequency is 75Hz) of the original signal and the signal after correction based on adaptive doppler, respectively; wherein, fig. 4(a) is the envelope power spectrum of the original signal, and fig. 4(b) is the envelope power spectrum of the corrected signal;
fig. 5 shows a time-domain waveform and a fourier frequency amplitude spectrum of an actual bearing outer ring fault signal and a corresponding time-frequency distribution thereof (doppler parameter [ u, M, k ] ═ unknown,0.0882, unknown ]); wherein, fig. 5(a) is a time domain waveform of an outer ring signal, fig. 5(b) is a frequency spectrum of the outer ring signal, and fig. 5(c) is a time-frequency distribution of the outer ring signal;
fig. 6 shows a time domain waveform and a fourier frequency amplitude spectrum of a signal after adaptive doppler correction and corresponding time-frequency distribution (doppler optimal parameter [ u, M, k ] ═ 0.0756,0.0838,361 ]); wherein, fig. 6(a) is a time domain waveform of the corrected signal, fig. 6(b) is a frequency spectrum of the corrected signal, and fig. 6(c) is a time-frequency distribution of the corrected signal;
fig. 7 is envelope power spectrums of the original signal and the signal corrected based on the adaptive doppler (the theoretical value of the failure frequency of the outer ring of the bearing is 138.74Hz), respectively, where fig. 7(a) is the envelope power spectrum of the original signal, and fig. 7(b) is the envelope power spectrum of the corrected signal.
Detailed Description
The invention is further described with reference to the following figures and examples.
The first embodiment is as follows:
table 1: doppler distortion signal mathematical model parameter
Taking the simulation signal as an example, the simulation signal adopts a single-side attenuated transient periodic signal similar to a fault mechanism:
wherein, ts，ξ，fcAnd fdReceiving time, attenuation damping, resonance frequency and fault frequency of the distorted signal are respectively; constructing an analog signal according to the model parameters in the table 1 and the Morse acoustic theory, and simultaneously adding-10 dB white Gaussian noise to the original signal to obtain a Doppler distortion signal x (t), wherein the fault frequency is fd75Hz, the theoretical value of Doppler distortion parameter is [ u, M, k]＝[0.250,0.0588,500]. The corresponding time domain waveform, fourier frequency amplitude spectrum and the corresponding time frequency distribution are shown in fig. 2. From FIG. 2(b)The Doppler distortion phenomenon of the signal can be easily found, the resonance frequency band of the signal is widened by about 100Hz, and the identification and analysis of the resonance band are interfered. The Doppler distortion signal is corrected by using the self-adaptive learning technology provided by the invention, and the specific operation process is as follows:
1. performing Hilbert transform on an original signal with the duration of 0.5s to obtain an analytic signal of the original signal;
2. randomly setting an initial value [ u, M, k ] of a poylor distortion parameter to [0.01,0.03,200], and giving constraint ranges of [ 00.2 ], [ 0.020.15 ], [ 100600 ];
3. according to the steps (2) and (3) of the invention content, a frequency shift operator H is constructed, and the analysis signal in the step (1) is operated by the frequency shift operator to obtain a transition reference item;
4. according to the step (4) of the invention, calculating a narrow-band energy ratio (setting a frequency narrow band B as 10Hz) of a frequency spectrum of a transition reference item at a resonance frequency k, taking the maximum energy ratio as an energy aggregation evaluation standard, optimizing a pseudo correction signal parameter according to a global optimization algorithm, and outputting a self-adaptive learning distortion correction parameter [ u, M, k ] ═ 0.2415,0.0562,502;
5. and (3) calculating a tone operator G according to the step (2) of the invention content through the acquired optimal Doppler distortion correction parameter, and performing sound pressure pseudo correction on the original distorted sound signal.
6. And (3) establishing a corresponding relation between a sound receiving ridge line and a transmitting time sequence according to the optimal Doppler distortion correction parameter, and completely correcting, recovering and outputting the pseudo sound pressure corrected signal in the step (5) by utilizing a time domain resampling technology (as shown in figure 3). It was found that the doppler phenomenon of the signal was significantly eliminated, while the spectrum had very good energy concentration and concentration, and the corrected resonance frequency was 998Hz, which was substantially the same as the original resonance frequency of 1000 Hz. Meanwhile, the envelope power spectrum of the original signal and the signal corrected based on the adaptive doppler is comparatively analyzed, as shown in fig. 4. It can be easily found that the envelope analysis is performed on the original signal in comparison with the envelope analysis directly performed on the original signalTo a fault frequency fd1154.8Hz, and the fault frequency f diagnosed by the discovery methodd275.2Hz with the same theoretical value fdThe complete coincidence is achieved at 75 Hz. And meanwhile, recovering 2-frequency multiplication information in the envelope spectrum of the corrected signal. The experimental results reflect that the invention has a self-adaptive learning effect on the correction of Doppler distortion signals, and is very beneficial to subsequent fault diagnosis.
Example two:
and testing by adopting a self-designed fault signal of the outer ring of the train bearing. Static fault signals are played by using the linear motion of the automobile, and dynamic Doppler distortion signals are acquired through a B & K microphone 4944-A and an NI PXI-4472/PXI-1033 chassis on the road side. The train bearing model is NJ (P)3226X, and the specific parameters are shown in Table 2.
Table 2: model NJ (P)3226X1 train bearing parameters (unit: mm)
The dynamic doppler fault signal acquisition parameters are shown in table 3:
table 3: train bearing fault signal acquisition parameter
And a single fault with the width of 0.18mm is processed on the outer ring of the bearing by adopting a linear cutting process. Under the collection conditions of Table 3, the bearing outer ring failure frequency is fdThe theoretical value of the speed ratio is M0.0882 at 138.74 Hz. The corresponding time domain waveform, fourier frequency amplitude spectrum and the corresponding time frequency distribution are shown in fig. 5. From FIG. 5, it is easy to find that there is Doppler distortion phenomenon, i.e. signal band broadening, interferenceThe resonance band is identified and analyzed. The Doppler distortion signal is corrected by using the self-adaptive learning technology provided by the invention, and the specific operation process is as follows:
1. performing Hilbert transform on an original signal with the duration of 0.2s to obtain an analytic signal of the original signal;
2. randomly setting an initial value [ u, M, k ] of a poylor distortion parameter to [0.01,0.03,200], and giving constraint ranges of [ 00.2 ], [ 0.020.15 ], [ 100600 ];
3. according to the steps (2) and (3) of the invention content, a frequency shift operator H is constructed, and the analysis signal in the step (1) is operated by the frequency shift operator to obtain a transition reference item;
4. according to the step (4) of the invention, calculating a narrow-band energy ratio (setting a frequency narrow band B to be 10Hz) of a frequency spectrum of a transition reference term at a resonance frequency k, taking the maximum energy ratio as an energy aggregation evaluation standard, optimizing a pseudo correction signal parameter according to a global optimization algorithm, and outputting a self-adaptive learning distortion correction parameter [ u, M, k ] ([ 0.0756,0.0838,361 ];
5. and (3) calculating a tone operator G according to the step (2) of the invention content through the acquired optimal Doppler distortion correction parameter, and performing sound pressure pseudo correction on the original distorted sound signal.
6. And (3) establishing a corresponding relation between a sound receiving ridge line and a transmitting time sequence according to the optimal Doppler distortion correction parameter, and completely correcting, recovering and outputting the pseudo sound pressure corrected signal in the step (5) by utilizing a time domain resampling technology (as shown in figure 6). It was found that the doppler phenomenon of the signal is significantly eliminated, while the spectrum has very good energy concentration and concentration. The envelope power spectra of the original signal and the signal corrected based on adaptive doppler are simultaneously analyzed in comparison, as shown in fig. 7. Compared with the error fault frequency f obtained by directly carrying out envelope analysis on the original signald1143.3Hz, the fault frequency f diagnosed by the discovery methodd2138.7Hz is basically the same as the theoretical value fdCoincide at 138.74 Hz. All in oneSome frequency doubling information is also recovered in the envelope spectrum of the corrected signal. The experimental result further verifies that the method has an important value for the rail-side acoustic diagnosis on the adaptive learning correction effect of the Doppler distortion signal.
In summary, the invention discloses a method for adaptively learning and correcting a doppler acoustic signal, which can combine the rail-side acoustic principle to construct a frequency shift operator and an acoustic modulation operator physical model, and realize pseudo correction on the doppler distortion signal through operating an operator, and has the advantages of robust adaptation, high efficiency and the like. The on-line intelligent correction and diagnosis of the on-orbit acoustic monitoring system has certain application prospect.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (3)
1. A method for adaptively learning and correcting Doppler acoustic signals is characterized in that: the method comprises the following steps:
step (1), obtaining an analytic signal of an acoustic measurement signal through Hilbert transform;
step (2), based on the rail-side acoustic theory, three Doppler distortion parameters are given: and (3) constructing two pseudo correction operators under the sound field model according to the distortion center time u, the wave velocity ratio M and the resonance frequency position k: a frequency shift operator H and an tone operator G;
performing frequency shift operation on the analyzed signal in the step (1) according to a frequency shift operator H to centralize a frequency band to a frequency position k, so as to realize frequency pseudo correction of Doppler distortion of the signal and obtain a transition reference item;
performing spectrum analysis on the transition reference item in the step (3), according to the aggregation of frequency band energy at a resonance frequency position k, calculating the resonance frequency narrowband energy ratio at the position and taking the maximum of the resonance frequency narrowband energy ratio as an evaluation principle, setting a frequency narrowband, marking the bandwidth as B, optimizing pseudo correction signal parameters based on a global optimization algorithm, and finally learning distortion correction parameters in a self-adaptive manner;
step (5) correcting parameters (u, M, k) through the optimal distortionoptAcquiring a tone operator G to realize sound pressure pseudo correction on the original sound pressure signal and restore the amplitude of the sound signal;
step (6) correcting parameters (u, M, k) through the optimal distortionoptAnd (5) reconstructing an acoustic signal emission time sequence, and finally realizing complete signal Doppler distortion correction on the sound pressure pseudo correction signal in the step (5) by utilizing a time domain resampling technology.
2. The adaptive learning-based doppler distortion acoustic signal correction method according to claim 1, wherein: in the step (2): two pseudo correction operation operators, namely a frequency shift operator and an acoustic modulation operator, are constructed respectively based on the orbital edge acoustic propagation theory, and the mathematical expressions are as follows:
frequency shift operator:
tone operators:
wherein,n is the signal length, r is the vertical distance, i.e. the vertical distance between the roadside microphone and the sound source movement direction, c isThree parameters of acoustic velocity, u, M and k are respectively the distortion center time, the wave velocity ratio and the resonance frequency position.
3. The adaptive learning-based doppler distortion acoustic signal correction method according to claim 1, wherein: in the step (4): the global optimization algorithm is characterized in that a plurality of initial points are generated by a multi-starting-point method, local extreme points in basins where the initial points are located are searched by a local solver, and a global optimal point is finally achieved.
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| CN109406147A (en) * | 2018-10-29 | 2019-03-01 | 安徽大学 | Train bearing rail side acoustic diagnosis method under variable speed working condition |
| CN109406147B (en) * | 2018-10-29 | 2020-11-13 | 安徽大学 | Train bearing rail side acoustic diagnosis method under variable speed working condition |
| CN110472305A (en) * | 2019-07-26 | 2019-11-19 | 西北工业大学 | The dimensionally-optimised method of film-type acoustic metamaterial based on genetic algorithm |
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