Doppler Distortion Removal Method for Multiple Acoustic Sources

2013 ◽  
Vol 373-375 ◽  
pp. 874-879
Author(s):  
Ao Zhang ◽  
Fang Liu ◽  
Fan Rang Kong
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Acoustic Signal ◽  
Instantaneous Frequency ◽  
Signal Analysis ◽  
Acoustic Signals ◽  
Acoustic Source ◽  
Sampling Sequence ◽  
Acoustic Sources ◽  
Dopplerlet Transform

In wayside fault diagnosis of train bearings, the phenomenon of Doppler distortion in the acoustic signal of moving acoustic source acquired with a microphone leads to the difficulty for signal analysis. In this paper, a new method based on Dopplerlet transform and re-sampling is proposed to eliminate the Doppler distortion of multiple acoustic sources which provide a reference for wayside fault diagnosis of train bearings. Firstly, search the parameters space to find the primary functionsDopplerlet atoms. According to the Morse acoustic theory, the instantaneous frequency of the Dopplerlet atom which we choose to remove Doppler distortion of the corresponding acoustic source can be acquired. Then, the re-sampling sequence can be established in time domain. Through the resample, the Doppler distortion can be removed. In the end of this paper, an experiment with practical acoustic signals is carried out, and the results verified the effectiveness of this method.

Doppler distortion removal based on Dopplerlet transform and re-sampling for wayside fault diagnosis of train bearings

2020 ◽  
pp. 095440622096956
Author(s):  
Ao Zhang ◽  
Changqing Shen ◽  
Qingbo He ◽  
Fei Hu ◽  
Fang Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Signal Analysis ◽  
Acoustic Source ◽  
Multiple Sources ◽  
Outer Race ◽  
Sampling Sequence ◽  
Distortion Effect ◽  
Dopplerlet Transform ◽  
Robust To Noise ◽  
Inner Race

In wayside fault diagnosis of train bearings, the phenomenon of Doppler distortion in the acoustic signal of moving acoustic source acquired with a microphone leads to the difficulty for signal analysis. In this paper, a new method based on Dopplerlet transform and re-sampling is proposed to remove the Doppler distortion, and applied in the fault diagnosis of train bearings. Firstly, search the parameters space to find the primary functions-Dopplerlet atoms. According to the Morse acoustic theory and Doppler effect, the instantaneous frequency of the Dopplerlet atom which we choose to remove Doppler distortion of the corresponding acoustic source can be acquired. Then, the re-sampling sequence can be established as the re-sampling vector in time domain. Through the resample, the Doppler distortion effect can be removed. Finally, simulations and experiments with practical acoustic signals of train bearings with a defect on the outer race and the inner race are carried out, and the results verified the effectiveness of this method. Comparing with the other methods of Doppler distortion removal, this method works without measuring the motion parameters in advance, and is quite robust to noise. Meanwhile, this method has the potential to eliminate the Doppler distortion of original signal with multiple sources.

Doppler Shift Removal Based on Instantaneous Frequency Estimation for Wayside Fault Diagnosis of Train Bearings

2014 ◽  
Vol 136 (2) ◽  
Author(s):  
Ao Zhang ◽  
Fei Hu ◽  
Qingbo He ◽  
Changqing Shen ◽  
Fang Liu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Acoustic Signal ◽  
Instantaneous Frequency ◽  
Doppler Shift ◽  
High Speed ◽  
Frequency Estimation ◽  
Acoustic Signals ◽  
Instantaneous Frequency Estimation ◽  
Comparison Results ◽  
Short Time

The phenomenon of Doppler shift in the acoustic signal acquired by a microphone amounted beside the railway leads to the difficulty for fault diagnosis of train bearings with a high moving speed. To enhance the condition monitoring performance of the bearings on a passing train using stationary microphones, the elimination of the Doppler shift should be implemented firstly to correct the severe frequency-domain distortion of the acoustic signal recorded in these conditions. In this paper, a Doppler shift removal method is proposed based on instantaneous frequency (IF) estimation (IFE) for analyzing acoustic signals from train bearings with a high speed. Specifically, the IFE based on short-time Fourier transform is firstly applied to attain the IF vector. According to the acoustic theory of Morse, the data fitting is then carried out to achieve the fitting IFs with which the resampling sequence can be established as the resampling vector in time domain. The resampled signal can be finally reconstructed to realize fault diagnosis of train bearings. To demonstrate the effectiveness of this method, two simulations and an experiment with practical acoustic signals of train bearings with a crack on the outer raceway and the inner raceway have been carried out, and the comparison results have been presented in this paper.

Doppler Shift Elimination Method Based on Instantaneous Frequency Estimation for the Wayside Acoustic Signal

2013 ◽  
Vol 333-335 ◽  
pp. 510-515 ◽  
Author(s):  
Fei Hu ◽  
Chang Qing Shen ◽  
Fang Liu ◽  
Ao Zhang ◽  
Fan Rang Kong
Keyword(s):  
Acoustic Signal ◽  
Instantaneous Frequency ◽  
Doppler Shift ◽  
Frequency Estimation ◽  
Elimination Method ◽  
Instantaneous Frequency Estimation ◽  
Synthetic Signal ◽  
Sampling Sequence ◽  
Sampled Signal ◽  
Frequency Components

The phenomenon of Doppler Shift leads to the difficulty for fault diagnosis of train bearings with a high moving speed. So the elimination of the Doppler shift should be implemented firstly. A Doppler Shift elimination method for the wayside acoustic signal is proposed. The instantaneous frequency estimation based on STFT was applied to attain the IF vector. According to the acoustic theory of Morse, the data fitting was then carried out to achieve the fitting IFs, with which the re-sampling sequence could be established to eliminate Doppler Shift. To demonstrate the validity of this method, an experiment with the synthetic signal containing several frequency components had been carried out. The results show that the re-sampled signal was revised without Doppler Shift.

Gear Local Fault Diagnosis with Empirical Mode Decomposition and Hilbert Huang Transformation

2011 ◽  
Vol 199-200 ◽  
pp. 899-904 ◽  
Author(s):  
Zhen Nan Han ◽  
Jian Xin Gao
Keyword(s):  
Fault Diagnosis ◽  
Empirical Mode Decomposition ◽  
Instantaneous Frequency ◽  
Signal Analysis ◽  
Physical Simulation ◽  
Gear Tooth ◽  
Vibration Signal ◽  
Vibration Energy ◽  
Mode Decomposition ◽  
Vibration Signal Analysis

A new method for gear local fault diagnosis based on vibration signal analysis is presented in this paper by using the concept of instantaneous frequency. The data from the physical simulation are used to detect the change in the instantaneous frequency and meshing vibration energy of the gear tooth fault by Empirical Mode Decomposition and Hilbert Huang Transformation (EMD-HHT). It is verified that method is effective by rig testing of geared system.

scholarly journals Fault Diagnosis of Planetary Gearbox Based on Motor Current Signal Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Ziyuan Jiang ◽  
Qinkai Han ◽  
Xueping Xu
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Signal Analysis ◽  
Current Signal ◽  
Learning Models ◽  
Planetary Gearbox ◽  
Motor Current ◽  
The Time Domain ◽  
Machine Learning Models

Planetary gearbox is one of the most widely used core parts in heavy machinery. Once it breaks down, it can lead to serious accidents and economic loss. Induction motor current signal analysis (MCSA) is a noninvasive method that uses current to detect faults. Currently, most MCSA-based fault diagnosis studies focus on the parallel shaft gearbox. However, there is a paucity of studies on the planetary gearbox. The effect of various signal processing methods on motor current and the performance of different machine learning models are rarely compared. Therefore, fault diagnosis of planetary gearbox based MCSA is conducted in this study. First, the effects of various faults on motor currents are studied. Specifically, the characteristic frequencies of a fault in sun/planet/ring gears and supporting bearings of the planetary gearbox are derived. Then, a signal preprocessing method, namely, singular spectrum analysis (SSA), is proposed to remove the supply frequency component in the current signal. Subsequently, four classical machine learning models, including the support vector machine (SVM), decision tree (DT), random forest (RF), and AdaBoost, are used for fault classifications based on the features extracted via principal component analysis (PCA). The convolutional neural network (CNN), which can automatically extract features, is also adopted. The dynamic experiment of the planetary gearbox with seven types of faults, including tooth chipping in sun/planet/ring gears, inner race spall in planet bearing, inner/outer races, and ball spalls in input support bearing, is conducted. Raw current signal in the time domain, reconstructed signal by SSA, and the current spectra in the frequency domain are used as the inputs of various models. The classification results show that the PCA-SVM is the best model for learned data while CNN is the best model for unlearned data on average. Furthermore, SSA mainly increases the accuracy of CNN in the time domain and exhibits a positive effect on unlearned data in the time domain. The classification accuracy increases significantly after transforming the time domain current data to the frequency domain.

scholarly journals Improved Empirical Wavelet Transform for Compound Weak Bearing Fault Diagnosis with Acoustic Signals

2020 ◽  
Vol 10 (2) ◽  
pp. 682 ◽  
Author(s):  
Chaoren Qin ◽  
Dongdong Wang ◽  
Zhi Xu ◽  
Gang Tang
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Frequency Modulation ◽  
Acoustic Signal ◽  
High Frequency ◽  
Acoustic Signals ◽  
Bearing Fault ◽  
Low Snr ◽  
Bearing Fault Diagnosis ◽  
Empirical Wavelet Transform

Most of the current research on the diagnosis of rolling bearing faults is based on vibration signals. However, the location and number of sensors are often limited in some special cases. Thus, a small number of non-contact microphone sensors are a suboptimal choice, but it will result in some problems, e.g., underdetermined compound fault detection from a low signal-to-noise ratio (SNR) acoustic signal. Empirical wavelet transform (EWT) is a signal processing algorithm that has a dimension-increasing characteristic, and is beneficial for solving the underdetermined problem with few microphone sensors. However, there remain some critical problems to be solved for EWT, especially the determination of signal mode numbers, high-frequency modulation and boundary detection. To solve these problems, this paper proposes an improved empirical wavelet transform strategy for compound weak bearing fault diagnosis with acoustic signals. First, a novel envelope demodulation-based EWT (DEWT) is developed to overcome the high frequency modulation, based on which a source number estimation method with singular value decomposition (SVD) is then presented for the extraction of the correct boundary from a low SNR acoustic signal. Finally, the new fault diagnosis scheme that utilizes DEWT and SVD is compared with traditional methods, and the advantages of the proposed method in weak bearing compound fault diagnosis with a single-channel, low SNR, variable speed acoustic signal, are verified.

Microbubble generation and trapping induced by femtosecond laser and acoustic signal analysis

2017 ◽  
Vol 34 (1-2) ◽  
pp. 33-44
Author(s):  
Kun YANG ◽  
Linyan XUE ◽  
Kang YIN ◽  
Shuang LIU ◽  
Jie MENG
Keyword(s):  
Femtosecond Laser ◽  
Acoustic Signal ◽  
Signal Analysis ◽  
Acoustic Signal Analysis

Separating Multiple Moving Sources by Microphone Array Signals for Wayside Acoustic Fault Diagnosis

2019 ◽  
Vol 141 (5) ◽  
Author(s):  
Wei Xiong ◽  
Qingbo He ◽  
Zhike Peng
Keyword(s):  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Matching ◽  
Matching Pursuit ◽  
Microphone Array ◽  
Time Frequency ◽  
Moving Sources ◽  
The Time Domain ◽  
Envelope Spectrum ◽  
Threshold Setting

Wayside acoustic defective bearing detector (ADBD) system is a potential technique in ensuring the safety of traveling vehicles. However, Doppler distortion and multiple moving sources aliasing in the acquired acoustic signals decrease the accuracy of defective bearing fault diagnosis. Currently, the method of constructing time-frequency (TF) masks for source separation was limited by an empirical threshold setting. To overcome this limitation, this study proposed a dynamic Doppler multisource separation model and constructed a time domain-separating matrix (TDSM) to realize multiple moving sources separation in the time domain. The TDSM was designed with two steps of (1) constructing separating curves and time domain remapping matrix (TDRM) and (2) remapping each element of separating curves to its corresponding time according to the TDRM. Both TDSM and TDRM were driven by geometrical and motion parameters, which would be estimated by Doppler feature matching pursuit (DFMP) algorithm. After gaining the source components from the observed signals, correlation operation was carried out to estimate source signals. Moreover, fault diagnosis could be carried out by envelope spectrum analysis. Compared with the method of constructing TF masks, the proposed strategy could avoid setting thresholds empirically. Finally, the effectiveness of the proposed technique was validated by simulation and experimental cases. Results indicated the potential of this method for improving the performance of the ADBD system.

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