Doppler distortion elimination using short-time sparse singular value decomposition strategy for wayside acoustic source fault diagnosis

Abstract

With the rapid development of transportation, wayside condition monitoring and fault diagnosis of key acoustic sources has attracted considerable attentions because of low cost and high efficiency. However, serious Doppler distortion exists in the wayside acquired signals when the monitored moving source passes by the system at high velocity, which makes it difficult for condition monitoring and fault diagnosis. This paper presents a novel method involving short-time sparse singular value decomposition to eliminate Doppler distortion in the wayside acquired signals based on a microphone array. The procedure of the proposed short-time sparse singular value decomposition is performed as follows. First, the Doppler distorted array signals are decomposed into a series of array segments by a sliding window with a proper window length. Afterwards, the time-varying direction of arrival of the corresponding array segments is acquired by individual sparse singular value decomposition. Then the fitting time-varying directions of arrival and time-domain interpolation resampling are employed to correct the Doppler distorted signals for recovering the objective characteristic frequency. Simulation analysis has validated that under heavy background noise situation, better localization accuracy and effectiveness of the proposed short-time sparse singular value decomposition could be achieved in comparison with other strategies like short-time multiple signal classification. Besides, the real data cases have verified the effectiveness of the proposed method, which shows great potential applications in wayside condition monitoring and fault diagnosis system for moving vehicles, trains, planes, etc.

Keywords

Microphone array Doppler distortion sparse singular value decomposition moving acoustic source condition monitoring and fault diagnosis

Get full access to this article

View all access options for this article.

References

Choe

Wan

Chan

. Neural pattern identification of railroad wheel-bearing faults from audible acoustic signals: comparison of FFT, CWT and DWT features. SPIE Proc Wavelet Appl 1997; 3087: 480–496.

Sneed WH and Smith RL. On-board real-time railroad bearing defect detection and monitoring. In: Proceedings of the 1998 ASME/IEEE JointRailroad conference 1998, pp.149–153. New York: IEEE.

Cao

Fan

Zhou

et al.

Wheel-bearing fault diagnosis of trains using empirical wavelet transform. Measurement 2016; 82: 439–449.

Wang

Chen

Tong

et al.

Matching synchrosqueezing wavelet transform and application to aeroengine vibration monitoring. IEEE Trans Instrum Meas 2017; 66: 360–372.

Yan

Gao

Chen

. Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 2014; 96: 1–15.

Wan

et al.

Improved ensemble superwavelet transform for vibration-based machinery fault diagnosis. J Manuf Sci Eng 2016; 138: 071012-071012–071019.

Zhang

Chen

et al.

Periodic impulsive fault feature extraction of rotating machinery using dual-tree rational dilation complex wavelet transform. J Manuf Sci Eng 2014; 136: 051011-051011–051016.

Laha

. Enhancement of fault diagnosis of rolling element bearing using maximum kurtosis fast nonlocal means denoising. Measurement 2017; 100: 157–163.

Dong

Sun

Tang

et al.

Bearing degradation state recognition based on kernel PCA and wavelet kernel SVM. Proc IMechE, Part C: J Mechanical Engineering Science 2014; 229: 2827–2834.

10.

Wang

Peter

Tsui

. An enhanced Kurtogram method for fault diagnosis of rolling element bearings. Mech Syst Signal Process 2013; 35: 176–199.

11.

Nguyen

Kang

Kim

J-M

et al.

Robust condition monitoring of rolling element bearings using de-noising and envelope analysis with signal decomposition techniques. Expert Syst Appl 2015; 42: 9024–9032.

12.

Jiang

Chen

Dong

et al.

The weak fault diagnosis and condition monitoring of rolling element bearing using minimum entropy deconvolution and envelop spectrum. Proc IMechE, Part C: J Mechanical Engineering Science 2012; 227: 1116–1129.

13.

Cline JE, Bilodeau JR and Smith RL. Acoustic wayside identification of freight car roller bearing defects. In: Proceedings of the 1998 ASME/IEEE joint railroad conference, 1998, pp.79–83. New York: IEEE.

14.

Florom R. Wayside acoustic detection of railroad roller bearing defects. ASME Paper 87-WA/RT-11, 1988, pp.79–92.

15.

Wang

et al.

Wayside acoustic diagnosis of defective train bearings based on signal resampling and information enhancement. J Sound Vib 2013; 332: 5635–5649.

16.

Dybała

Radkowski

. Reduction of Doppler effect for the needs of wayside condition monitoring system of railway vehicles. Mech Syst Signal Process 2013; 38: 125–136.

17.

Liu

Kong

et al.

Doppler effect reduction based on time-domain interpolation resampling for wayside acoustic defective bearing detector system. Mech Syst Signal Process 2014; 46: 253–271.

18.

Liu

Shen

et al.

Doppler effect reduction scheme via acceleration-based Dopplerlet transform and resampling method for the wayside acoustic defective bearing detector system. Proc IMechE, Part C: J Mechanical Engineering Science 2014; 228: 3356–3373.

19.

Shen

Cai

et al.

A parameterized Doppler distorted matching model for periodic fault identification in locomotive bearing. Proc IMechE, Part C: J Mechanical Engineering Science 2015; 230: 3791–3802.

20.

Knapp

Carter

. The generalized correlation method for estimation of time delay. IEEE Trans Acoust Speech Signal Process 1976; 24: 320–327.

21.

Benesty

Jingdong

Yiteng

. Time-delay estimation via linear interpolation and cross correlation. IEEE Trans Speech Audio Process 2004; 12: 509–519.

22.

Schmidt RO. A signal subspace approach to multiple emitter location and spectral estimation. PhD Dissertation, Stanford University, USA, 1982.

23.

Johnson

Dudgeon

. Array signal processing: concepts and techniques, New York: Simon & Schuster, 1992.

24.

Cardenas Cabada

Leclere

Antoni

et al.

Fault detection in rotating machines with beamforming: Spatial visualization of diagnosis features. Mech Syst Signal Process 2017; 97: 33–43.

25.

Paulraj A, Roy R and Kailath T. Estimation of signal parameters via rotational invariance techniques-ESPRIT. In: 1985 Nineteeth Asilomar conference of circuits, systems and computers, 1985, pp.83–89. New York: IEEE.

26.

Zhang

et al.

Doppler correction using short-time MUSIC and angle interpolation resampling for wayside acoustic defective bearing diagnosis. IEEE Trans Instrum Meas 2017; 66: 671–680.

27.

Malioutov

Cetin

Willsky

. A sparse signal reconstruction perspective for source localization with sensor arrays. IEEE Trans Signal Process 2005; 53: 3010–3022.

28.

Sun

Wang

et al.

Source localization for sparse array using nonnegative sparse Bayesian learning. Signal Process 2016; 127: 37–43.

29.

Carlin

Rocca

Oliveri

et al.

Directions-of-arrival estimation through Bayesian compressive sensing strategies. IEEE Trans Antennas Propag 2013; 61: 3828–3838.

30.

Kwok

Jones

. Improved instantaneous frequency estimation using an adaptive short-time Fourier transform. IEEE Trans Signal Process 2000; 48: 2964–2972.

31.

Morse

. Theoretical acoustics, Princeton, NJ: Princeton University Press, 1968.

32.

Ouyang

Zhang

et al.

Online Doppler effect elimination based on unequal time interval sampling for wayside acoustic bearing fault detecting system. Sensors 2015; 15: 21075–21098.

33.

Zhang

Ouyang

et al.

Multi-bearing weak defect detection for wayside acoustic diagnosis based on a time-varying spatial filtering rearrangement. Mech Syst Signal Process 2018; 100: 224–241.

34.

Mailloux

. Phased array antenna handbook, Boston, MA: Artech House, 2005.