Blind source extraction of acoustic emission signals for rail cracks based on ensemble empirical mode decomposition and constrained independent component analysis

Abstract In order to detect rail cracks by acoustic emission (AE) technology, a constrained independent component analysis algorithm is proposed to extract investigated components sequentially. Standard independent component analysis is modified through the iterative constraints of higher-order statistics, including the recursive kurtosis and waveform factor, to extract the required signals more accurately and efficiently. The proposed method is combined with ensemble empirical mode decomposition and initially verified by a single-channel simulation, which manifests a better separation efficiency and a higher robustness in recovering sources. To further testify the practicability, constrained independent component analysis is applied in single-channel experiment in an actual operating railway. It can be concluded that the proposed method is effective to detect crack signals in real noise environment and occurrence of crack signals can be distinguished from noisy mixtures, which can provide a guidance in the actual application of AE detection of rail cracks.

[1]  Fang Liu,et al.  Trajectory optimization by using EMD- and ICA-based processing method , 2019 .

[2]  Ying Wang,et al.  Sparse representation approach to data compression for strain-based traffic load monitoring: A comparative study , 2017, Measurement.

[3]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[4]  Luay Yassin Taha,et al.  Efficient blind source extraction of noisy mixture utilising a class of parallel linear predictor filters , 2018, IET Signal Process..

[5]  Yu Zhou,et al.  Constrained independent component analysis and its application to machine fault diagnosis , 2011 .

[6]  Nan Pan,et al.  Mechanical compound faults extraction based on improved frequency domain blind deconvolution algorithm , 2017, Mechanical Systems and Signal Processing.

[7]  Yi Shen,et al.  An investigation on rail health monitoring using acoustic emission technique by tensile test , 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[8]  Toshihiro Furukawa,et al.  Determined and overdetermined convolutive blind source extractions by approximate joint diagonalization , 2019, Acoustical Science and Technology.

[9]  Zhang Yi,et al.  Robust extraction of specific signals with temporal structure , 2006, Neurocomputing.

[10]  A Anastasopoulos,et al.  Acoustic emission monitoring of wheel sets on moving trains , 2013 .

[11]  Dorothy V. M. Bishop,et al.  Journal of Neuroscience Methods , 2015 .

[12]  Andrew D. Back,et al.  A First Application of Independent Component Analysis to Extracting Structure from Stock Returns , 1997, Int. J. Neural Syst..

[13]  Yan Wang,et al.  A new rail crack detection method using LSTM network for actual application based on AE technology , 2018, Applied Acoustics.

[14]  Piet C. W. Sommen,et al.  A single stage approach to blind source extraction based on second order statistics , 2013, Signal Process..

[15]  Noureddine Zerhouni,et al.  Tool wear condition monitoring based on continuous wavelet transform and blind source separation , 2018, The International Journal of Advanced Manufacturing Technology.

[16]  Yi Shen,et al.  A signal-adapted wavelet design method for acoustic emission signals of rail cracks , 2018, Applied Acoustics.

[17]  Qiang Wang,et al.  Accent extraction of emotional speech based on modified ensemble empirical mode decomposition , 2010, 2010 IEEE Instrumentation & Measurement Technology Conference Proceedings.

[18]  Saeid Sanei,et al.  Constrained Blind Source Extraction of Readiness Potentials From EEG , 2013, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[19]  Addisson Salazar,et al.  A general procedure for learning mixtures of independent component analyzers , 2010, Pattern Recognit..

[20]  Fathi M. Salem,et al.  Blind information-theoretic multiuser detection algorithms for DS-CDMA and WCDMA downlink systems , 2005, IEEE Transactions on Neural Networks.

[21]  Pierre Comon,et al.  Handbook of Blind Source Separation: Independent Component Analysis and Applications , 2010 .

[22]  Mohammad Bagher Shamsollahi,et al.  Extraction and Automatic Grouping of Joint and Individual Sources in Multisubject fMRI Data Using Higher Order Cumulants , 2019, IEEE Journal of Biomedical and Health Informatics.

[23]  Sung-Nien Yu,et al.  Selection of significant independent components for ECG beat classification , 2009, Expert Syst. Appl..

[24]  Robert Lewis Reuben,et al.  Rail–wheel interaction monitoring using Acoustic Emission: A laboratory study of normal rolling signals with natural rail defects , 2010 .

[25]  Dezhong Peng,et al.  Second-Order Cyclostationary Statistics-Based Blind Source Extraction From Convolutional Mixtures , 2017, IEEE Access.

[26]  Pengfei Wang,et al.  Blind separation of temporally correlated noncircular sources using complex matrix joint diagonalization , 2019, Pattern Recognit..

[27]  Dmitri B. Chklovskii,et al.  Blind Nonnegative Source Separation Using Biological Neural Networks , 2017, Neural Computation.

[28]  Pengju He,et al.  Single channel blind source separation on the instantaneous mixed signal of multiple dynamic sources , 2017, Mechanical Systems and Signal Processing.

[29]  Mayorkinos Papaelias,et al.  Onboard detection of railway axle bearing defects using envelope analysis of high frequency acoustic emission signals , 2016 .

[30]  Robert X. Gao,et al.  Performance enhancement of ensemble empirical mode decomposition , 2010 .

[31]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[32]  Liang Fang,et al.  A fault feature extraction method for rotating shaft with multiple weak faults based on underdetermined blind source signal , 2018, Measurement Science and Technology.

[33]  Jun Peng,et al.  Using Joint Generalized Eigenvectors of a Set of Covariance Matrix Pencils for Deflationary Blind Source Extraction , 2018, IEEE Transactions on Signal Processing.

[34]  Hongchao Wang,et al.  A two-step blind source extraction method and its application in fault diagnosis of rolling element bearing , 2019, Journal of Mechanical Science and Technology.

[35]  Tielin Shi,et al.  Multiband Envelope Spectra Extraction for Fault Diagnosis of Rolling Element Bearings , 2018, Sensors.

[36]  Hongyan Ma,et al.  Defect detection and location in switch rails by acoustic emission and Lamb wave analysis: A feasibility study , 2016 .

[37]  Mahesh Chandra,et al.  An improved wavelet-based signal-denoising architecture with less hardware consumption , 2019 .