An improved AE detection method of rail defect based on multi-level ANC with VSS-LMS

Abstract In order to ensure the safety and reliability of railway system, Acoustic Emission (AE) method is employed to investigate rail defect detection. However, little attention has been paid to the defect detection at high speed, especially for noise interference suppression. Based on AE technology, this paper presents an improved rail defect detection method by multi-level ANC with VSS-LMS. Multi-level noise cancellation based on SANC and ANC is utilized to eliminate complex noises at high speed, and tongue-shaped curve with index adjustment factor is proposed to enhance the performance of variable step-size algorithm. Defect signals and reference signals are acquired by the rail-wheel test rig. The features of noise signals and defect signals are analyzed for effective detection. The effectiveness of the proposed method is demonstrated by comparing with the previous study, and different filter lengths are investigated to obtain a better noise suppression performance. Meanwhile, the detection ability of the proposed method is verified at the top speed of the test rig. The results clearly illustrate that the proposed method is effective in detecting rail defects at high speed, especially for noise interference suppression.

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

[2]  K. Ono.  Application of acoustic emission for structure diagnosis , 2011 .

[3]  W. H. Prosser,et al.  Monitoring Damage Growth in Titanium Matrix Composites Using Acoustic Emission , 1994 .

[4]  Cristobal Ruiz-Carcel,et al.  Application of Linear Prediction, Self-Adaptive Noise Cancellation, and Spectral Kurtosis in Identifying Natural Damage of Rolling Element Bearing in a Gearbox , 2015 .

[5]  Martine Wevers,et al.  Listening to the sound of materials: Acoustic emission for the analysis of material behaviour , 1997 .

[6]  Kristoffer Bruzelius,et al.  An initial investigation on the potential applicability of Acoustic Emission to rail track fault detection , 2004 .

[7]  Robert B. Randall,et al.  Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms , 2004 .

[8]  M. N. Bassim.  Detection of fatigue crack propagation with acoustic emission , 1992 .

[9]  D. Bianchi,et al.  Wavelet packet transform for detection of single events in acoustic emission signals , 2015 .

[10]  Esben Auken,et al.  Adaptive noise cancelling of multichannel magnetic resonance sounding signals , 2012 .

[11]  Yimin Shao,et al.  Adaptive noise cancellation based on beehive pattern evolutionary digital filter , 2014 .

[12]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[13]  Robin Clark,et al.  Rail flaw detection: overview and needs for future developments , 2004 .

[14]  M. A. Hamstad,et al.  On the far-field structure of waves generated by a pencil lead break on a thin plate , 1994 .

[15]  Andrew Ball,et al.  Adaptive noise cancelling and time-frequency techniques for rail surface defect detection , 2015 .

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

[17]  Sophie Hallstedt,et al.  A model-based approach for sustainability and value assessment in the aerospace value chain , 2015 .

[18]  Mario Carpentieri,et al.  A framework for the damage evaluation of acoustic emission signals through Hilbert-Huang transform , 2016 .

[19]  Wu Zheng-guo,et al.  Variable Step Adaptive Filtering LMS Algorithm Based on Tongue-Like Curve , 2004 .

[20]  Zheng Qian,et al.  Planetary gearbox fault feature enhancement based on combined adaptive filter method , 2015 .

[21]  Yi Shen,et al.  Acoustic emission detection of rail defect based on wavelet transform and Shannon entropy , 2015 .

[22]  Yi Shen,et al.  An analysis of the simulated acoustic emission sources with different propagation distances, types and depths for rail defect detection , 2014 .