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

• Published in: Mechanical systems and signal processing Vol. 99; pp. 420 - 433
• Main Authors: Zhang, Xin, Cui, Yiming, Wang, Yan, Sun, Mingjian, Hu, Hengshan
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 15.01.2018; Elsevier BV
• Subjects: Acoustic emission; Acoustic emission testing; Acoustic noise; Adaptive noise cancellation; Emission analysis; High speed; Interference; Rail defect detection; Railways; Reference signals; Studies; Tongue; Variable step-size algorithm; Acoustic emission; Variable step-size algorithm; Rail defect detection; Adaptive noise cancellation
• ISSN: 0888-3270; 1096-1216
• DOI: 10.1016/j.ymssp.2017.06.029

•An improved AE detection method of rail defect is proposed.•A rail-wheel test rig is set up to obtain the defect signals and noise signals.•Multi-level noise cancellation is employed to eliminate complex noises at high speed.•Improved tongue-like curve is utilized to enhance the performance of LMS.•Filter lengths are investigated to obtain a better noise suppression performance. 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.