A new rail crack detection method using LSTM network for actual application based on AE technology. (15th December 2018)

• Record Type: Journal Article
• Authors: Zhang, Xin; Zou, Zhongxian; Wang, Kangwei; Hao, Qiushi; Wang, Yan; Shen, Yi; Hu, Hengshan
• Is Part Of: Applied acoustics. Volume 142(2018)
• Page Start: 78
• Page End: 86
• Publication Date: 2018-12-15
• Subjects: Acoustic emission -- Rail crack detection -- Noise elimination -- Long Short-Term Memory network; Acoustical engineering -- Periodicals; Periodicals; 620.2
• DOI: 10.1016/j.apacoust.2018.08.020
• Languages: English
• ISSNs: 0003-682X

Abstract

Highlights: A two-level structure with LSTM network is proposed to detect rail crack signal. The improved noise model is built by multiple known kinds of noise signals. The unknown noise interference is removed by the model of crack signal. The investigation is carried out in the real noise environment of railway. Abstract: In order to use acoustic emission (AE) technology in the actual application of rail crack detection, an important problem to be solved is how to overcome the noise interference of wheel-rail contact movement. In this paper, a new method is proposed to eliminate noise interference and detect rail crack signal based on AE technology, which has a two-level structure with Long Short-Term Memory (LSTM) network. At the first level, an improved noise model from multiple kinds of noise signals is built by the LSTM network. This model is used to eliminate the known noise signals. At the second level, the model of crack signal is built to remove the unknown noise interference from the denoised signal of the first level. Based on the proposed two-level structure, the crack signals can be detected. All the AE signals are acquired from the real noise environment of railway. Meanwhile, the detection ability of the proposed method is analyzed and verified. The results demonstrate that the proposed method is effective to detect crack signals in actual application. It can provide a useful guidance for AE detection of rail cracks.