Looseness Identification of Track Fasteners Based on Ultra-Weak FBG Sensing Technology and Convolutional Autoencoder Network

Abstract

Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness.

Looseness inspection of fasteners based on digital shearing speckle pattern interferometry and convolutional neural network

“…In recent years, multiple researchers have proposed fastener looseness inspection methods, with significant efforts focused on sensor-based inspection methods. For example, Li et al proposed a method based on a convolutional autoencoding network, using ultra-weak fiber Bragg grating sensing arrays to identify the fastener loosening status by collecting vibration information from the distributed vibration responses of track beds; 2 Zhang et al compared two acoustic methods to identify the loose state of three types of joints and find a good agreement between theoretical prediction and experimental estimate. 3 In addition, Wang et al utilized entropy-based active sensing to capture various types of signals and employed a genetic algorithm-based least square support vector machine (SVM) to learn and recognize these signals, ultimately enabling the detection of loose fasteners.…”

“…For example, Li et al. proposed a method based on a convolutional autoencoding network, using ultra-weak fiber Bragg grating sensing arrays to identify the fastener loosening status by collecting vibration information from the distributed vibration responses of track beds; 2 Zhang et al. compared two acoustic methods to identify the loose state of three types of joints and find a good agreement between theoretical prediction and experimental estimate 3 .…”