Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data

Abstract

Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings.

Novel sound event and sound activity detection framework based on intrinsic mode functions and deep learning

“…In addition to MFCC, features such as linear predictive coding [12], discrete cosine transforms [13,14], wavelet [9,15], Perceptual Linear Prediction (PLP) [16], Linear Prediction Cepstral Coefficients (LPCC) [17], and Line Spectral Frequencies (LSF) [18] have been used in various studies for SED. MFCC has been used as a usual feature in a wide range of acoustic and sound-based machine-learning methods, for example, in voice disorder detection [19], emotion recognition [20][21][22], singing voice separation [23], fault detection using acoustic and sound data [24,25], leak detection [26] and tree cutting events detection [27].…”

A Deep-LSTM-Based Fault Detection Method for Railway Vehicle Suspensions

“…This approach involves learning from normal operational data to build a predictive model and then to predict normal data, establishing a distribution of prediction errors. Rustam et al [36] propose a method for detecting railway track faults using machine learning and deep learning models, in which acoustic data are analyzed to identify different types of railway track faults. Eunus et al [37] combine convolutional autoencoders, a ResNet-based RNN, and LSTM to analyze images of railway tracks for detection.…”