Regular inspection of railway track health is crucial for maintaining safe and reliable train operations. Factors, such as cracks, ballast issues, rail discontinuity, loose nuts and bolts, burnt wheels, superelevation, and misalignment developed on the rails due to non-maintenance, pre-emptive investigations and delayed detection, pose a grave danger and threats to the safe operation of rail transport. The traditional procedure of manually inspecting the rail track using a railway cart is both inefficient and prone to human error and biases. In a country like Pakistan where train accidents have taken many lives, it is not unusual to automate such approaches to avoid such accidents and save countless lives. This study aims at enhancing the traditional railway cart system to address these issues by introducing an automatic railway track fault detection system using acoustic analysis. In this regard, this study makes two important contributions: data collection on Pakistan railway tracks using acoustic signals and the application of various classification techniques to the collected data. Initially, three types of tracks are considered, including normal track, wheel burnt and superelevation, due to their common occurrence. Several well-known machine learning algorithms are applied such as support vector machines, logistic regression, random forest and decision tree classifier, in addition to deep learning models like multilayer perceptron and convolutional neural networks. Results suggest that acoustic data can help determine the track faults successfully. Results indicate that the best results are obtained by RF and DT with an accuracy of 97%.

中文翻译：

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使用声学分析进行铁路轨道故障检测的新方法

定期检查铁路轨道健康状况对于维持安全可靠的列车运行至关重要。裂缝、道碴问题、轨道不连续、螺母和螺栓松动、车轮烧焦、超高以及由于非维护、先发制人的调查和延迟检测而在轨道上产生的错位等因素对轨道构成了严重的危险和威胁。铁路运输安全运行。使用铁路小车手动检查铁轨的传统程序既低效又容易出现人为错误和偏差。在像巴基斯坦这样的火车事故已夺去许多人的生命的国家，将此类方法自动化以避免此类事故并挽救无数生命的情况并不少见。本研究旨在通过引入使用声学分析的自动铁路轨道故障检测系统来增强传统的铁路车辆系统以解决这些问题。在这方面，本研究做出了两个重要贡献：使用声学信号收集巴基斯坦铁路轨道的数据以及对收集的数据应用各种分类技术。最初，考虑了三种类型的轨道，包括正常轨道、车轮烧毁和超高，因为它们经常发生。除了多层感知器和卷积神经网络等深度学习模型外，还应用了几种著名的机器学习算法，如支持向量机、逻辑回归、随机森林和决策树分类器。结果表明，声学数据可以帮助成功确定轨道故障。结果表明，最佳结果是通过 RF 和 DT 获得的，准确率为 97%。