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首页> 外文期刊>Journal of intelligent & fuzzy systems: Applications in Engineering and Technology >Intelligent condition monitoring of railway signaling in train detection subsystems
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Intelligent condition monitoring of railway signaling in train detection subsystems

机译：列车检测子系统中铁路信号的智能状态监测

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摘要

Condition monitoring is a prevalent method to improve the Reliability, Accessibility, Maintainability and Safety level of a system. By using this method, detection and diagnostics of imminence or persistence faults, will be viable. The ability of detecting imminent faults, it will be possible to prevent any breakdowns via preventive maintenance, which leads to improve Reliability and Accessibility and functional lifetime expansion of the system. In recent years, monitoring systems have gained much consideration for boosting the quality of equipment operations in railway systems. In this paper, we employed Neuro-Fuzzy Network (NFN) for fault detection and diagnostics in a typical audio frequency track circuit, which is a combination of knowledge-based and data-based systems. It has the merits of both fuzzy systems and neural networks. In other words, NFN is capable of interacting with low precision data and has the learning ability of a neural network. For the realization of the method; a typical track circuit has been modeled and simulated. Healthy and faulty data have been used for training the algorithm. When put to work, occurrence of fault modes or their imminence is detected and localized with a good precision by the algorithm.
机译：状态监视是一种提高系统可靠性，可访问性，可维护性和安全性水平的流行方法。通过使用这种方法，对即将发生或持续存在的故障进行检测和诊断将是可行的。具有检测到即将发生的故障的能力，可以通过预防性维护来防止任何故障，从而提高可靠性和可访问性，并延长系统的使用寿命。近年来，监视系统在提高铁路系统中设备操作的质量方面已引起广泛关注。在本文中，我们采用神经模糊网络（NFN）在典型的音频跟踪电路中进行故障检测和诊断，该电路是基于知识的系统和基于数据的系统的组合。它具有模糊系统和神经网络的优点。换句话说，NFN能够与低精度数据进行交互，并具有神经网络的学习能力。用于该方法的实现;典型的跟踪电路已经过建模和仿真。健康和错误的数据已用于训练算法。当投入工作时，通过算法可以很好地检测并定位故障模式或其迫在眉睫的位置。

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