Fault prognosis based on Hidden Markov Models

A. Soualhi, G. Clerc, H. Razik
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引用次数: 4

Abstract

Monitoring electrical motors in critical or sensitive environments is one of the major challenges of our era. This can be applied both in electric vehicles, hybrid and avionics. Therefore the development of tools, which are able to ensure continuity of service by identifying and predicting faults, is crucial to provide a reliable monitoring. This paper presents two methods based on Hidden Markov Models for the prediction of impending faults. They are based on pattern recognition, that is a data-driven approach widely used in the field of faults detection and diagnostic. This paper aims to show that methods such as Hidden Markov Models, commonly used in the diagnosis, can also be used in the field of prognosis. The first method, based on the recognition of degradation processes, allows predicting the imminent appearance of the fault and the second is based on modeling the state of degradation of the studied system. An example of application is given to demonstrate their applicability. The results show their effectiveness to predict the imminent appearance of a fault.
基于隐马尔可夫模型的故障预测
监控关键或敏感环境中的电动机是我们这个时代的主要挑战之一。这可以应用于电动汽车、混合动力汽车和航空电子设备。因此,开发能够通过识别和预测故障来确保服务连续性的工具对于提供可靠的监测至关重要。本文提出了两种基于隐马尔可夫模型的即将发生故障预测方法。它们是基于模式识别的,模式识别是一种数据驱动的方法,广泛应用于故障检测和诊断领域。本文旨在表明,在诊断中常用的隐马尔可夫模型等方法也可用于预后领域。第一种方法基于对退化过程的识别,可以预测即将出现的故障;第二种方法基于对所研究系统的退化状态进行建模。最后给出了应用实例,说明了该方法的适用性。结果表明,该方法在预测断层即将出现方面是有效的。
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