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首页> 外文会议>ASME(American Society of Mechanical Engineers) International Design Engineering Technical Conferences and Computers and Information in Engineering Conference 2005(DETC2005) vol.1: Biennial Conference on Mechanical Vibration and Noisen pt.A; 20050924-28; >DEGRADATION ASSESSMENT FOR MACHINERY PROGNOSTICS USING HIDDEN MARKOV MODELS
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DEGRADATION ASSESSMENT FOR MACHINERY PROGNOSTICS USING HIDDEN MARKOV MODELS

机译：使用隐马尔可夫模型对机械预测进行退化评估

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摘要

Degradation detection and recognition of degradation pattern are crucial to the successful deployment of prognostics. A machine degradation process is known to be stochastic instead of deterministic. Recognizing the degradation pattern needs helps from stochastic and probabilistic models. Among various stochastic approaches, Hidden Markov Models (HMMs) have been proven to be very effective in modeling both dynamic and static signals. In this paper, aiming to providing a guideline of how to effectively and efficiently use the HMMs to assess degradation for various machinery prognostic applications, three different approaches of applying the HMMs are reviewed and compared. It demonstrates that depending on the varieties of applications, available prior knowledge, and characteristics of degradation processes, those three implementation approaches perform differently. A full understanding of the strengths and weaknesses of each deployment approach is extremely important in order to effectively utilize this powerful tool for system degradation assessment.
机译：降级检测和降级模式识别对于成功部署预后至关重要。已知机器降级过程是随机的，而不是确定性的。认识到退化模式的需求可以从随机和概率模型中获得帮助。在各种随机方法中，隐马尔可夫模型（HMM）已被证明对动态和静态信号建模非常有效。在本文中，旨在为如何有效有效地使用HMM来评估各种机械预后应用的退化提供指导，对三种不同的HMM应用方法进行了回顾和比较。它表明，根据应用程序的种类，可用的现有知识以及降级过程的特征，这三种实现方法的执行方式有所不同。为了有效地利用这一功能强大的工具进行系统降级评估，充分了解每种部署方法的优缺点非常重要。

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