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首页> 外文期刊>Mechanical systems and signal processing >Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring
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Adaptive hidden Markov model-based online learning framework for bearing faulty detection and performance degradation monitoring

机译：基于自适应隐马尔可夫模型的轴承故障检测和性能退化监测在线学习框架

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摘要

This study proposes an adaptive-learning-based method for machine faulty detection and health degradation monitoring. The kernel of the proposed method is an "evolving" model that uses an unsupervised online learning scheme, in which an adaptive hidden Markov model (AHMM) is used for online learning the dynamic health changes of machines in their full life. A statistical index is developed for recognizing the new health states in the machines. Those new health states are then described online by adding of new hidden states in AHMM. Furthermore, the health degradations in machines are quantified online by an AHMM-based health index (HI) that measures the similarity between two density distributions that describe the historic and current health states, respectively. When necessary, the proposed method characterizes the distinct operating modes of the machine and can learn online both abrupt as well as gradual health changes. Our method overcomes some drawbacks of the HIs (e.g., relatively low comprehensibility and applicability) based on fixed monitoring models constructed in the offline phase. Results from its application in a bearing life test reveal that the proposed method is effective in online detection and adaptive assessment of machine health degradation. This study provides a useful guide for developing a condition-based maintenance (CBM) system that uses an online learning method without considerable human intervention.
机译：这项研究提出了一种基于自适应学习的方法，用于机器故障检测和健康状况下降监视。所提出方法的核心是使用无监督在线学习方案的“进化”模型，其中自适应隐马尔可夫模型（AHMM）用于在线学习机器在整个生命周期中的动态健康变化。开发了统计索引以识别机器中的新健康状态。然后，通过在AHMM中添加新的隐藏状态来在线描述那些新的健康状态。此外，通过基于AHMM的健康指数（HI）在线量化机器中的健康退化，该指数测量分别描述历史和当前健康状态的两个密度分布之间的相似性。必要时，所提出的方法可以表征机器的不同运行模式，并且可以在线学习突变以及逐渐发生的健康状况变化。我们的方法克服了基于离线阶段构建的固定监控模型的HI的某些缺点（例如，相对较低的可理解性和适用性）。从其在轴承寿命测试中的应用结果表明，该方法可有效地在线检测和自适应评估机器的健康状况。这项研究为开发基于状态的维护（CBM）系统提供了有用的指导，该系统使用在线学习方法而无需人工干预。

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