Data-driven fault classification in industrial applications is complicated by unknown fault classes and limited training data. In addition, different faults can have similar effects on sensor outputs resulting in fault classification ambiguities, i.e., multiple fault hypotheses can explain the data. One solution is to identify and rank all plausible fault classes that give useful information, for example, at a workshop when performing troubleshooting. A probabilistic fault classification algorithm is proposed for residual data classification combining the Weibull-calibrated one-class support vector machines for fault class modeling and Bayesian filtering for time-series analysis. The fault classifier ranks different fault classes and can identify sequences from unknown fault realizations, i.e., faults not represented in training data. Real residual data computed from sensor data and model analysis of an internal combustion engine are used as a case study illustrating the usefulness of the proposed method.

中文翻译：

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贝叶斯滤波的残差数据驱动的开放集故障分类

由于未知的故障类别和有限的培训数据，在工业应用中由数据驱动的故障分类非常复杂。此外，不同的故障可能会对传感器输出产生类似的影响，从而导致故障分类不明确，即多个故障假设可以解释数据。一种解决方案是识别并分类所有可能的故障类别，这些类别可提供有用的信息，例如，在进行故障排除时在车间。提出了一种基于概率分布的概率故障分类算法，该算法结合了经过Weibull校准的一类支持向量机进行故障分类和贝叶斯滤波进行时间序列分析。故障分类器对不同的故障类别进行排序，并可以从未知故障实现中识别序列，即，未在训练数据中表示的故障。