A Hypothesis testing approach to Zero-Fault-Shot learning for Damage Component Classification
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Abstract
Often, in condition monitoring, datasets are asymmetric. That is, for most machines being monitored, there is no labeled fault data, only nominal data (hence, the dataset is asymmetric). Deep Learning and other neural network-based mechanization have difficulty solving this type of problem, as they typically require a full set of labeled data, both nominal and faulted. Zero-Fault Shot learning is a class of machine learning problems with no labeled fault training data. In this class of problems, only nominal data is used for knowledge transfer. In this paper, a mixed hypothesis testing and Bayes classifier it used to provide both inferences to the type of fault and also provide information as to when maintenance should be provided. This is done without any fault data and demonstrates knowledge transfer from a set of nominal components, greatly reducing the cost of implementation and fielding of a system.
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Zero Shot Learning, Deep Learning, Predictive Maintenance
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