Abstract
This paper presents a novel method for anomaly detection in a helical gear box, where the objective is to predict incipient faults before they become catastrophic. The anomaly detection algorithm relies on symbolic time series analysis and is built upon concepts from automata theory, information theory, and pattern recognition. Early detection of slow time-scale anomalous behavior is achieved by observing time series data at the fast time-scale of machine operation.
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