Solutions for machinery anomaly detection and diagnosis are typically designed on an ad hoc, custom basis, and previous studies have shown limited success in automating or generalizing these solutions. Reusing and maintaining the analysis software, especially when the machine usage pattern or operating condition changes, remains a challenge. This paper outlines a strategy to make use of operational data obtained from the machine’s controller and signals obtained from external sensors to provide an accurate analysis within each operating condition. Operational data collected from the controller is used both for labeling datasets into different operating conditions and for analysis. Principal component analysis (PCA) is adopted to identify critical sensors that can provide useful information. Self-organizing map (SOM)-based anomaly detection and diagnosis methods are used to automatically convert data to easily understandable machine health information for operators. Experiment trials conducted on a feed-axis test-bed demonstrated the effectiveness of incorporating operational data for anomaly detection and diagnosis.

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