One-Class Learning time-series Shapelets for Detecting SlightChanges in Periodic Waveforms

Abstract

To automate the maintenance of equipment that requires high-frequency data for diagnostics, such as bearings and motors, a high-performance and interpretable anomaly prediction method is essential. However, detecting slight changes in waveforms, which indicate early signs of anomalies, is challenging due to noise interference. This paper proposes a method that combines the Shapelets learning technique, known for its clear decision evidence, with a band-pass filter. This combination helps detect slight waveform changes, capturing early signs of anomalies. An experiment show that this method can learn from a small amount of data and automatically identify frequency bands associated with anomalies.