人工知能学会第二種研究会資料
Online ISSN : 2436-5556
周期波形の僅かな変化を検知可能な1クラス Shapelets 学習法
山本 昌治植野 研山口 晃広
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研究報告書・技術報告書 フリー

2024 年 2024 巻 SMSHM-001 号 p. 13-17

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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.

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