Features that enable predictive maintenance and anomaly detection.

Introducing examples of effective features for detecting anomalies from data for practical applications of AI.

The initiative of predictive maintenance, which involves predicting the timing of equipment failures and implementing maintenance, is gaining attention. From the perspective of complementing the intuition, skills, and experience of engineers with AI, it is expected that the demand for predictive maintenance will further increase in the future. This white paper introduces examples of effective features for detecting anomalies from time series data. Additionally, it includes discussions on the limitations of machine learning and features from the perspective of system integration. [Contents (excerpt)] ■ Limitations of machine learning ■ Examples of features that enable failure prediction ■ Features from the perspective of system integration ■ Summary *For more details, please refer to the PDF document or feel free to contact us.

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