Full-Cycle Failure Analysis Using Conventional Time Series Analysis and Machine Learning Techniques

The paper studies time series of dynamical systems for failures, applying data-driven machine learning techniques, such as clustering and tipping point analysis. Artificial data with known properties and real systems case studies are considered, with diverse patterns of time series. Applicability of various techniques is discussed. The proposed methodology may be useful in industrial and geophysical applications, where sensor records are available for data-driven failure analysis.