Methods to Improve the Prognostics of Time-to-Failure Models
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Abstract
Autonomous and autonomic systems have started to develop machine learning (ML) methods for prognostics and health management (PHM) directly at the platform level. Remaining-useful-life (RUL) estimation, also known as Time-to-failure (TTF) estimation, using streaming sensor data is critical for PHM as it can help to decide and schedule appropriate courses of action (COAs). This work casts the RUL-estimation problem as a classification problem over a finite-time horizon. Rather than using a winner-take-all method to develop a RUL estimator, we propose a top-K estimator that considers the RUL values corresponding to the K-largest probabilities yielded by the classifier to develop our estimator. The top-K RUL values can be used to drive the execution of conservative or aggressive PHM strategies, or be tracked over time to develop robust RUL estimators that leverage the history of RUL estimates. The performance of the proposed RUL estimators is illustrated on a dataset from NASA’s Prognostics Center of Excellence.
How to Cite
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prognostic health management, time-to-failure, machine learning, confidence values, TTF-error-based scores, top-K based estimators
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