Enhanced restricted boltzmann machine with prognosibility regularization for prognostics and health assessment

Abstract

Embodiments of the present invention provide an enhanced Restricted Boltzmann Machine (RBM) system with a novel regularization term to generate features automatically that are suitable for predicting remaining useful life (RUL) of engineered systems such as machines, tools, apparatus, or parts. The system improves the trendability of the output features, which may better represent the degradation pattern of such systems. The disclosed system has been demonstrated to improve trendability and RUL prediction accuracy, offering improved predictive power earlier in the life cycle of the machine, tool, or part. During operation, the system implements an RBM including a loss function. The system then extracts a set of features from a degradation measurement via the RBM. The system fits a rate-of-change slope for a respective feature and adds a regularization term to the loss function based on the fitted slope. The system then selects a subset of the enhanced features based on a measure of monotonic trending and aggregates the subset into a health value. The system then predicts a RUL as a weighted average of features best matching a historical degradation pattern in the health value.