A New State Recognition and Prognosis Method Based on a Sparse Representation Feature and the Hidden Semi-Markov Model

Abstract

Equipment degradation state recognition and prognosis are considered two significant parts of a prognostics and health management (PHM) system that help to reduce downtime and decrease economic losses. In this paper, a sparse representation (SR) feature is proposed as a new degradation feature, and the hidden semi-Markov model (HSMM) is established. The new method offers three significant advantages over the traditional HSMM. (1) Since the degradation information is incomplete, a Gaussian mixture model (GMM) is used here for degradation data clustering and state division. (2) A new degradation feature based on the wavelet packet transform (WPT) and SR can better extract the structural information of the collected signal and reflect the degradation characteristics. (3) To conduct remaining useful life (RUL) predictions, an improved model is proposed, which adds a control variable that can dynamically adjust the state duration. The effectiveness of the proposed method is demonstrated using 8 groups of bearing data from the Center for Intelligent Maintenance Systems (IMS). The results show that the HSMM with the SR feature achieves the best recognition accuracy, of 85.28%. Moreover, the improved prediction model achieves a prediction accuracy of 86.11% on average for 8 bearings.

Keywords

• Sparse representation
• hidden semi-Markov model
• degradation recognition
• feature extraction
• prognostic
• bearing