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.
Highlights
Equipment monitoring, detection, and management have played important roles in modern militaries
It should be emphasized that the optimal results obtained by the four indicators may not be completely consistent
(2) The most significant improvement over classical methods is the construction of the degradation feature, which is termed the sparse representation (SR) feature
Summary
Detection, and management have played important roles in modern militaries. Liu et al [11] improved the HSMM by using a sequential Monte Carlo method to describe the probability relationships between degradation states and observations. They developed a new online health prognostic method for RUL estimation. They proposed an adaptive hidden semi-Markov model (AHSMM) [12] for multisensor equipment diagnosis and prognosis. A new feature based on the wavelet packet transform (WPT) [23] and sparse representation (SR) [24], [25] is proposed in this paper to observe degradation. The process of three algorithms can be found in [32] for more details
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