A semi-supervised GAN method for RUL prediction using failure and suspension histories. (1st April 2022)

• Record Type: Journal Article
• Authors: He, Rui; Tian, Zhigang; Zuo, Ming J.
• Is Part Of: Mechanical systems and signal processing. Volume 168(2022)
• Publication Date: 2022-04-01
• Subjects: Remaining useful life -- Prediction -- Suspension history -- Semi-supervised learning -- Generative adversarial network; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2021.108657
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Deep learning methods have shown great potential to provide reliable remaining useful life (RUL) predictions in Prognostics and Health Management applications. However, deep learning models, particularly supervised learning methods, are strongly dependent on a large number of failure histories. In practice, engineering assets are generally replaced by new ones before failure during planned maintenance, resulting in a small number of failure histories and often times more than twice as many suspension histories. In this paper, a semi-supervised generative adversarial network (GAN) regression model is developed to consider both failure and suspension histories for RUL predictions. The proposed GAN model utilizes conditional multi-task objective functions to capture useful information from suspension histories to improve prediction accuracy, instead of simply treating them as unlabeled data. The method will not directly predict the failure times of suspension histories, but match the statistical information between similar failure and suspension histories to the greatest extent for model training. As a result, the failure information of suspension histories will not only rely on the failure histories but also on the generated data, thereby improving the model generalization, especially when the amount of data is limited. In addition, a robustness evaluation method is proposed to assess the uncertainty of the prognostic model caused by the scarce failure data. The accuracy and credibility of the proposed approach are validated by using two case studies.