Infrared image stream based regressors for contactless machine prognostics. (1st June 2021)

• Record Type: Journal Article
• Authors: Dong, Yifan; Xia, Tangbin; Wang, Dong; Fang, Xiaolei; Xi, Lifeng
• Is Part Of: Mechanical systems and signal processing. Volume 154(2021)
• Publication Date: 2021-06-01
• Subjects: Prognostics and health management -- Remaining useful life prediction -- Neural network -- Multiple weighted time window -- Image stream based regressor; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2020.107592
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A contactless sensing technology is studied to monitor machinery degradation. New neural network regressors are proposed to handle 4D of image stream tensor data. Various input preprocessing means of raw image stream data are investigated. A multiple weighted time window policy is developed to increase the prediction accuracy. The proposed regressor is illustrated to predict RUL based on image stream well. Abstract: In a practical production environment, machinery operators would consider using contactless sensing technology to monitor machinery degradation condition due to the concern of interference on production. A stream of time-series infrared images as a contactless sensing technology could capture the spatial and temporal information of a machinery degradation process. However, due to four dimensions (4D) of image stream tensor data, most existing remaining useful life (RUL) prediction methods are not capable of processing this kind of data. To fill this gap, image stream based regressors consisting of a neural network and multiple weighted time window policy are proposed. In the regressors, neural networks are utilized and trained to model the correlation between degradation image stream and its associated RUL. The networks are respectively designed based on some primary neural network blocks, including a fully-connected layer, long short-term memory network (LSTM) and convolutional neural network (CNN). To achieve the processing capability of 4D data, various input preprocessing means of raw image stream data are investigated. Meanwhile, to increase the prediction accuracy of the trained networks, a multiple weighted time window (MWTW) policy is developed. The policy aims to use whole monitoring data rather than a recent time window in existing neural network based RUL prediction methods. The proposed image stream based regressors are validated by using two datasets of degradation infrared images. Results showed that the regressors can predict RUL based on image stream well and MWTW policy has a significant effect on the increase of the prediction accuracy.