Time Series Data for Equipment Reliability Analysis With Deep Learning - Baotong Chen; Yan Liu; Chunhua Zhang; Zhongren Wang (IEEE Access, 2020)
With the deep integration of cyber physical production systems in the era of Industry 4.0, smart workshop dramatically increases the amount of data collected by smart device. A key factor in achieving smart manufacturing is to use data analysis methods for evaluating the equipment reliability and for supporting the predictive maintenance of equipment. Based on these insights, this paper proposes a deep learning-based approach that uses time series data for equipment reliability analysis. First, a framework of the TensorFlow-enabled deep neural networks (DNN) model for equipment reliability analysis is presented. Secondly, using time series equipment data, an evaluation strategy of equipment reliability based on deep learning is proposed. Finally, the reliability of a cylinder, an important part of the small trolley in automobile assembly line, is evaluated in a case study. Compared with the traditional reliability analysis method such as PCA and HMM, the prediction results show a significant improvement in prediction accuracy. This work contributes to promoting artificial intelligence algorithms for realizing highly efficient manufacturing.