Predicting Remaining Useful Life of Equipment based on Deep Learning-based Model

• Main Authors: Kuang-Chieh Huang, 黃冠傑
• Other Authors: Chia-Yu Hsu
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/2bd366

碩士 === 元智大學 === 資訊管理學系 === 106 === With the development of smart manufacturing, in order to instantly detect abnormal conditions of the equipment, a large number of sensors were built to record the variables associated with the collection of production equipment. This research focuses on the remaining useful life(RUL)prediction. Remaining useful life is a part of the predictive maintenance(PDM), it is condition based, according to the development trend of the machine in the past to detect the machine is going to malfunction, our purpose is to detect early that the machine needs to be replaced or repaired to ensure the sustainability of the system. Existing literature methods are often difficult to extract meaningful features from sensing data. This research proposes a deep learning method, constructing an autoencoder gated recurrent unit (AE-GRU) neural network model, autoencoder extracts the important features from the raw data and gated recurrent unit picks up the information of the sequences to forecasting remaining useful life precisely. In the experiment of this research, we use for the prognostics challenge competition at the IEEE International Conference on Prognostics and Health Management (PHM08) and evaluated by 5 folds cross-validation. In the verification of root mean square error(RMSE) in our experiments, our method is better than other methods, such as deep neural network(DNN)、recurrent neural network(RNN)、long short-term memory neural network(LSTM)、gated recurrent unit neural network(GRU).