科研之友
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摘要
With the prevalent development and use of predictive maintenance models for Internet-of-Things scenarios, the deep learning technology is gaining momentum. Feature extraction helps to increase efficiency in training the deep-learning-based predictive maintenance model. However, there are common situations of time-lagged correlations among industrial sensor data, resulting in reduction the effect of feature extraction. In this paper, we propose a feature extraction method for multisensors data with time-lagged correlation. A curve-registration method of correlation maximization algorithm is used to solve the problem of time-lagged correlation for multi sensors. Then we apply a recurrent neural network, namely, long short-term memory to develop a lightweight predictive maintenance model with the help of proposed feature extraction method. The effectiveness of the proposed feature extraction approach is demonstrated by examining real cases in a power plant. The experimental results indicate that our method can (1) effectively improve the accuracy of prediction and (2) improve the performance of the prediction model.
