Intelligent monitoring and diagnostics using a novel integrated model based on deep learning and multi-sensor feature fusion

Monitoring and diagnostics are vitally important in smart manufacturing systems since early detection can reduce downtime, protect environment, improve work efficiency, and save cost. The current work for monitoring and diagnostics mainly process the system condition data from multisensor by some mainstream machine learning and deep learning methods. However, these methods' performance is limited by the following weaknesses: (1) The multi-sensor information are not well used, and its feature fusion is not considerd. (2) Current advanced methods, such as convolution neural network (CNN), long short-term memory neural network (LSTM), are still facing some problems due to their inherent structures. CNN does not consider the sequential and temporal dependency; LSTM does not consider spatial correlation. Thus, a novel integrated model based on deep learning and multi-sensor feature fusion is proposed. The developed parallel convolutional neural network (PCNN) in the integrated model can achieve multisensory feature fusion to overcome the first weakness. The integrated CNN, deep residual networks (DRN), LSTM, can solve the second point. Specifically, the signals collected from multiple sensors are turned into multi-channel images, and the PCNN is designed to extract and fused the features of the converted images. Then, DRN and LSTM are developed to accept the extracted high-dimensional features by the designed CNN and generate the prediction results by fully connected neural networks. Two experiments, including cutting tool monitoring and bearing fault diagnosis, are conducted to validate the superiority and robustness of the proposed method. Compared with the state-of-the-art algorithms, the results show that the proposed model is more robust and accurate. (C) 2020 Elsevier Ltd. All rights reserved.