Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing

Abstract

• We solved the fault prediction problem with the combination of CNN and LSTM . • We solved the maintenance decision problem with the deep reinforcement learning. • The enhanced visual guidance of the maintenance process was realized by AR , which could integrate the invisible maintenance information into the machine tools. • The proposed predictive maintenance approach outperforms others in terms of accuracy and effectiveness. In the Industry 4.0 era, the number and complexity of machine tools are both increased, which is prone to cause malfunctions and downtime in the manufacturing process. Predictive Maintenance (PdM), as a pivotal part of Prognostics and Health Management (PHM), plays a vital role in enhancing the reliability of machine tools in the Internet of Things (IoT)-enabled manufacturing. In order to realize a highly reliable maintenance plan integrated with the fault prediction, the maintenance decision-making, and the Augmented Reality (AR)-enabled auxiliary maintenance, an intelligent predictive maintenance approach for machine tools is proposed in this paper via multiple services cooperating within a single framework. The fault prediction service is supported by the combination of Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM). Specified features from massive production data acquired by IoT can be comprehensively extracted using CNN, and their nonlinear relationship can be fitted by LSTM. Based on the fault prediction result, deep reinforcement learning is adopted to achieve the production control and schedule maintenance personnel if a fault code appears. On top of this, the guidance information from the maintenance experience database can be integrated into the faulty machine tools in the form of visibility through AR, which can guide the maintenance personnel to complete maintenance tasks more efficiently. Moreover, the remote expert service is also integrated in the AR-supported auxiliary maintenance, which is activated to solve unexpected faults that are not stored in the maintenance experience database. Comparative experiments are conducted in the IoT-enabled manufacturing workshop with real-world case studies, and the results demonstrate that the proposed predictive maintenance approach is both effective and practical.