A Hybrid Deep Learning Framework for Intelligent Predictive Maintenance of Cyber-physical Systems

Abstract

The proliferation of cyber-physical systems (CPSs) and the advancement of the Internet of Things (IoT) technologies have led to explosive digitization of the industrial sector. It offers promising perspectives for high reliability, availability, maintainability, and safety production process, but also makes the systems more complex and challenging for health assessment. To deal with these challenges, one needs to develop a robust approach to monitor and assess the system’s health state. In this article, a practical and effective hybrid deep learning multi-task framework integrating the advantages of convolutional neural network (CNN) and long short-term memory (LSTM) neural network to reflect the relatedness of remaining useful life prediction with health status detection process for complex multi-object systems in CPS environment is developed. The CNN is used as a feature extractor to compress condition monitoring data and directly extract significant spatiotemporal features from raw multi-sensory input data. The LSTM is used to capture long-term temporary dependency features. The advantages of the proposed hybrid deep learning framework have been verified on the popular NASA’s C-MAPSS dataset. The experimental study compares this approach to the existing methods using the same dataset. The results suggest that the proposed hybrid CNN-LSTM model is superior to existing methods, including traditional machine learning and deep learning-based methods. The proposed framework can provide strong support for the health management and maintenance strategy development of complex multi-object systems.