Deep-Learning-Enabled Predictive Maintenance in Industrial Internet of Things: Methods, Applications, and Challenges

Abstract

With the great evolution of human society from the information era to the smart automation era, intelligent production and maintenance have become the core orientation of Industry 4.0. Data-driven intelligent predictive maintenance (IPdM) offers potential solutions to satisfy the urgent demand of predicting impending failures and mitigating unplanned downtime of industrial machinery, and is envisioned as a critical enabler for future industrial Internet of Things. In this article, we provide a comprehensive survey of deep learning (DL)-based IPdM for the researchers and practitioners who focused on the promotion of fault diagnosis/prognosis. First of all, four mainstream algorithms of DL are reviewed around the specific IPdM applications of recent five years. Some prominent discussions and insights on the DL-based IPdM are given here in terms of data characteristics and model performance. Particularly, as an important contribution, a new industrial dataset of dual-bearings rotating machinery is published for opening research to address one of the main challenges for the development of DL-based IPdM, i.e., the shortage of high quality industrial datasets. Compared with current datasets, our dataset is multisensory, which is closer to reality and more suitable for building advanced DL-based IPdM models. We also give a DL-based case study to demonstrate how to build a predictive model for maintenance decisions based on this dataset. Finally, potential research trends and challenges are also discussed for enhancing the intelligence of IPdM in the industrial scenarios.