  As Artificial Intelligent (AI) technology advances and increasingly large
amounts of data become readily available via various Industrial Internet of
Things (IIoT) projects, we evaluate the state of the art of predictive
maintenance approaches and propose our innovative framework to improve the
current practice. The paper first reviews the evolution of reliability
modelling technology in the past 90 years and discusses major technologies
developed in industry and academia. We then introduce the next generation
maintenance framework - Intelligent Maintenance, and discuss its key
components. This AI and IIoT based Intelligent Maintenance framework is
composed of (1) latest machine learning algorithms including probabilistic
reliability modelling with deep learning, (2) real-time data collection,
transfer, and storage through wireless smart sensors, (3) Big Data
technologies, (4) continuously integration and deployment of machine learning
models, (5) mobile device and AR/VR applications for fast and better
decision-making in the field. Particularly, we proposed a novel probabilistic
deep learning reliability modelling approach and demonstrate it in the Turbofan
Engine Degradation Dataset.
