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.

通过评估现有的预测性维护方法并提出创新性框架，本研究介绍一种基于人工智能和工业互联网的智能化维护框架，包括概率可靠性建模和深度学习等最新机器学习算法，并利用涡轮风扇引擎退化数据集验证了新颖的概率深度学习可靠性建模方法。