论文标题
通过AI和IIOT从预测维护到智能维护
Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT
论文作者
论文摘要
随着人工智能（AI）技术的进步和越来越多的数据通过各种工业互联网（IIOT）项目很容易获得，我们评估了预测性维护方法的艺术状态，并提出了我们的创新框架以改善当前的实践。本文首先回顾了过去90年来可靠性建模技术的演变，并讨论了在行业和学术界开发的主要技术。然后，我们介绍下一代维护框架 - 智能维护，并讨论其关键组件。该基于AI和IIOT的智能维护框架由（1）最新的机器学习算法组成，包括概率可靠性建模，通过深度学习，（2）通过无线智能传感器进行实时数据收集，传输和存储，（3）大数据技术，（3）（4）连续集成和机器学习模型，（5）机器学习模型，（5）移动设备和AR/VR设备的持续集成和部署。特别是，我们提出了一种新型的概率深度学习可靠性建模方法，并在Turbofan Engine降解数据集中证明了它。
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.
