Abstract

In our daily life, when we are sick, we usually go to the hospital and ask experienced doctors for diagnosis and treatment. The long-term operation of machines will also produce "disease", that is the so-called fault. We need to seek the help of "doctor" of the machine to diagnose the occurrence of the fault and predict its development trend, and then provide guidance for its operation and maintenance. The new generation of artificial intelligence technology represented by deep learning provides a new way of intelligent diagnosis for machine doctors. On the basis of introducing the development history of artificial neural network, this talk introduces the concept and characteristics of deep learning, and then discusses several typical deep network models and their application in intelligent diagnosis of machines, as well as the development trend of deep learning in the future.

日常生活中我们生病了，通常会去医院请有经验的医生诊断,找出病源后对症治疗。机器设备长期运行也会生“病”，即产生所谓的故障，因而需要寻求机器“医生”的帮助诊断出故障的发生以及发展趋势，为其运维提供指导。以深度学习为代表的新一代人工智能技术为机器 “医生”提供了一种智能诊断新途径。本报告在介绍人工神经网络发展历史的基础上，引出深度学习的概念及特征，随后介绍几种典型的深度网络模型及其在机器设备智能诊断中的应用，以及深度学习在今后的发展趋势。

Speaker Bio

Ruqiang Yan, PhD, Professor/Doctoral supervisor. He is Executive Director of International Machinery Center, iHarbour Academy of Frontier Equipment. Dr. Yan received his Ph.D. degree in Mechanical Engineering from the University of Massachusetts Amherst, USA, in 2007. He is a Fellow of American Society of Mechanical Engineers (ASME), and received the Millions of Leading Engineering Talents Award and the IEEE Instrumentation and Measurement Society Technical Award in 2019. His research interests include instrumentation design, data analytics, machine learning, and energy-efficient sensing and sensor networks for the condition monitoring and fault diagnosis of complex engineering systems.

严如强，博士，教授/博士生导师,高端装备研究院国际机械中心执行主任，2007年毕业于美国马萨诸塞大学阿默斯特分校机械与工业工程系，获机械工程专业博士学位。美国机械工程师学会会士, 2019年国家百千万人才工程入选者，IEEE仪器与测量学会科技奖获得者。严教授的研究兴趣包括仪器设计、数据分析，机器学习以及节能型无线传感与传感器网，及其在复杂工程系统状态监测与故障诊断中的应用。