Data-Driven Modeling for Analysis, Fault Detection, Optimization and Control of Dynamic Systems

author: time:2019-04-15 clicks:

Time and place: 2019.4.16, 14:30 pm, Wuhan National High Magnetic Field Center B206

Presenter: Jian-Qiao Sun

Title: Data-Driven Modeling for Analysis, Fault Detection, Optimization and Control of Dynamic Systems

Abstract:

In this talk, we review efforts of the past few decades in developing mathematical models for analysis, fault detection, optimization and control of various civil, mechanical and biological systems. Examples with “small” number of data are first discussed. These include the data driven modeling of acoustic materials, fatigue life prediction of metallic structures, modeling, fault detection and control of HVAC systems in office buildings, and surgical outcome prediction. The methods for data-driven research are discussed including statistical analysis, principal component analysis, correlation analysis and neural networks. We then discuss the implications of the availability of “big” data. We then review the recent advances of methods in artificial intelligence, and discuss their potentials and challenges for applications to civil and mechanical systems operating in complex environment. An example of our preliminary study of fault detection of rotor dynamic systems with deep learning is then presented to conclude the talk. It is hoped that this talk will stimulate the interests in artificial intelligence and its application to traditional engineering disciplines.

Address: Luoyu road 1037, Wuhan 430074, China
Tel: 86 27 87792334
Email: phmff@mail.hust.edu.cn
Copyright ©2017 WHMFC, HUST