汇报题目：参加IEEE International Conference on Industrial Engineering and Engineering Management (IEEE IEEM) 2017 参会报告

汇报时间：2017年12月19日(星期二) 19:00

汇报地点：科技园西五楼南205会议室

汇报人：邢赛博

会议名称：2017 IEEE International Conference on Industrial Engineering and Engineering Management

会议时间：10-13 December 2017

会议地点：Singapore

会议简介：The IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) is the leading international forum to disseminate, to all branches of industries, information on the most recent and relevant research, theories and practices in IEEM. All submissions are subjected to rigorous review before an acceptance decision is made. This conference has been hosted by leading universities in Asia and has been attended by around 500 participants from 50 countries each time. IEEM conferences link researchers and practitioners from different branches of industrial engineering and engineering management from around the world. Built on the experience of the earlier conferences, IEEM has been a conference of very high standard. IEEM conferences have been held in major destinations such as Bangkok, Hong Kong, Macao and Singapore.

会议交流工作

Oral presentation: Intelligent fault diagnosis of Rotating Machinery using locally connected restricted Boltzmann machine in big data era

报告人：邢赛博

参加论文信息

Title: Intelligent fault diagnosis of Rotating Machinery using locally connected restricted Boltzmann machine in big data era

Author: Saibo Xing, Yaguo Lei, Feng Jia, Jing Lin

Abstract: In intelligent fault diagnosis, unsupervised feature learning is a potential tool to replace the manual feature extraction in big data era. Therefore, we first develop a locally connected restricted Boltzmann machine (LCRBM) from the traditional RBM in order to handle the periodic appearance of fault characteristics in the raw signals of rotating machinery. Then, using LCRBM, we propose a method for intelligent fault diagnosis of rotating machinery. In the method, LCRBM is used to obtain features directly from raw signals. Based on the features learned by LCRBM, the method uses softmax regression to recognize faults. The proposed method is verified by the dataset of locomotive bearings and its superiority is demonstrated by the comparison with methods using the traditional RBM and eighteen widely used manual features. Results indicate that the proposed method is able to automatically learn fine features from raw signals of rotating machinery and achieves higher diagnosis accuracies.