Feature Extraction of Rolling Bearing Fault Diagnosis

摘要：

　　Mechanical equipment fault diagnosis occupies an important position in the industrial production, and feature extraction plays an important role in fault diagnosis.This paper analyzes various methods of feature extraction in rolling bearing fault diagnosis and classifies them into two big categories, which are methods of depending on empirical rules and experimental trials and using objective methods for screening.The former includes five methods: frequency as the characteristic parameters, multi-sensor information fusion method, rough set attribute reduction method, zoom method and vibration signal as the characteristic parameters.The latter includes two methods: sensitivity extraction and data mining methods to select attributes.Currently, selection methods of feature parameters depend heavily on empirical rules and experimental trials, thus extraction results are be subjected to restriction from subjective level, feature extraction in the future will develop toward objective screening direction.

关键词： rolling bearing fault diagnosis feature extraction

作者: Sun Lijie Zhang Li Yang Yongbo Zhang Dabo Wu Lichun

作者单位: School of Information,Liaoning University,Shenyang 110036,China

会议类型: 国际会议

会议名称: the 3nd International Conference on Digital Manufacturing & Automation (第三届数字制造与自动化国际会议(ICDMA 2012))

会议地点: 桂林

会议语种:英文

页码: 993-997

在线出版日期: 2012-08-01（万方平台首次上网日期，不代表论文的发表时间）