Assessment based on support vector machine for rolling bearing grade-life

作者：Hong Jie^*; Han Lei; Miao Xuewen; Ma Yanhong

来源：Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(8): 896-899.

• Assessment models
• Bearing tests
• Energy feature
• Failure data
• Feature vectors
• Grade-life
• Pattern recognition algorithms
• Rolling bearing
• Rolling bearings
• Support vector machine (SVM)
• Vibration signal
• Wavelet package
• Wavelet Packet Analysis
• Wavelet packet transforms

Grade-life was used to describe rolling bearing';s service life, and an assessment model was presented for bearing';s Grade-life. Signal feature extraction and pattern recognition algorithm were keys to construct the model. Vibration signals of the rolling bearing were analyzed, and the wavelet packet analysis theory was adopted to extract the grade-life characteristics. Through signal reconfiguration with wavelet package to extract energy feature of various frequency bands acting as the life feature vector was input into support vector machine (SVM) to realize the mapping between the grade-life vector and the grade-life of rolling bearing, and the model in establishing the identification by using bearing test stand run-to-failure data. The validity and creditability of model has been demonstrated by bearing test stand dates.

• 出版日期2010
• 单位北京大学