Grade-life model of rolling bearing based on support vector machine

Grade-life was used to describe rolling bearing's service life, showing the entire service life is divided into four stages: good bearing condition, initial defect condition, damaged bearing condition and upcoming failure condition; an assessment model was presented for bearing's grade-life. Signal feature extraction and pattern recognition algorithm were crucial 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, the support vector machine was used to realize the mapping between the grade-life vector and the grade-life of rolling bearing, and the model of establishing the identification model by using bearing test stand run-to-failure data as learning samples was employed. The validity and creditability of model has been demonstrated by bearing test stands.