基于支持向量机的滚动轴承状态寿命评估

应用状态寿命描述滚动轴承的使用寿命,并建立了滚动轴承的状态寿命评估模型.状态寿命评估模型建模的关键是振动信号的特征提取和状态的识别算法.针对滚动轴承振动的特点,提取小波包重构信号的频带能量构造特征向量,利用支持向量机作为辨识算法建立滚动轴承状态寿命评估模型.滚动轴承全寿命试验验证了模型的有效性和可信性.

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

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.