机构地区: 青岛理工大学,山东青岛266520
出 处: 《煤矿机械》 2017年第8期18-20,共3页
摘 要: 在分析了滚动轴承退化趋势预测是实现滚动轴承预防维护关键的基础上,提出了基于时间编码信号处理与识别(TESPAR)和最小二乘支持向量机(LS-SVM)相结合的滚动轴承性能退化趋势预测新方法。将通过TESPAR提取的性能退化特征集输入到LS-SVM中完成了滚动轴承的性能退化趋势预测,最后通过实例验证了所提出方法的有效性。 The prediction of the degradation trend of rolling bearing maintenance of rolling bearing, a new method of rolling bearing degradatio time-coded signal processing and recognition (TESPAR) is the key to realize the n trend prediction based on and least squares support vector machine(LS- are predicted by inputting the performance degradation feature set extracted by TESPAR into LS-SVM. Finally, the effectiveness of the proposed method is verified by an example.