摘要
在工程应用中识别轴承故障已经不能满足设备维护策略制定的需求,利用振动信号对轴承的劣化趋势进行预测,对轴承全寿命周期各阶段的峰值指标、歪度指标、峭度指标、均方根等时域指标变化趋势进行分析,将峰值指标作为反映带式输送机轴承健康状态的退化特征量,形成退化特征序列。利用退化特征量建立灰色预测模型,预测轴承劣化趋势的变化。通过工程应用进行验证,结果表明,灰色模型可以有效地预测轴承劣化趋势,具有工程实用价值。
Identifying bearing failures in engineering applications can no longer meet the needs of equipment maintenance strategy formulation, in order to predict the deterioration trend of bearings by means of vibration signals, the variation trend of peak value index, skewness index, kurtosis index and root mean square index in different stages of the life cycle of bearings have been analyzed. The peak value index has been used as the deteriorating characteristic quantity to describe the bearing health status of belt conveyor, and the deteriorating characteristic sequence has been formed. The grey prediction model has been established by using the degradation characteristic quantity to predict the change of bearing deterioration trend, which has been verified by engineering application. The results show that the grey model can effectively predict the deterioration trend of bearings and has engineering practical value.
作者
马海龙
MA Hailong(Beijing Tiandi Longyue Science and Technology Co., Ltd., Beijing 100043, China)
出处
《煤矿机电》
2019年第4期51-54,共4页
Colliery Mechanical & Electrical Technology
基金
中国煤炭科工集团有限公司科技创新创业资金专项(2018QN035,2018QN037)
关键词
带式输送机
轴承寿命
灰色模型
劣化趋势预测
belt conveyor
bearing life
grey model
prediction of deterioration trend
