基于动态自学习阈值和趋势滤波的机械故障智能预警方法

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振动与冲击 ›› 2014, Vol. 33 ›› Issue (24) : 8-14.

论文

基于动态自学习阈值和趋势滤波的机械故障智能预警方法

张明，冯坤，江志农

作者信息 +

A mechanical failure early warning methodology based on dynamic self-learning threshold and trend filtering techniques

ZHANG Ming, FENG Kun, JIANG Zhi-nong

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文章历史 +

摘要

针对当前机械在线监测系统报警难以实现机械故障早期预警问题，提出一种智能预警方法。基于在线监测系统大量监测数据统计分析，采用动态的自学习阈值算法计算预警阈值，并应用ℓ1趋势滤波技术消除随机误差获取滤波后的趋势。应用动态自学习阈值替代监测系统中的常规报警阈值，比较自学习预警阈值与滤波后的趋势，实现了机械故障早期预警。工程实例表明,该方法能够对机械故障实现早期预警，对预防机械事故的发生有重要的作用。

Abstract

Because that the alarm mode of current online monitoring system is hard to warn early mechanical failure, an early warning methodology is proposed in this paper. Based on statistical analysis of mass data in online monitoring system, taking advantage of dynamic self learning threshold algorithm calculates warning threshold, and using ℓ1 trend filtering technology obtains filtered trend which has eliminated random errors. With dynamic self-learning threshold instead of general alarm threshold on monitoring system, early warning can be acquired by comparing the self-learning warning threshold and filtered trend. It can be shown form the project cases that early warning of mechanical failure can be achieved by the proposed method, which plays an important role in preventing occurrence of mechanical failure.

导出引用

张明;冯坤;江志农. 基于动态自学习阈值和趋势滤波的机械故障智能预警方法[J]. 振动与冲击, 2014, 33(24): 8-14

ZHANG Ming;FENG Kun;JIANG Zhi-nong. A mechanical failure early warning methodology based on dynamic self-learning threshold and trend filtering techniques[J]. Journal of Vibration and Shock, 2014, 33(24): 8-14