CAJ | 학술논문

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。
침대당전궤계재선감측계통보경난이실현궤계고장조기예경문제，제출일충지능예경방법。기우재선감측계통대량감측수거통계분석，채용동태적자학습역치산법계산예경역치，병응용l1추세려파기술소제수궤오차획취려파후적추세。응용동태자학습역치체대감측계통중적상규보경역치，비교자학습예경역치여려파후적추세，실현료궤계고장조기예경。공정실례표명，해방법능구대궤계고장실현조기예경，대예방궤계사고적발생유중요적작용。
Because the alarm mode of current online monitoring systems is hard to realize early-warning a mechanical fault,an early warning methodology was proposed here.Based on statistical analysis of mass data in online monitoring systems,advantages of the dynamic self learning threshold algorithm were taken to calculate a warning threshold,and the l1 trend filtering technology was used to gain the filtered trend eliminating random errors.With dynamic self-learning threshold instead of general alarm threshold in monitoring systems,early warning of a fault could be acquired by comparing the self-learning warning threshold and the filtered trend.It was shown from engineering examples that early warning of a mechanical fault can be achieved with the proposed method,it plays an important role in preventing occurrence of mechanical fault.