全生命周期健康监测诊断系统研究
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摘要
随着先进的仪器测量、大数据、人工智能等科学技术的发展,基于全生命周期的设备健康监测诊断对现代企业智能制造将起着越来越重要的作用。应力波传感器连续检测设备运动部件间的摩擦、冲击的电子信号,数据采集箱对信号进行处理、能量计算和幅值分析。借助于应力波能量趋势、应力波振幅直方图、应力波频谱等分析工具,系统对设备状态进行实时监测、预测故障的发生和其变化趋势,大幅度提高了设备运行和维修效率。基于人工神经网络的改进算法,对设备故障进行智能化的准确预测和定量分析,并提供设备健康诊断报告。试验表明,系统具有较强的鲁棒性和自适应能力,能更早地预测到故障的发生,更准确地判定故障的部位和类型,降低了维修成本,提高了运行效率,提高了生产安全性。
With the development of advanced instrument measurement,big data,and artificial intelligence,the equipment health monitoring and diagnosis system based on the life cycle is playing the more and more important role in the modern enterprise intelligent manufacturing. The stress wave sensor continuously detects the electronic signals of the friction and mechanical shock among the moving parts of the equipment. Then,the signal processing,energy calculation and amplitude analysis are utilized to mine the acquired data.With the help of analysis tools,the energy trend,the amplitude histogram and the spectrum of the stress wave can be monitored in real time. The occurrence of fault and its change trend can be predicted. In this way,the efficiency of equipment operation and maintenance is enhanced. The improved algorithm based on artificial neural network can predict and analyze the equipment fault intelligently and provide the diagnosis report of equipment health. Experimental results show that the system has strong robustness and self-adaptive ability,predict the occurrence of fault earlier,and determine the location and type of the fault more accurately. The maintenance cost is reduced. The operation efficiency and the safety of production are improved.
With the development of advanced instrument measurement,big data,and artificial intelligence,the equipment health monitoring and diagnosis system based on the life cycle is playing the more and more important role in the modern enterprise intelligent manufacturing. The stress wave sensor continuously detects the electronic signals of the friction and mechanical shock among the moving parts of the equipment. Then,the signal processing,energy calculation and amplitude analysis are utilized to mine the acquired data.With the help of analysis tools,the energy trend,the amplitude histogram and the spectrum of the stress wave can be monitored in real time. The occurrence of fault and its change trend can be predicted. In this way,the efficiency of equipment operation and maintenance is enhanced. The improved algorithm based on artificial neural network can predict and analyze the equipment fault intelligently and provide the diagnosis report of equipment health. Experimental results show that the system has strong robustness and self-adaptive ability,predict the occurrence of fault earlier,and determine the location and type of the fault more accurately. The maintenance cost is reduced. The operation efficiency and the safety of production are improved.
引文
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[13]王燕.基于S变换和模糊KNN的暂态电能质量扰动分类[J].科技资讯,2015(7):4-5.WANG Y.Classification of transient power quality disturbances based on S transform and fuzzy KNN[J].Science and Technology Information,2015(7):4-5.
[14]李志农,朱明,褚福磊,等.基于经验小波变换的机械故障诊断方法研究[J].仪器仪表学报,2014,35(11):2424-2432.LI ZH N,ZHU M,CHU F L,et al.Mechanical fault diagnosis method based on empirical wavelet transform[J].Chinese Journal of Scientific Instrument,2014,35(11):2424-2432.
[15]周永道,王会琦,吕王勇.时间序列分析及应用[M].北京:高等教育出版社,2015.ZHOU Y D,WANG H Q,LU W Y.Time Series Analysis and Its Application[M].Beijing:Higher Education Press,2015
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[2]石慧,曾建潮.基于寿命预测的预防性维护维修策略[J].计算机集成制造系统,2014,20(5):1133-1140.SHI H,ZENG J CH.Preventive maintenance strategy based on life prediction[J].Computer integrated Manufacturing System,2014,20(5):1133-1140.
[3]刘志伟,刘锐,徐劲松,等.复杂系统故障预测与健康管理(PHM)技术研究[J].计算机测量与控制,2010,18(12):2687-2689.LIU ZH W,LIU R,XU J S,et al.Complex system fault prediction and health management(PHM)technology research[J].Computer Measurement and Control,2010,18(12):2687-2689.
[4]WU T SH,CHEN SH Y,TAREEN S,et al.Stress wave analysis based prognostic health management[J].Journal of Chinese Electronics,2018,27(3):565-572.
[5]SCHEEREN S.Aircraft prognostics and health monitoring[R].Stress Wave Analysis Application,2016:23-24.
[6]赵光权,葛强强,刘小勇,等.基于DBN的故障特征提取及诊断方法研究[J].仪器仪表学报,2016,37(9):1946-1953.ZHAO G Q,GE Q Q,LIU X Y,et al.Fault feature extraction and diagnosis method based on deep belief network[J].Chinese Journal of Scientific Instrument,2016,37(9):1946-1953.
[7]洪涛,邱畅啸,黄志奇.基于PCA相似系数与SVM的涡轮泵故障检测算法[J].电子测量与仪器学报,2012,26(6):514-520.HONG T,QIU CH X,HUANG ZH Q.Turbo pump fault detection algorithm based on PCA similarity coefficient and SVM[J].Journal of Electronic Measurement and Instrument,2012,26(6):514-520.
[8]吴天舒,陈蜀宇,吴朋.基于应力波分析的状态监控与故障预测研究[J].仪器仪表学报,2017,38(12):3061-3070.WU T SH,CHEN SH Y,WU P.Condition monitoring and fault prediction based on stress wave analysis[J].Chinese Journal of Scientific Instrument,2017,38(12):3061-3070.
[9]林琦,俞水良.数字扭振测试系统的信号处理及频谱校正[J].电子测量与仪器学报,2012,26(9):824-828.LIN Q,YU SH L.Signal processing and spectrum correction of digital torsional vibration testing system[J].Journal of Electronic Measurement and Instrument,2012,26(9):824-828.
[10]奥本海默.信号与系统[M].西安:西安交通大学出版社,2010.OPPENHEIM A V.Signals and Systems[M].Xi'an:Xi'an Jiaotong University Press,2010.
[11]武国宁,齐晶晶,周亚同.时频分析方法:研究与展望[J].图像与信号处理,2018,7(1):24-35.WU G N,QI J J,ZHOU Y T.Time-frequency analysis method:Research and prospect[J].Journal of Image and Signal Processing,2018,7(1):24-35.
[12]任敏.压缩图像传输提取重构优化仿真研究[J].计算机仿真,2017,34(7):316-319.REN M.Simulation research on optimization of compression image transmission extraction and reconstruction[J].Computer Simulation,2017,34(7):316-319.
[13]王燕.基于S变换和模糊KNN的暂态电能质量扰动分类[J].科技资讯,2015(7):4-5.WANG Y.Classification of transient power quality disturbances based on S transform and fuzzy KNN[J].Science and Technology Information,2015(7):4-5.
[14]李志农,朱明,褚福磊,等.基于经验小波变换的机械故障诊断方法研究[J].仪器仪表学报,2014,35(11):2424-2432.LI ZH N,ZHU M,CHU F L,et al.Mechanical fault diagnosis method based on empirical wavelet transform[J].Chinese Journal of Scientific Instrument,2014,35(11):2424-2432.
[15]周永道,王会琦,吕王勇.时间序列分析及应用[M].北京:高等教育出版社,2015.ZHOU Y D,WANG H Q,LU W Y.Time Series Analysis and Its Application[M].Beijing:Higher Education Press,2015
[16]LAZZUS J A.Neural network-particle swarm modeling to predict thermal properties[J].Mathematical and Computer Modeling,2013,57(9-10):2408-2418.