基于生命周期的智能家居故障预测诊断算法
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摘要
随着我国人民生活水平的提高,智能家居逐渐走进寻常百姓家,因此,智能家居系统的研究日益成为各高校、研究所和企业研究的热点,但是却很少有关于智能家居系统故障预测诊断方面的研究。
本文以智能家居的故障诊断为研究背景,在基于MAS智能家居能量系统MAES(Multi-Agent-based Home Energy System)研究的基础上,对智能家居系统的故障特征进行分析,得出智能家居系统故障相互关联、“黑色”参数与“白色”参数共存等特点。针对故障相互关联的特点,本文以历史故障数据库为样本,采用概率统计的方法,提出了基于生命周期的智能家居故障检测。针对智能家居系统作为灰色系统的特点,本文以灰色预测理论作为理论基础,采用灰色预测模型,通过智能家居故障特征参数的实时监测,实现了智能家居故障特征参数的预测,并以基于生命周期的智能家居故障检测算法获得的检测阈值为判断依据,实现了故障的提前预测。针对灰色关联分析应用于智能家居故障诊断的优势和不足,本文以灰色关联理论为基础,提出了以故障率作为优化因数的智能家居故障优化灰色关联度诊断算法,该优化因数的获得是以威布尔分布函数作为拟合曲线,采用一元线性回归和最小二乘法,获得家居设备或零部件全生命周期的故障率函数而得到的。最后的例子验证了该算法的有效性。
With the improvement of living standards of our people, Smart Home gradually moves into the ordinary family. Therefore, the study on Smart Home system has increasingly became the focus of colleges, universities, research institutes and enterprises. But the study on the fault forecast and diagnosis of Smart Home system are very few.
The paper chooses the Smart Home’s fault diagnosis as research background, on the basis of MAS-based Smart-Home energy system MAES (Multi-Agent-based Home Energy System), analyzes the Smart Home’s fault characteristics, testifies the features of Smart Home system that faults are interrelated and "black" parameters and the "white" parameters are coexistence. In view of the characteristics of faults are interrelated, the paper chooses historical faults database as swatches, the statistical probability method as method, proposes Smart Home fault detection based on the lifecycle. In view of the characteristics that the Smart Home system is a gray system, the paper chooses Grey Prediction Theory as a theoretical foundation, using a GM model, based on the real-time monitoring data of the Smart Home feature fault parameters, achieves the fault characteristic parameters’forecast, and chooses the threshold values of the Smart Home fault detection algorithm based on the lifecycle as a diagnosis judgment, achieves the fault forecast. In view of the strengths and weaknesses of Grey correlation analysis for the fault diagnosis of the Smart home, the paper also chooses Grey Relation Theory as foundation, put forwards The Smart Home optimization grey correlation fault diagnosis algorithm based on fault rate, to get the optimal factor, the paper chooses Weibull distribution function as a fitting curves, using 1st Linear Regression and Least-squares method, gets the home equipment’s or parts’full lifecycle fault rate function. The final example verifies the effectiveness of the algorithm. Keyword:Smart home,Lifecycle,fault forecast diagnosis,Grey System Theory
本文以智能家居的故障诊断为研究背景,在基于MAS智能家居能量系统MAES(Multi-Agent-based Home Energy System)研究的基础上,对智能家居系统的故障特征进行分析,得出智能家居系统故障相互关联、“黑色”参数与“白色”参数共存等特点。针对故障相互关联的特点,本文以历史故障数据库为样本,采用概率统计的方法,提出了基于生命周期的智能家居故障检测。针对智能家居系统作为灰色系统的特点,本文以灰色预测理论作为理论基础,采用灰色预测模型,通过智能家居故障特征参数的实时监测,实现了智能家居故障特征参数的预测,并以基于生命周期的智能家居故障检测算法获得的检测阈值为判断依据,实现了故障的提前预测。针对灰色关联分析应用于智能家居故障诊断的优势和不足,本文以灰色关联理论为基础,提出了以故障率作为优化因数的智能家居故障优化灰色关联度诊断算法,该优化因数的获得是以威布尔分布函数作为拟合曲线,采用一元线性回归和最小二乘法,获得家居设备或零部件全生命周期的故障率函数而得到的。最后的例子验证了该算法的有效性。
With the improvement of living standards of our people, Smart Home gradually moves into the ordinary family. Therefore, the study on Smart Home system has increasingly became the focus of colleges, universities, research institutes and enterprises. But the study on the fault forecast and diagnosis of Smart Home system are very few.
The paper chooses the Smart Home’s fault diagnosis as research background, on the basis of MAS-based Smart-Home energy system MAES (Multi-Agent-based Home Energy System), analyzes the Smart Home’s fault characteristics, testifies the features of Smart Home system that faults are interrelated and "black" parameters and the "white" parameters are coexistence. In view of the characteristics of faults are interrelated, the paper chooses historical faults database as swatches, the statistical probability method as method, proposes Smart Home fault detection based on the lifecycle. In view of the characteristics that the Smart Home system is a gray system, the paper chooses Grey Prediction Theory as a theoretical foundation, using a GM model, based on the real-time monitoring data of the Smart Home feature fault parameters, achieves the fault characteristic parameters’forecast, and chooses the threshold values of the Smart Home fault detection algorithm based on the lifecycle as a diagnosis judgment, achieves the fault forecast. In view of the strengths and weaknesses of Grey correlation analysis for the fault diagnosis of the Smart home, the paper also chooses Grey Relation Theory as foundation, put forwards The Smart Home optimization grey correlation fault diagnosis algorithm based on fault rate, to get the optimal factor, the paper chooses Weibull distribution function as a fitting curves, using 1st Linear Regression and Least-squares method, gets the home equipment’s or parts’full lifecycle fault rate function. The final example verifies the effectiveness of the algorithm. Keyword:Smart home,Lifecycle,fault forecast diagnosis,Grey System Theory
引文
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[5]吴凡.状态监测和故障诊断技术的现状与展望[J].国外电子测量技术, 2006, 25 (3) .
[6]邱立鹏.设备剩余寿命的预测与分析[D]: (硕士学位论文).大连:大连理工大学. 2000.
[7]胡文君,褚家荣,苏毅.设备故障诊断技术的现状与发展[J].后勤工程学院学报,2004.
[8]章卫国,杨向忠.制冷系统故障预测与诊断方法研究[D]: (硕士学位论文).西安:西安科技大学. 2005.
[9]李伟.复杂系统的智能故障诊断技术现状及其发展趋势[J].计算机仿真, 2004, 21(10).
[10]陈炜峰,陆静霞.故障诊断技术及其发展趋势[J].农机化研究, 2005.
[11]郭世照.大型变压器运行寿命预测研究[D]. (硕士学位论文).山东:山东大学, 2004.
[12]高洁,谢敬东.基于关联函数的变压器故障可拓诊断方法[J].系统工程, 2000(5).
[13]余江海.一种制冷系统故障诊断方法研究[D].上海交通大学硕士学位论文, 2001.
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[18]韩江洪等.智能家居系统与技术[M].合肥:合肥工业大学出版社,2005.
[19]刘苗苗.基于MAS的家居能量系统的Agent实现及其智能管理[D]. (硕士学位论文).重庆:重庆大学, 2007.
[20]吴今培.智能故障诊断技术的发展和展望[J].振动、测试与诊断, 1999(19).
[21]王学萌等.灰色系统预测决策建模程序集[J].科学普及出版社. 1986.
[22]宋丹娜,柴立元,何德文.生命周期评价模型综述[J].工业安全与环保, 2006(32).
[23]杨家红,肖松文等.产品生命周期评价系统研究[J].计算机工程与科学, 2004.
[24]吴杰等.中国人群心电图时间参数的年龄和性别差异[J].中华心血管病杂志, 2001年10月第29卷第10期.
[25]赵修义等,年龄与正常脑白质平均表观扩散系数的相关性[J].第一军医大学学报, 2004; 24(11).
[26]樊新海,苗卿敏,王华民.灰色预测GM(1,1)模型及其改进与应用[J].装甲兵工程学院学报, 2003.
[27]罗党,刘思峰,党耀国.灰色模型GM(1,1)优化[J].中国工程科学, 2003.
[28]徐涛,冷淑霞.灰色系统模型背景之探讨与改进[J].淄博学院学报(自然科学与工程版), 2002.
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[31]马秀红,宋建社.基于回归分析的备件故障率预测模型[J].计算机仿真, 2003(11).
[32]李英德.最小二乘法与线性回归[J].昌潍师专学报, 1998(5).
[33]沈建新,周儒荣.产品全生命周期管理系统框架及关键技术研究[J].南京航空航天大学学报, 2003(5).
[34]谢慧.空调系统状态监测与故障诊断系统的研究.北京工程学院硕士论文. 2003.
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[36]苏勋家,王汉功.灰色理论在故障预测中的应用[J].中国设备管理, 1991.
[37]吴祖堂,李岳,温熙森.灰关联分析在机械设备故障诊断中的应用[J].系统工程理论与实践, 1999.
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[39]吕锋.灰色系统关联度之分辨系数的研究[J].系统工程理论与实践, 1997.
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