| 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... |
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