AR模型（AR model）.doc

AR模型（AR model） This article is contributed by dadadali001 Volume 5, phase 2 20cr7 June Journal of nanjing institute of engineering (natural science edition) J is nal 0 f V01.5. No. 2 Nanjillg ln of the ln of the mte 0 f TecllIlolo stare (Natural Sci infinity Edi60n) Jun. 2007 Article no. : 1672 -- 2558 (2007) 02 0045 -- 05 AR model is applied to the study of vibration signal trend prediction Yang hao, huang dongsheng (changsha institute of automobile and mechanical engineering, changsha, hunan, 410076) Abstract: the trend prediction of vibration signal of equipment condition monitoring and fault diagnosis is an important content. This article is about the basic method of random signal modeling and model set criteria, and discusses the optimal order number of bearing vibration signal selection problem. Validated with field measured data, the calculation results show that using Burg algorithm of AR model can well fit the bearing vibration signal time series, and has a certain accuracy, can meet the prediction requirements. Keywords: random signal; AR model; Burg algorithm; In the forecast, classification number: 0329 document identification code: A A Study of AR Model Based on Burg For Trend F0recast of vibration Signals YANG Hao, HUANG Dong - sheng (Dept. Of Automobile and Mechanical Engineering, Changsha Changsha UniVersity of science technology, 410076, China) Abstmct: Trend forec caries t of vibmtion aIs is Ming imponant si seal This paper puts pan Of condition Monitoring the diagnosis of equipmems. a. FBNV training a Basie modeling method F qu random si print a18 a. And introduces Mle that dete book ines the model This is veri - Prepare nking. The 8 uthor also discusses The problem of choosing fied Best mnking value for employed The to Be torn ng by Dat8 obtained FMM actual Nl linudng. Bu sings the name of the the thmetic is on Estimale the models Par SAN. .r11le result Im going to do the same thing with the tlle tlle B magpie Ed Burg thmetic c resistance You si