EARLY FAULT FORECASTING BASED ON ALMOST PERIODIC TIME- VARYING AUTOREGRESSIVE MODEL

Abstract

Abstract: According to the characteristics of vibration signal in rotating machinery, one novel method of early fault forecasting based on almost periodic time varying autoregressive (APTV-AR) model is presented and the algorithm of identifying parameters based on higher order cyclic-statistics (HOCS) is proposed, which has the advantage of suppress additive stationary noise. In the end vibration signals from rotors with early rub-impact are analyzed with the APTV-AR model. At first, model parameters are identified under normal condition and then each kurtosis of residual signal under faulty conditions is calculated. The results demonstrate that the proposed method can detect early faults and forecast unknown faults.

Key words: Early forecasting, HOCS, Kurtosis, System identification, Time-varying AR model

CLC Number:

TP27 TH133

Chen Zhongsheng;Yang Yongmin;Hu Zheng;Shen Guoji. EARLY FAULT FORECASTING BASED ON ALMOST PERIODIC TIME- VARYING AUTOREGRESSIVE MODEL[J]. , 2005, 41(1): 184-188.

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EARLY FAULT FORECASTING BASED ON ALMOST PERIODIC TIME- VARYING AUTOREGRESSIVE MODEL

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