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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: HOCS Time-varying AR model Kurtosis System
identification Early forecasting
CLC No: TP27
TH133
维修工程预先研究资助项目(413270303).
Received 20040426, received in revised form 20041015
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