关键词: 高阶循环统计量, 峭度, 时变AR模型, 系统辨识, 早期预报

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