An Adaptive Model of Rotating Machinery Subject to Vibration Monitoring

摘要

This paper proposes a state-space model of nonstationary multivariate vibration signals for the on-line estimation of the state of rotating machinery by means of noise-adaptive Kalman filtering algorithm and spectral analysis in the time-frequency domain. Experimental results demonstrate that the proposed model is able to quickly detect the actual state of rotating machinery even under highly nonstationary conditions with abrupt changes and also precisely highlight frequency components in a signal highly contaminated with noise and therefore, yield accurate spectral information for an early warning of incipient fault in rotating machinery diagnosis. This is achieved through combination with a change detection statistic in bispectral domain.