A Time-Series Driven Mechanical System State Description Method and Its Application in Condition Monitoring

Nonlinear and time-varying characteristics of mechanical systems may lead to the variation of fault modes which makes it hard to be accurately evaluated. To fulfill this requirement, the authors propose a time-domain signal-driven mechanical system state description method in this research. Based on the theory of stochastic subspace identification, an abnormal evaluation model is established to investigate the performance of mechanical systems. First, by constructing the projection matrix of the established Hankel matrixes from the given time series at different times, the system state matrix can be obtained. Subsequently, based on the eigenvectors of the reference state matrix, the testing samples can be decomposed. Finally, by calculating the difference between the reference and testing signal, the anomaly can be acquired, and the quantitative evaluation of the health of the mechanical system can also be realized. The proposed method is validated in bearing performance degradation, bearing fault diagnosis, and milling tool wear experiments and received positive results. The proposed method, therefore, provides a new possibility for monitoring the service performance of mechanical systems.