In-process modal parameter identification for spindle health monitoring

This paper presents a stochastic subspace identification (SSI)-based approach for in-process spindle health monitoring. The approach identifies modal parameters of a spindle from the structural excitations experienced by the spindle during its operation. Experimental results obtained from the spindle system under different speeds and loading conditions confirm the effectiveness of the approach. The results further indicate that variations in the modal parameters identified during the experiments can serve as an indicator for the spindle's health condition. Therefore, the SSI-based modal parameter identification method can be effectively utilized for monitoring spindle health condition.