Multi-Sensor Information Based Remaining Useful Life Prediction With Anticipated Performance

For a class of multi-sensor dynamic systems subject to latent degradation, the remaining useful life prediction with anticipated performance is mainly considered in this paper. The hidden degradation process is first identified recursively by adopting distributed fusion filtering based on observations from multiple sensors. Then the remaining useful life distribution is predicted on the basis of converged degradation state and parameter updating during the operating process. The uncertainty index is aanalyzed to quantitatively evaluate the benefits of increasing multi-sensor information for predicted remaining useful life, and the sensor selection is also discussed for satisfying the anticipated performance such as variance. Our main results are verified by a numerical example, and a practical case study of the milling machine experiment.