A health self-sensing framework for electromechanical equipment using encoder signal

Health sensing plays a pivotal role to ensure the reliability of electromechanical equipment. However, conventional health sensing methods rely too heavily on additional sensors, while requirements of installation and wiring for external sensors limit their wide application in the era of industry 4.0. To this end, a health self-sensing approach for electromechanical equipment is proposed. Firstly, the health information is captured via rotary encoders that are more widely applied in electromechanical equipment. To separate the speed jitter caused by failure, a Gini-guided Robust principal component analysis (RPCA) method is proposed for further processing to realize self-sensing of equipment. Finally, the effectiveness of presented framework is verified by simulation and experiment.