Health State Evaluation for Production Systems by Aggregating Multi-source Data based Hidden Markov Model

摘要

Modern production systems are the carriers that advanced manufacturing technologies can be realized. Accurate health state evaluation is the basis for ensuring stable operation of the production system and developing a maintenance strategy However, the most existing health evaluation techniques of production systems are based on the single source data. The multi-source data based hidden Markov model is proposed to evaluate health state (including machine performance data, process quality data, etc.). The operational features-based health definition of the production system of production systems is proposed. The modeling process of hidden Markov model based on multi-source data is given, and the multi-performance degradation law of production system is revealed. And taking the pre-grinding gear hob production system as an example, the accuracy and superiority of this proposed approach are verified.