Self-adaptive production performance monitoring framework under different operating regimes

Abstract

Dynamic operational regimes in modern manufacturing systems generate a myriad of challenges for production performance monitoring applications. Heterogeneous data streams and fast production changeovers often complicate sensor information, leading to misinterpretation of systemic performance issues. Conventional methods address this problem by explicitly modeling these operational regimes. However, it requires significant engineering hours and expertise, constituting a substantial adoption barrier for small-to-medium enterprises (SMEs). This paper proposes a self-adaptive smart monitoring framework that autonomously discovers and accounts for operational regime changes to offer accurate predictions on systemic performance despite the complexities in continuous multi-sourced data acquisition and dynamic regime behavior of machines. Computational experiments tested the methodology using a predictive system in two manufacturing cells under dynamic operational regimes. The proposed framework outperforms benchmark policies commonly used in prediction models by improving prediction accuracy from 3% to 62%, along with a better convergence rate. The results demonstrated that the proposed framework can positively impact smart maintenance implementation for SMEs with limited resources.