Abstract
Smart manufacturing systems are being advocated to leverage technological advances that enable them to be more resilient to faults through rapid diagnosis for performance assurance. In this paper, we propose a co-simulation approach for engineering digital twins (DTs) that are used to train Bayesian Networks (BNs) for fault diagnostics at equipment and factory levels. Specifically, the co-simulation model is engineered by using cyber–physical system (CPS) consisting of networked sensors, high-fidelity simulation model of each equipment, and a detailed discrete-event simulation (DES) model of the factory. The proposed DT approach enables injection of faults in the virtual system, thereby alleviating the need for expensive factory-floor experimentation. It should be emphasized that this approach of injecting faults eliminates the need for obtaining balanced data that include faulty and normal factory operations. We propose a Structural Intervention Algorithm (SIA) in this paper to first detect all possible directed edges and then distinguish between a parent and an ancestor node of the BN. We engineered a DT research test-bed in our laboratory consisting of four industrial robots configured into an assembly cell where each robot has an industrial Internet-of-Things sensor that can monitor vibrations in two-axes. A detailed equipment-level simulator of these robots was integrated with a detailed DES model of the robotic assembly cell. The resulting DT was used to carry out interventions to learn a BN model structure for fault diagnostics. Laboratory experiments validated the efficacy of the proposed approach by accurately learning the BN structure, and in the experiments, the accuracy obtained by the proposed approach (measured using Structural Hamming Distance) was found to be significantly better than traditional methods. Furthermore, the BN structure learned was found to be robust to variations in parameters, such as mean time to failure (MTTF).
Highlights
Publisher’s Note: MDPI stays neutralWith the increasing globalization of manufacturing, manufacturers are facing fiercer competition, leaving very little room for inefficiencies, such as downtime
Research in digital twin (DT), which used to be called virtual system, studied real-time decision-making by using such approaches for production and maintenance scheduling [74,75,76,77]
We explored a way to leverage DT to train a Bayesian network (BN), addressing key limitations of the current approaches in training Bayesian Networks (BNs), including insufficient and imbalance-class data, the high cost of carrying out intervention on the factory floor, and the subjective nature of expert elicitation
Summary
Publisher’s Note: MDPI stays neutralWith the increasing globalization of manufacturing, manufacturers are facing fiercer competition, leaving very little room for inefficiencies, such as downtime. Factory-level faults, on the other hand, occur at the unit, cell, area, site, or enterprise levels in the integrated ISA-95 and ISA-98 model [5]. They refer to underperformance of the overall system or subsystem expressed as shortcomings in key performance indicators with regard to jurisdictional claims in published maps and institutional affiliations. Identifying the causal relationships among process variables is required for effective fault diagnostics [24], which is challenging to derive from observational data, as statistical dependency does not always imply causality [25].
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