Preferred decision for industrial equipment operation rotation considering health state based on belief rule base and evidential reasoning

The health state of equipment will decline in the long-term operation, resulting in the need to rotate multiple equipment to fulfill the operation task (OT). In the current engineering, three available equipment rotation strategies are summarized. However, the selection of these strategies is arbitrary and the health state of the equipment during operation rotation is neglected, which causes poor benefits and heavy risks. For this purpose, a quantitative decision-making mechanism using belief rule base (BRB) and evidential reasoning (ER) is proposed to determine the preferred strategy. Specifically, BRB serves as the preferred decision model, which reflects the mapping relationship between the OT and the rotation strategy. A parameter optimization model is then designed to improve decision rationality. To obtain the labeled historical OTs required for the parameter optimization model, a hierarchical ER method is developed to evaluate the performance of the rotation strategy to obtain the labeled historical OTs, where the health state of the equipment is quantitatively analyzed. The proposed method comprehensively utilizes knowledge and data and provides a quantitative decision-making framework for equipment operation rotation. A case of the natural gas storage tank (NGST) verifies the effectiveness of the proposed method.