Research on health state assessment and prediction for complex equipment based on the improved FMECA and GM (1,1)

Health state assessment and prediction is the foundation of maintenance decision-making and resource managing for complex equipment. Aiming at complex equipment faults' multiplicity, coupling, and fuzziness, a novel health state assessment and prediction method is proposed. This proposed method is based on the improved failure mode effects and criticality analysis (FMECA) and grey model of the first-order differential equation with one variable (GM (1,1)). First, the improved FMECA is raised to measure the failure risk degree (FRD) of complex equipment and determine its health state in accordance with its FRD. Then, according to the state assessment results, the development trend of its health state is predicted by the improved GM (1,1). Finally, the effectiveness and superiority of the proposed method are verified through the health state assessment and prediction of the circulating water pump in company A. The implementing results reveal that the proposed method has significant advantages and is suitable for state assessment during a certain time period and state prediction in the short-range scenarios. Furthermore, reasonable values of parameters p and 19 can effectively improve its prediction accuracy.