Degradation of electronic equipment is typically accompanied by a deviation in their electrical parameters from their design values. When the function of the electronic equipment fails, parametric faults of the electrical parameters will ultimately occur. Such electrical parameters can characterize the state of health can be defined as health parameters. For complex electronic equipment such as hardware circuits of Nuclear power plant DCS cards, the use of health parameters for fault diagnostic has been widely used, but there is no mature method for sub-health diagnostic. To address this problem, an electronic equipment health assessment method was developed using a technology based on the combination of mechanism knowledge and data-driven. The method first uses Physics of Failure approach and hardware circuit logic principle to identify the major failure mechanisms of the circuit, and then determines the key health parameters and their sub-health thresholds on this basis. The time series data under specific test conditions for the key health parameters of the circuits to be assessed is collected and compared with the sub-health thresholds, then the health status assessment results based on mechanism knowledge can be obtained. As well, the data-driven health assessment technology based on Mahalanobis distance is applied to analyze the real-time health parameter data of all samples, and the healthy outlier samples are identified and quantified, and the health status assessment results based on the data-driven technology can be obtained. The results of the two are comprehensively evaluated to finally determine the circuit health status. Practice was carried out on the DCDC chip of a nuclear power DCS analog output module, and the results showed that this method was feasible for engineering.

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