Reliability Analysis and Cloud-aided Health Management for Electric Locomotive Vehicle Circuit Board

Abstract

This work proposes a method for predicting the life of a circuit board based on circuit board reliability analysis and cloud-aided temperature. Firstly, a digital prototype of the circuit board for the circuit locomotive is established, the operating conditions of the circuit locomotive are collected, and thermal simulations are performed based on the characteristics of the working conditions. Next, the Failure Mode Mechanism and Effects Analysis (FMEA) method is used to conduct failure mechanism analysis on the circuit board to analyze its underlying failure physical model for reliability. Based on the physical failure model, the circuit board's reliability analysis and life prediction are performed according to the thermal, and the weak points in the design are identified. Finally, a thermocouple sensor is used to collect the temperature of the weak point of the circuit board, which is uploaded to the server through the 5G module for real-time monitoring of the circuit board's status. The circuit board's thermal simulation and vibration simulation analysis identified four high-temperature areas in the circuit board, which are the primary mode of circuit board failure caused by solder joint cracking, easily affected by temperature cycle conditions. By adding a temperature sensor to the weak point, real-time collection and detection of the circuit board's temperature are achieved. Compared with traditional reliability analysis methods, this method can realize real-time monitoring of weak points and provide a circuit board product improvement plan.