This paper aims to enhance the effectiveness of risk management in the entire vehicle development process. Based on a Sino-foreign cooperative entire vehicle research and development project, the study analyzes the characteristics of the low-voltage harness in vehicles, identifies and categorizes risk factors, and utilizes fault tree analysis to model the causal relationships of various risks. These relationships are then mapped into a Bayesian Network (BN). By employing the fuzzy set theory, collected expert opinions are transformed into prior and conditional probabilities of node events, establishing a risk assessment model for vehicle low-voltage harness development based on fault tree-Bayesian network. This model quantifies the probability and impact of risk factors. For key and sensitive risk factors, corresponding risk response strategies are proposed, providing effective risk management for harness development.

Automotive wiring harness  /  Risk management  /  Fault Tree Analysis (FTA)  /  Bayesian Network (BN)