Application of Fuzzy Inference in FMECA for Hydraulic Excavators

Conventional FMECA typically evaluates failure modes in isolation, making it difficult to represent same‐level failure interactions, multiple simultaneous effects, and cross‐level propagation in complex mechanical systems such as hydraulic excavators. To address this issue, this article develops a multi‐level fuzzy inference framework integrated with FMECA for hydraulic excavator reliability analysis. The proposed method combines expert elicitation, triangular membership functions, and a hierarchical fuzzy rule base to quantify the propagation of failure effects from component failure modes to the subsystems and further to the entire machine level. A case study of six major subsystems shows that the Attachment and Hydraulic Circuit subsystems have the highest comprehensive impact scores, with values of 3.5 and 2.9, respectively. Further analysis indicates that the Attachment subsystem is mainly associated with boom‐ and arm‐related structural failure paths, whereas the Hydraulic Circuit subsystem is characterized by dense coupling among pump, valve, actuator, oil, line, and cooling branches. A Monte Carlo‐based sensitivity analysis further shows that the main ranking pattern is robust under reasonable perturbations of the input severity scores. These findings identify the dominant weak links of the hydraulic excavator system and provide quantitative support for maintenance prioritization and reliability‐oriented decision‐making.