This study presents a tri-state modeling-based FTA-BN hybrid diagnostic framework for assessing the reliability of diesel powertrains in heavy-duty railway maintenance machinery. Recognizing the critical role of these power units in project timeline control and construction efficiency, the framework addresses the challenge of multi-phase fault evolution under harsh operating conditions. A tri-state model—comprising fully operational, performance degradation, and functional failure states—is introduced to enable dynamic, quantitative evaluation of system degradation, overcoming the limitations of traditional binary-state models in characterizing performance degradation processes. The methodology employs a modular failure decomposition strategy and constructs a multi-level fault tree model based on the functional topology of the Deutz BF12L513C powertrain. By integrating a Dynamic Bayesian Network (DBN), the framework achieves three objectives: (1) probabilistic fusion of long-term operational data, (2) quantification of expert-based conditional probabilities using the Delphi method, and (3) uncertainty propagation among coupled failure modes. Analysis of 3 years of field data yields a system reliability of 0.9330, with a 6.25% probability of performance degradation and a 0.44% probability of functional failure. Fault path analysis identifies hydraulic circuit integrity (node X67) and the exhaust energy recovery subsystem (node X73) as key reliability bottlenecks. This framework offers a scalable approach for preventive maintenance and life-cycle reliability management in complex engineering systems.
Zhang et al. (Tue,) studied this question.