Reliable fault detection in train transmission systems is essential for safe and efficient railway operation, yet it is challenged by variable operating conditions and scarce fault samples. To address these issues, a novel unified health domain relation learning (UHDRL) framework is proposed. Specifically, a pseudofault sample library is constructed to generate diverse synthetic fault examples, reducing UHDRL’s reliance on healthy samples. A unified health domain mechanism is designed to map the different operating conditions into a common feature space, thereby reducing distribution shifts caused by operating variations. Additionally, a health relation learning mechanism is proposed to construct feature pairs between healthy representations and pseudo-faults to uncover intrinsic and discriminative attributes of health states. Experiments on three train transmission systems, conducted under both deterministic and non-deterministic operating condition changes, demonstrate that UHDRL is highly adaptable and robust in zero-fault-sample settings, improving detection accuracy by over 12% compared with existing methods.
Chen et al. (Fri,) studied this question.