A just-in-time manifold-based fault detection method for electrical drive systems of high-speed trains

Electrical drive systems of high-speed trains are typical complex industrial systems with dynamic nonlinearity. During the actual operation of high-speed trains, the operation state is switched to meet the operation requirements, which leads to the multi-mode characteristics of electrical drive systems. Inherent characteristics of electrical drive systems have brought great obstacles to common fault detection methods. Therefore, online detection of incipient faults in electrical drive systems is imperative. On the one hand, the symptoms of incipient faults are slight and easy to be covered by unknown noises and disturbances; On the other hand, incipient faults will corrupt the health state and system remaining life, and gradually evolve into destructive faults. With the help of the idea to solve global problems through local modeling, this paper constructs a just-in-time manifold model by integrating local manifold learning into the just-in-time learning framework. The proposed scheme avoids the loss of feature information in the global structure by extracting the feature information of each local structure. The model construction is based on the eigenstructure of local data, which reduces the computational complexity of modeling and improves the detection accuracy. Ultimately, the efficacy and superiority of the proposed scheme are illustrated via a series of experiments on a platform of electrical drive systems.