**1. Introduction**

Compared with other transportation means, the high-speed train (HST) has been recognized as one of the most popular tools owing to its unique advantages such as safe, comfortable and environmentally friendly. It is reported that Japan, Germany, China and France have their own HST intellectual property rights [1, 2]. On the other hand, the system performance could be severely affected by various uncertainties, such as material diversities, manufacturing tolerances and operational environment variations, resulting in a high probability of failure. As the key power equipment of HST, the resilience enhancement of the traction motor control system is essential to ensure its reliable operation. Most of the fault feature extraction and diagnosis strategies are based on the motor current signature analysis, which may cause false-negative diagnosis results in several cases [1–3]. As an alternative analysis tool, the motor's torque has been proved as the resilience enhancement method most affected by the faults during the steady-state operation [4]. In addition, the HST traction motor is a

fail-safe system with few fault samples, which makes the traditional machine learning algorithm (especially neural network) fails to the lower diagnosis accuracy associated with limited fault characteristic information [5]. In order to improve the coupled fault diagnosis, the support vector machine (SVM) classification model optimized by the gray wolf optimizer (GWO) algorithm has been proposed in [6, 7]. Though GWO has more obvious optimization power than other heuristic algorithms, the optimal local outcomes will often occur with an inflexible scheme.

It is worth noting that most of the existing works are brought forward under the assumption that only certain types of single fault may occur [8, 9]. However, in practice, coupled faults are more common to encounter in HST [1]. Considering the tight coupling components in traction motor, a single stator interturn fault may eventually lead to rotor broken bar fault [10, 11].

In general, compared with the traditional method, this paper has considered electromagnetic torque characteristics, data driven model and fault attribute knowledge coding to construct an agile fault feature matrix. Aiming at the linear indivisible attribute between fault feature and the corresponding fault classes, an improved GWO (IGWO) has been proposed to agile optimize the SVM classification model with various fault severity levels. Through the experimental analysis of the multiple fault diagnosis indexes, the advantages of the proposed reliable diagnosis approach have been proved.
