Fault localization for automatic train operation based on the adaptive error locating array algorithm

When the combinatorial testing method is used to locate faults in the complex signalling system of high-speed rail in order to prevent the system from being affected by combinatorial testing case explosion, which could results from the masking effects caused by multiple faults, the Minimum Fault Schema (MFS) can be accurately and efficiently located. Taking the Automatic Train Operation (ATO) scenario in intelligent high-speed rail as an example, a fault localization method based on the Adaptive Error Locating Array (AELA) algorithm is proposed. To begin with, according to the characteristics of ATO, the adaptive fault localization model is designed and the test parameter table is constructed. Then the Partial Variable Intensity Covering Array (PVICA) algorithm is used to generate the initial set of test cases, and the cases are executed sequentially. Furthermore, based on the test case execution results, the fault location module is invoked to prioritize the generation of additional test cases. These cases are designed to locate the MFS more easily in the given parameter range using the Adaptive Particle Swarm Optimization (APSO) algorithm. Finally, the MFS is determined. The validity and accuracy of the proposed method are verified through the simulation testing platform for Beijing-Shenyang high-speed rail. The experimental results of ablation and comparison show that the Integrity , average Accuracy and average C-Evaluation of the proposed algorithm can reach up to 100%, 91.07% and 84.56% respectively. Compared to four mainstream adaptive algorithms of fault localization, the proposed algorithm is less affected by the masking effects caused by multiple faults and requires the fewest number of test cases. The research results can offer useful guidance for verifying the integrity and reliability of the ATO function and contribute to the intrinsic safety for rail transit.