Abstract: Incorporating system state data from the dynamic response phase of a magnetostrictive sensor testing platform into training datasets for fault diagnosis strategies is challenging, often resulting in low reliability of data-driven diagnostic results. To address this issue, a data-driven fault diagnostic strategy based on steady-state identification is proposed. First, a steady-state identification strategy is designed to obtain system-state data generated during the steady-state operation of the test platform. Support vector data description (SVDD), which can handle single-class classification problems, is used for steady-state identification of the test platform. During the training process, particle swarm optimization (PSO) is utilized to optimize the hyperparameters of the SVDD. Second, a support vector machine (SVM) optimized by PSO is adopted to perform fault diagnosis based on the state data collected during the steady-state operation of the test platform to enhance fault diagnosis accuracy and the reliability of the diagnostic results. Finally, a simulation model of the test platform is established using the Simscape module in Simulink to validate the proposed steady-state identification-based fault diagnostic strategy. The simulation results demonstrate that compared with the SVM-based data-driven fault diagnosis strategy without steady-state identification, the proposed strategy achieves an average accuracy improvement of 5% across different modes, confirming the effectiveness of the proposed method for fault diagnosis in the test platform.

Key words: shield, magnetostrictive sensor test platform, steady-state identification, fault diagnosis