Data-driven prognostic method based on self-supervised learning approaches for fault detection

As a part of prognostics and health management (PHM), fault detection has been used in many fields to improve the reliability of the system and reduce the manufacturing costs. Due to the complexity of the system and the richness of the sensors, fault detection still faces some challenges. In this paper, we propose a data-driven method in a self-supervised manner, which is different from previous prognostic methods. In our algorithm, we first extract feature indices of each batch and concatenate them into one feature vector. Then the principal components are extracted by Kernel PCA. Finally, the fault is detected by the reconstruction error in the feature space. Samples with high reconstruction error are identified as faulty. To demonstrate the effectiveness of the proposed algorithm, we evaluate our algorithm on a benchmark dataset for fault detection, and the results show that our algorithm outperforms other fault detection methods.