Journal Article10.1007/S10845-018-1431-X
Data-driven prognostic method based on self-supervised learning approaches for fault detection
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TL;DR: This paper proposes a data-driven method in a self-supervised manner, which is different from previous prognostic methods, and shows that the algorithm outperforms other fault detection methods.
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Abstract: 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.
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Citations
Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings
TL;DR: A case study on FEMTO-ST datasets shows that the fine-tuned model is competent for incipient fault detection, outperforming other state-of-the-art methods.
132
Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings
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Prior Knowledge-Augmented Self-Supervised Feature Learning for Few-Shot Intelligent Fault Diagnosis of Machines
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Detection of Anomalies in Industrial IoT Systems by Data Mining: Study of CHRIST Osmotron Water Purification System
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