An Intelligent Approach to Detecting Novel Fault Classes for Centrifugal Pumps Based on Deep CNNs and Unsupervised Methods

• URL: http://arxiv.org/abs/2309.12765v1
• Date: Fri, 22 Sep 2023 10:10:30 GMT
• Title: An Intelligent Approach to Detecting Novel Fault Classes for Centrifugal Pumps Based on Deep CNNs and Unsupervised Methods
• Authors: Mahdi Abdollah Chalaki, Daniyal Maroufi, Mahdi Robati, Mohammad Javad Karimi, Ali Sadighi
• Abstract summary: In this paper, we assume a partial knowledge of the system faults and use the corresponding data to train a convolutional neural network. A combination of t-SNE method and clustering techniques is then employed to detect novel faults.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Despite the recent success in data-driven fault diagnosis of rotating machines, there are still remaining challenges in this field. Among the issues to be addressed, is the lack of information about variety of faults the system may encounter in the field. In this paper, we assume a partial knowledge of the system faults and use the corresponding data to train a convolutional neural network. A combination of t-SNE method and clustering techniques is then employed to detect novel faults. Upon detection, the network is augmented using the new data. Finally, a test setup is used to validate this two-stage methodology on a centrifugal pump and experimental results show high accuracy in detecting novel faults.

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