An Intelligent Approach to Detecting Novel Fault Classes for Centrifugal Pumps Based on Deep CNNs and Unsupervised Methods
arXiv:2309.12765 · doi:10.1109/icspis54653.2021.9729350
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.
6 pages, 9 figures
References in corpus (4)
- Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
- Improving neural networks by preventing co-adaptation of feature detectors
- Deep Learning and Its Applications to Machine Health Monitoring: A Survey
- Missing-Class-Robust Domain Adaptation by Unilateral Alignment for Fault Diagnosis