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Publication Type : Conference Paper
Publisher : Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016), Springer International Publishing, Cham .
Source : Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016), Springer International Publishing, Cham (2018)
ISBN : 9783319606187
Keywords : Feature extraction, Least Square Support Vector Machine (LS-SVM), Machine condition monitoring, Overlapping Group Shrinkage (OGS), Variational mode decomposition (VMD), Vibration analysis.
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Center for Computational Engineering and Networking (CEN)
Year : 2018
Abstract : Maintenance of rotating parts in machines is not easy. Prediction of faults in advance reduces the frequency of breakdown and improves the life time of machines. This paper proposes a machine condition monitoring system, which formulates the fault diagnosis problem as a machine learning based pattern classification problem. The vibration signals acquired from rotating machines are initially processed by a group-sparse denoising algorithm namely Overlapping Group Shrinkage (OGS). In OGS, the group sparse signal denoising problem is casted as a convex optimization problem with a group sparsity promoting penalty function. The denoised signals are then processed by Variational Mode Decomposition (VMD), which decomposes the signal into specific frequency modes. For representing the signal in the feature space, energy of each mode is extracted and is classified by LS-SVM classifier. The performance of the proposed condition monitoring system is evaluated in terms of classification accuracies and is compared with statistical features.
Cite this Research Publication : A. Chandran, Anjali, T., Dr. Neethu Mohan, and Dr. Soman K. P., “Overlapping Group Sparsity Induced Condition Monitoring in Rotating Machineries”, in Proceedings of the Eighth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2016), Cham, 2018.