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Publication Type : Conference Paper
Publisher : Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Institute of Electrical and Electronics Engineers Inc., p.764-767.
Source : Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, Institute of Electrical and Electronics Engineers Inc., p.764-767 (2016)
ISBN : 9781509061662
Keywords : Artificial intelligence, condition-based maintenance, Failure analysis, Fault detection, Fault diagnosis systems, Fault identifications, Fault tolerant computer systems, feature normalization, Frequency domain analysis, Higher dimensional features, Learning systems, Machine fault diagnosis, Mapping, Speed, Statistical features, Support vector machines, Time and frequency domains
Campus : Coimbatore
School : School of Engineering
Center : Computational Engineering and Networking
Department : Mechanical Engineering
Year : 2016
Abstract : High accuracy fault diagnosis systems are extremely important for effective condition based maintenance (CBM) of rotating machines. In this work, we develop a fault diagnosis system using time and frequency domain statistical features as input to a backend support vector machine (SVM) classifier. We evaluate the performance of the baseline system for speed dependent and speed independent performance. We show how feature mapping and feature normalization can help in enhancing the speed independent performance of machine fault diagnosis systems. We first perform feature mapping using locality constrained linear coding (LLC) which maps the input features to a higher dimensional feature space to be used as input to an SVM classifier (LLC-SVM). It is seen that there is a significant improvement in the speed independent performance of the fault identification system. We obtain an improvement of 11.81% absolute and 10.53% absolute respectively for time and frequency domain LLC-SVM systems compared to the respective baseline systems. We then explore variance normalization considering the speed specific variations as noise to further improve the performance of the fault diagnosis system. We obtain a performance improvement of 8.20% absolute and 6.71% absolute respectively over the time and frequency domain LLC-SVM systems. It may be noted that that the variance normalized LLC-SVM system outperforms. © 2016 IEEE.
Cite this Research Publication : A.S. Raghunath, Sreekumar, K. T., Kumar, C. S., and Dr. K. I. Ramachandran, “Improving speed independent performance of fault diagnosis systems through feature mapping and normalization”, in Proceedings - 2016 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016, 2016, pp. 764-767.