Publication Type:

Conference Paper

Source:

12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, Institute of Electrical and Electronics Engineers Inc. (2015)

ISBN:

9781467373999

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-84994311266&partnerID=40&md5=b5bdac32ce52dd9a597ca9f815e6ad85

Keywords:

Artificial intelligence, Classification accuracy, Failure analysis, Fault detection, Fault diagnosis systems, Fault tolerant computer systems, Frequency domain analysis, Higher-dimensional, Image retrieval, K-nearest neighbours, Learning algorithms, Learning systems, Machine fault diagnosis, Nearest neighbor search, Rotating machinery, Speed, Statistical features, Support vector machines, Time and frequency domains, Time domain features, Vector spaces, Vectors, Vibration analysis

Abstract:

<p>Support Vector Machine (SVM) is an important machine learning algorithm widely used for the development of machine fault diagnosis systems. In this work, we use an SVM back-end classifier, with statistical features in time and frequency domain as its input, for the development of a fault diagnosis system for a rotating machine. Our baseline system is evaluated for its speed dependent and speed independent performances. In this paper, we use locality constrained linear coding (LLC) to map the input feature vectors to a higher dimensional linear space, and remove some of the speed specific dimensions to improve the speed independent performance of the fault diagnosis system. We use LLC to do the feature mapping to the higher dimensional space, and select only the k nearest neighbour basis vectors to represent the input feature vector and thus reduce/minimize the effect of speed specific factors from the input feature vector, and thus improve the speed independent performance of the fault diagnosis. We compare the performance of the LLC-SVM system for the time and frequency domain statistical features. The proposed approach has improved the overall classification accuracy by 11.81% absolute for time domain features and 10.53% absolute for frequency domain features compared to the baseline speed independent system. © 2015 IEEE.</p>

Notes:

cited By 0; Conference of 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control, INDICON 2015 ; Conference Date: 17 December 2015 Through 20 December 2015; Conference Code:121075

Cite this Research Publication

K. Ta Sreekumar, Gopinath, Ra, Pushparajan, Mb, Raghunath, A. Sa, Dr. Santhosh Kumar C., Ramachandran, K. Ia, and Saimurugan, Mb, “Locality constrained linear coding for fault diagnosis of rotating machines using vibration analysis”, in 12th IEEE International Conference Electronics, Energy, Environment, Communication, Computer, Control: (E3-C3), INDICON 2015, 2015.

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