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Condition Monitoring of Roller Bearing by K-Star Classifier and K-Nearest Neighborhood Classifier Using Sound Signal.

Rahul Kumar Sharma*1, V. Sugumaran1, Hemantha Kumar2, Amarnath M3
1 V.I.T. university, Chennai, India.
2 N. I. T. Karnataka, Srinivasanagar, Surathkal, Mangalore, Karnataka, India.
3 I. I. I. T. D. & M. Jabalpur, Khamaria, Jabalpur, India.
* Corresponding author
Department of Mechanical Engineering, School of Mechanical and Building Sciences, VIT University, Vandalur – Kelambakkam Road, Chennai - 600127, Tamil Nadu, India.
E-mail: rahulkumar.sharma2011@vitalum.ac.in

Structural Durability & Health Monitoring 2017, 11(1), 1-16. https://doi.org/10.3970/sdhm.2017.012.001

Abstract

Most of the machineries in small or large scale industry have rotating element supported by bearings for rigid support and accurate movement. For proper functioning of machinery, condition monitoring of the bearing is very important. In present study sound signal is used to continuously monitor bearing health as sound signals of rotating machineries carry dynamic information of components. There are numerous studies in literature that are reporting superiority of vibration signal of bearing fault diagnosis. However, there are very few studies done using sound signal. The cost associated with condition monitoring using sound signal (Microphone) is less than the cost of transducer used to acquire vibration signal (Accelerometer). This paper employs sound signal for condition monitoring of roller bearing by K-star classifier and k-nearest neighborhood classifier. The statistical feature extraction is performed from acquired sound signals. Then two layer feature selection is done using J48 decision tree algorithm and random tree algorithm. These selected features were classified using K-star classifier and k-nearest neighborhood classifier and parametric optimization is performed to achieve the maximum classification accuracy. The classification results for both K-star classifier and k-nearest neighborhood classifier for condition monitoring of roller bearing using sound signals were compared.

Keywords

K-star, k-nearest neighborhood; k-NN, machine learning approach, condition monitoring, fault diagnosis, roller bearing, decision tree algorithm; J48, random tree algorithm, decision making, two layer feature selection, sound signal, statistical features.

Cite This Article

Sharma, R. K., Sugumaran, V., Kumar, H., M, A. (2017). Condition Monitoring of Roller Bearing by K-Star Classifier and K-Nearest Neighborhood Classifier Using Sound Signal.. Structural Durability & Health Monitoring, 11(1), 1–16.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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