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Sensors 2016, 16(3), 316; doi:10.3390/s16030316

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry

1
Faculty of Engineering, University of Kragujevac, Kragujevac 34000, Serbia
2
Tetra Pak, Gornji Milanovac Packaging Materials Plant, Gornji Milanovac 32300, Serbia
3
School of Electrical Engineering, University of Belgrade, Belgrade 11000, Serbia
4
Faculty of Mathematics and Informatics, University of Sofia, Sofia 1000, Bulgaria
5
Faculty of Technology, University of Nis, Nis 18000, Serbia
6
Center of Excellence DEWS, University of L’Aquila, L’Aquila 67100, Italy
*
Author to whom correspondence should be addressed.
Academic Editor: Thomas B. Messervey
Received: 11 December 2015 / Revised: 19 February 2016 / Accepted: 25 February 2016 / Published: 1 March 2016
View Full-Text   |   Download PDF [4146 KB, uploaded 1 March 2016]   |  

Abstract

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings. View Full-Text
Keywords: total productive maintenance; reliability; bearings; fault diagnosis; wavelet transform; statistical pattern recognition total productive maintenance; reliability; bearings; fault diagnosis; wavelet transform; statistical pattern recognition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Gligorijevic, J.; Gajic, D.; Brkovic, A.; Savic-Gajic, I.; Georgieva, O.; Di Gennaro, S. Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry. Sensors 2016, 16, 316.

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