Citation

Lim, Way Soong and Wong, Wai Kit and Loo, Chu Kiong (2011) Quaternion based omnidirectional machine condition monitoring system. International Journal of Image Processing (IJIP), 5 (2). pp. 145-165. ISSN 1985-2304

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Abstract

Thermal monitoring is useful for revealing some ser ious electrical problems in a factory that often go undetected until a serious breakdown occurs. In facto ries, there are various types of functioning machines to be monitored. When there is any malfunctioning of a machine, extra heat will be generated which can be picked up by thermal camera for image processing and identification purpose. In this paper, a new and effective omnidirectional machine condition monitoring system applying log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is proposed for monitoring machine condition in an omnidirectional view. With this monitoring system, it is convenient to detect and monitor the conditions of (overheat or not) of more than one machines in an omnidirectional view captured by using a single thermal camera. Log-polar mapping tec hnique is used to unwarp omnidirectional thermal image into panoramic form. Two classification characteristics namely: peak to sidelobe ratio (PSR) and real to complex ratio of the discrete quaternion correlation output (p-value) are applied in the proposed machine condition monitoring system. Large PSR and p-value observe in a good match among correlation of the input thermal image with a particular reference image, while small PSR and p-value observe in a bad/not match among correlation of the input thermal image with a particular reference image. Simulation results also show that the proposed system is an efficient omnidirectional machine monitoring system with accuracy more than 97%.

Item Type:	Article
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	15 Jan 2014 01:22
Last Modified:	29 Dec 2020 18:59
URII:	http://shdl.mmu.edu.my/id/eprint/4833

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