Nowadays, most factories rely on machines to help boost up their production and process. Therefore, an effective machine condition monitoring system plays an important role in these factories to ensure that their production and process are running smoothly all the time. In this paper, a new and effective machine condition monitoring system using log-polar mapper, quaternion based thermal image correlator and max-product fuzzy neural network classifier is proposed. 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 this proposed machine condition monitoring system. Large PSR and p-value showed a good match among correlation of the input thermal image with a particular reference image, but reversely for small PSR and p-value match. In the simulation, log-polar mapping is found to have solved the rotation and scaling invariant problems in quaternion based thermal image correlation. Besides, log-polar mapping can possess two fold data compression capability. Log-polar mapping helps smoothen up the output correlation plane, hence making better measurement for PSR and p-values. The simulation results have also proven that the proposed system is an efficient machine condition monitoring system with an accuracy of more than 94%. © 2010 Elsevier B.V.
Wong, W. K., Loo, C. K., Lim, W. S., & Tan, P. N. (2010). Thermal condition monitoring system using log-polar mapping, quaternion correlation and max-product fuzzy neural network classification. Neurocomputing, 74(1–3), 164–177. https://doi.org/10.1016/j.neucom.2010.02.027