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International Journal of Rotating Machinery
Volume 2012, Article ID 847203, 10 pages
Research Article

Confirmation of Thermal Images and Vibration Signals for Intelligent Machine Fault Diagnostics

1Department of Mechanical Engineering, Faculty of Engineering, Diponegoro University, Jalan Professor Sudarto, Tembalang, Semarang 50275, Indonesia
2Department of Mechanical and Automotive Engineering, Pukyong National University, San 100, Yongdang-dong, Nam-gu, Busan 608-739, Republic of Korea
3Department of Energy and Mechanical Engineering, Institute of Marine Industry, Gyeongsang National University, 445 Inpyeong-dong, Gyeongnam-do, Tongyoung City 650-160, Republic of Korea

Received 20 June 2012; Revised 27 August 2012; Accepted 29 August 2012

Academic Editor: Hui Ma

Copyright © 2012 Achmad Widodo et al. 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.


This paper deals with the maintenance technique for industrial machinery using the artificial neural network so-called self-organizing map (SOM). The aim of this work is to develop intelligent maintenance system for machinery based on an alternative way, namely, thermal images instead of vibration signals. SOM is selected due to its simplicity and is categorized as an unsupervised algorithm. Following the SOM training, machine fault diagnostics is performed by using the pattern recognition technique of machine conditions. The data used in this work are thermal images and vibration signals, which were acquired from machine fault simulator (MFS). It is a reliable tool and is able to simulate several conditions of faulty machine such as unbalance, misalignment, looseness, and rolling element bearing faults (outer race, inner race, ball, and cage defects). Data acquisition were conducted simultaneously by infrared thermography camera and vibration sensors installed in the MFS. The experimental data are presented as thermal image and vibration signal in the time domain. Feature extraction was carried out to obtain salient features sensitive to machine conditions from thermal images and vibration signals. These features are then used to train the SOM for intelligent machine diagnostics process. The results show that SOM can perform intelligent fault diagnostics with plausible accuracies.