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. 1. Introduction Machine condition monitoring and fault diagnostics are very important for machineries in the industry to guarantee high performance of their functionality. High reliability is needed to reduce product and profit loss because of unwanted downtime. Therefore, machine condition monitoring and fault diagnostics become a critical issue in maintenance activity to ensure availability, minimize operator hazard, and reduce economic losses [1]. For this purpose, several sensors such as vibration sensors, acoustic emission sensors, and temperature sensors are used to fulfill the requirement for machine condition monitoring procedure [2]. The assessment of machine components of critical rotating machines, such as bearings (journal or rolling element), using such sensors is significant for early detection of machine condition. The application of vibration sensors as fundamental tools for machine condition monitoring has been used extensively over a period of approximately four decades [3]. The reason to use these sensors was their effectiveness of measurement process and data analysis that can represent the machine conditions. Vibration signal monitoring of installed
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