Application of Self Organizing Map for Intelligent Machine Fault Diagnostics Based on Infrared Thermography Images

2011 ◽  
Author(s):  
Achmad Widodo ◽  
Djoeli Satrijo ◽  
Muhammad Huda ◽  
Gang-Min Lim ◽  
Bo-Suk Yang
Keyword(s):  
Infrared Thermography ◽  
Self Organizing Map ◽  
Fault Diagnostics ◽  
Intelligent Machine ◽  
Machine Fault ◽  
Self Organizing

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

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Achmad Widodo ◽  
Djoeli Satrijo ◽  
Toni Prahasto ◽  
Gang-Min Lim ◽  
Byeong-Keun Choi
Keyword(s):  
Vibration Signal ◽  
Rolling Element Bearing ◽  
Self Organizing Map ◽  
Fault Diagnostics ◽  
Vibration Signals ◽  
Thermal Images ◽  
Outer Race ◽  
Intelligent Machine ◽  
Machine Fault ◽  
The Time Domain

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.

Machine Fault Diagnosis and Prognosis using Self-Organizing Map

2014 ◽  
pp. 129-148
Author(s):  
Kesheng Wang ◽  
Zhenyou Zhang ◽  
Yi Wang
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Manufacturing Systems ◽  
Maintenance Scheduling ◽  
Self Organizing Map ◽  
Corrective Maintenance ◽  
Diagnosis And Prognosis ◽  
Machine Fault ◽  
Fault Diagnosis And Prognosis ◽  
Self Organizing

This chapter proposes a Self-Organizing Map (SOM) method for fault diagnosis and prognosis of manufacturing systems, machines, components, and processes. The aim of this work is to optimize the condition monitoring of the health of the system. With this method, manufacturing faults can be classified, and the degradations can be predicted very effectively and clearly. A good maintenance scheduling can then be created, and the number of corrective maintenance actions can be reduced. The results of the experiment show that the SOM method can be used to classify the fault and predict the degradation of machines, components, and processes effectively, clearly, and easily.

Auscultating Diagnosis for Hemodialysis Shunt Stenosis using a Self-Organizing Map and Hidden Markov Model

2012 ◽  
Vol 132 (10) ◽  
pp. 1589-1594 ◽  
Author(s):  
Hayato Waki ◽  
Yutaka Suzuki ◽  
Osamu Sakata ◽  
Mizuya Fukasawa ◽  
Hatsuhiro Kato
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Self Organizing Map ◽  
Self Organizing

An Auscultaiting Diagnosis Support System for Assessing Hemodialysis Shunt Stenosis by Using Self-organizing Map

2011 ◽  
Vol 131 (1) ◽  
pp. 160-166 ◽  
Author(s):  
Yutaka Suzuki ◽  
Mizuya Fukasawa ◽  
Osamu Sakata ◽  
Hatsuhiro Kato ◽  
Asobu Hattori ◽  
...  
Keyword(s):  
Support System ◽  
Self Organizing Map ◽  
Self Organizing

Classification of Heat Wave Events in Seoul Using Self-Organizing Map

2018 ◽  
Vol 9 (3) ◽  
pp. 209-221 ◽  
Author(s):  
Seung-Yoon Back ◽  
Sang-Wook Kim ◽  
Myung-Il Jung ◽  
Joon-Woo Roh ◽  
Seok-Woo Son
Keyword(s):  
Heat Wave ◽  
Self Organizing Map ◽  
Self Organizing
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