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Intelligent Fault Diagnosis of Induction Motors Using Vibration Signals
한천(Tian Han),양보석(Bo-Suk Yang),김재식(Jae-Sik Kim) 대한기계학회 2004 대한기계학회 춘추학술대회 Vol.2004 No.4
In this paper, an intelligent fault diagnosis system is proposed for induction motors through the combination of feature extraction, genetic algorithm (GA) and neural network (ANN) techniques. Features are extracted from motor vibration signals, while reducing data transfers and making on-line application available. GA is used to select most significant features from whole feature database and optimize the ANN structure parameter. Optimized ANN diagnoses the condition of induction motors online after trained by the selected features. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origin on the induction motors. The results of the test indicate that the proposed system is promising for real time application.
Development of an e-maintenance system integrating advanced techniques
Tian Han(한천),Bo-Suk Yang(양보석) 한국동력기계공학회 2004 한국동력기계공학회 학술대회 논문집 Vol.- No.-
In recent years, the globalization and fast growth of the Internet technologies, computer and information technologies make industry maintenance pattern changed. Accordingly, a new maintenance concept, e-maintenance, comes out and has been gradually replacing traditional maintenance. In this paper, a new e-maintenance system is proposed that mainly focuses on cooperation and sharable information among associated areas, and the application of advanced techniques.
Wireless PDA-based Machine Condition Monitoring
Jong-Duk Son(손종덕),Tian-Han(한천),Dong-Soo Lim(임동수),Bo-Suk Yang(양보석) 대한기계학회 2005 대한기계학회 춘추학술대회 Vol.2005 No.11
Mobile computing devices are becoming increasingly prevalent in a huge range of physical area, offering considerable market opportunities. Mobile devices like personal digital assistant (PDA) can support remote condition monitoring in plant equipments. LabVIEW software allows easy interactions between acquisition instrumentation and operators. Also it can integrate artificial intelligence algorithms. This paper presents preliminary results of a platform for remote monitoring system, which aims at enabling mobile access to real-time monitoring data by means of PDA. This system consists of two parts; one is condition monitoring and the other is fault diagnosis by using an ART-KNN neural network. Neural network algorithm does not destroy the initial training. It uses additional training data that is suitable for the classification of machine conditions. Condition monitoring and fault diagnosis are conducted by LabVIEW in a notebook PC. The proposed system is applied to the condition monitoring and fault diagnosis of an induction motor. The application demonstrates that wireless PDA is a convenient device for the development of a powerful user interface in maintenance.
Random Forest Classifier for Machine Fault Diagnosis
Xiao Di(디샤오),Tian Han(한천),Bo-Suk Yang(양보석) 대한기계학회 2006 대한기계학회 춘추학술대회 Vol.2006 No.11
This paper is to confirm the possibilities of applying random forests algorithm (RF) in machine fault diagnosis and propose a hybrid method combined with genetic algorithm to improve the classification accuracy. The proposed method is based on RF, a novel assemble classifier which builds a number of decision trees to improve on the single tree classifier. Although there are several existed techniques for faults diagnosis, the application research on RF is meaningful and necessary because of its fast execution speed, the characteristic of tree classifier, and high performance in machine faults diagnosis. Evaluation of RF based method has been demonstrated by a case study on induction motor faults diagnosis. Experimental results indicate the validity and reliability of RF based diagnosis method.