• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.929-940
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• 2006

Early detection and diagnosis of incipient induction machine faults increases machinery availability, reduces consequential damage, and improves operational efficiency. However, fault detection using analytical methods is not always possible because it requires perfect knowledge of a process model. This paper proposes a neural network based expert system for diagnosing problems with induction motors using vibration analysis. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals, and the neural network is trained and tested using the vibration spectra. The efficiency of the developed neural network expert system is evaluated. The results show that a neural network expert system can be developed based on vibration measurements acquired on-line from the machine.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.314-316
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• 1993

In this paper, a method to detect and diagnose faults in Automated Manufacturing Systems(AMS) is proposed. In AMS, it is necessary to monitor the process-status. The detection and diagnosis of faults are often difficult in monitoring level with given passive data. We propose the model-based monitoring system for faults detection and diagnosis using Petri Nets to model AMS efficiently and easily. Simulation results show the validity of proposed method with example of Reverse Mill Process in Automated Mill Lines.

• Korean Chemical Engineering Research
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• v.58 no.3
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• pp.408-415
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• 2020

In this study, the AE-SOM method, which combines auto-encoder and self-organizing map, is used to detect and diagnose faults in EVA production process. Then, the fault propagation pathways are identified using Granger causality test. One year and seven months of operation data were obtained to detect faults of the process, and the process variables of the autoclave reactor are mainly analyzed. In the data pretreatment process, the data are standardized and 200 samples of each grade are randomly chosen to obtain a fault detection model. After that, the best matching unit (BMU) of each grade is confirmed by applying AE-SOM. The faults are determined based on each BMU. When a fault is found, the most causative variable of the fault is identified by using a contribution plot, and the fault propagation pathway is identified by Granger causality test. The prognostic of the two shutdowns is detected, and the fault propagation pathway caused by the faulty variable was analyzed.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.6
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• pp.347-357
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• 2013

Cyber-Physical system(CPS) is characterized by collaborating computational elements controlling physical entities. In CPS, human desire to acquire useful information and control devices anytime and anywhere automatically has increased the necessity of a high reliable system. However, the physical world where CPS is deployed has management complexity and maintenance cost of 'CPS', so that it is impossible to make reliable systems. Thus, this paper presents an 'Autonomic Control System towards High-reliable Cyber-Physical Systems' that comprise 8-steps including 'fault analysis', 'fault event analysis', 'fault modeling', 'fault state interpretation', 'fault strategy decision', 'fault detection', 'diagnosis&reasoning' and 'maneuver execution'. Through these activities, we fascinate to design and implement 'Autonomic control system' than before. As a proof of the approach, we used a ISR(Intelligent Service Robot) for case study. The experimental results show that it achieves to detect a fault event for autonomic control of 'CPS'.

• Journal of Electrical Engineering and Technology
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• v.5 no.1
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• pp.36-44
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• 2010

This paper presents the design of an algorithm to detect, identify, and locate faults in radial distribution feeders of Provincial Electricity Authority (PEA). The algorithm consists of three major steps. First, the adaptive algorithm is applied to track/estimate the system electrical parameter, i.e. current phasor, voltage phasor, and impedance. Next process, the impedance rule base is used to detect and identify the type of fault. Finally, the current compensation technique and a geographic information system (GIS) are applied to evaluate a possible fault location. The paper also shows the results from field tests of the automate fault location and illustrates the effectiveness of the proposed fault location scheme.

• Proceedings of the KIEE Conference
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• 1996.11a
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• pp.450-453
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• 1996

This research presents a new method of detecting and diagnosing faults of a power plant. Detection of characteristic wave patterns from multichannel instrumentation signals forms the basis of the proposed approach. The dynamics of 500MW drum-type boiler (Boryung coal-fired plant unit #1 and #2) and its control systems are modeled and simulated to generate diverse operation patterns and fault situations and to utilize them for the development of the fault detection algorithms. The results of the boiler system modeling and simulations show a fairly high agreement when compared with some of the actual plant performance test data.

• Plant Journal
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• v.11 no.2
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• pp.39-44
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• 2015

According to a research that is based on a previous study, But in a different way, This study shows fault diagnosis of Induction motor bearing which runs in coal-fired power plant industries on Desulfurization absorber agitator using Spectrum analysis of Stator Current and visual inspection. As a result of harmonic content analysis of stator current spectrum, It was possible to detect ball and outer race fault frequency. The comparison in the context of this experiment proves that the amplitude of faulty frequency is increased in three times at a fault in ball and in outer race. Spectrum analysis of stator current can be used to detect the presence of a fault condition as well as experiment in faulty bearings, besides early fault detection in bearings can prevent unexpected power generation loss and emergency maintenance cost.

• Journal of Electrical Engineering and Technology
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• v.2 no.3
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• pp.353-357
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• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2007.05a
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• pp.274-279
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• 2007

The fault identification of electrical rotating machinery have been special interests due to one of important elements in the industrial production line. It is directly related with products quality and production costs. The sudden breakdown of a motor will affect to the shut down of the whole processes. Therefore, rotating machines are required to a periodic diagnosis and maintenance for improving its reliability and increasing their lifetime. The objective of this work is to develop the diagnosis system with current signals for the effective identification of healthy and faulty motors using the developed diagnosis algorithm, which consists of the feature calculation, feature extraction, and feature classification procedures.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.