• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.578-580
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• 1997

A new method of Fault Diagnosis in linear systems using unknown input observer design technique is presented. This method is based upon the fact that the structural uncertainties, actuator faults, and sensor faults of a linear system can be rewritten in unknown inputs. The proposed method can simultaneously estimate the state variables of an actual system, as well as the actuator and sensor faults.

• Journal of Electrical Engineering and Technology
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• v.2 no.4
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• pp.485-493
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• 2007

This paper deals with the problem of detection of induction motor incipient faults. Artificial Neural Network (ANN) approach is applied to detect two types of incipient faults (1). Interturn insulation and (2) Bearing wear faults in single-phase induction motor. The experimental data for five measurable parameters (motor intake current, rotor speed, winding temperature, bearing temperature and the noise) is generated in the laboratory on specially designed single-phase induction motor. Initially, the performance is tested with two inputs i.e. motor intake current and rotor speed, later the remaining three input parameters (winding temperature, bearing temperature and the noise) were added sequentially. Depending upon input parameters, the four ANN based fault detectors are developed. The training and testing results of these detectors are illustrated. It is found that the fault detection accuracy is improved with the addition of input parameters.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1558-1565
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• 2015

In this paper, we propose a new method for detecting bearing faults using vibration signals. The proposed method is based on support vector machines (SVMs), which treat the harmonics of fault-related frequencies from vibration signals as fault indices. Using SVMs, the cross-validations are used for a training process, and a two-stage classification process is used for detecting bearing faults and their status. The proposed approach is applied to outer-race bearing fault detection in three-phase squirrel-cage induction motors. The experimental results show that the proposed method can effectively identify the bearing faults and their status, hence improving the accuracy of fault diagnosis.

• Proceedings of the KIEE Conference
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• 2007.04c
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• pp.150-152
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• 2007

The stator faults yield asymmetrical operation of induction machines, such as irregular current, torque pulsation, increased losses and decreased average torque. So it is necessary to detect the stator faults and develope the monitoring system for detecting faults including vibration and noise. This paper describes the method to analysis the induction motors with the stator winding inter-turn short for investigation of the asymmetrical operation during normal and transient states. And a simple method is used for the simulation and analysis of the induction machines with stator asymmetries. Finally, simulation results, finite element analysis and experimental ones are presented. The results can be useful for real-time on-line monitoring of an induction motor.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1233-1240
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• 2004

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Transactions of the Korean Society of Automotive Engineers
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• v.25 no.3
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• pp.273-286
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• 2017

We develop an inspection method for bearing faults with a rapid change in the rotation speed and present indexes for the pass/fail inspection. At the end of line, impulse noises generated by the operation of machines and conveyors may distort the inspection results. In this paper, we present robust inspection indexes for bearing faults under impulse noises, by taking into account fault signals having pulse train. Using logistic regression, we optimize the pass/fail criterion for each index and evaluate the performance of the inspection indexes based on the total error rate.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2058-2064
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• 2014

In transformer fault diagnosis, dissolved gas analysis (DGA) is been widely employed for a long period and numerous methods have been innovated to interpret its results. Still in some cases it fails to identify the corresponding faults. Due to the limitation of training data and non-linearity, the estimation of key-gas ratio in the transformer oil becomes more complicated. This paper presents Intuitionistic Fuzzy expert System (IFS) to diagnose several faults in a transformer. This revised approach is well suitable to diagnosis the transformer faults and the corresponding action to be taken. The proposed method is applied to an independent data of different power transformers and various case studies of historic trends of transformer units. It has been proved to be a very advantageous tool for transformer diagnosis and upkeep planning. This method has been successfully used to identify the type of fault developing within a transformer even if there is conflict in the results of AI technique applied to DGA data.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1376-1385
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• 2017

This paper proposes a method for estimating the expected frequency of voltage swells caused by asymmetrical faults in a power system. Although voltage swell is less common than voltage sag, repeated swells can have severe destructive impact on sensitive equipment. It is essential to understand system performance related to voltage swells for finding optimal countermeasures. An expected swell frequency at a sensitive load terminal can be estimated based on the concept of an area of vulnerability (AOV) and long-term system fault data. This paper describes an effective method for calculating an AOV to voltage swells. Interval estimation for an expected swell frequency is also presented for effective understanding of system performance. The proposed method provides long-term performance evaluation of the frequency and degree of voltage swell occurrences.

• Proceedings of the Korean Information Science Society Conference
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• 2011.06a
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• pp.336-339
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• 2011

When SOAP messages carry vital business information, their integrity and confidentiality needs to be preserved. Concerns have been raised due to XML Rewriting attacks on SOAP message which create a foundation for typical faults in SOAP messages and make it vulnerable to use in Web Service environment. We have already provided a solution to tackle this problem on integrity of SOAP messages in earlier works by proposing a system called System Description Assistant. That system was able to identify and fix typical faults in SOAP messages. This paper mainly reflects future directions of our previous researches and enhances previous ones by adding more comprehensive functions to detect and possibly fix faults occurred due to XML rewriting attacks.

• International Journal of Safety
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• v.11 no.2
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• pp.22-25
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• 2012

This paper deals with the problem of fault diagnosis for identifying a single fault when the number of assumed faults is larger than that of predictive variables. Principal component analysis (PCA) is employed to isolate and identify a single fault. PCA is a method to extract important information as reducing the number of large dimension in a process. The patterns of all assumed faults can be recognized by PCA and these can be employed whether a new fault is one of predefined faults or not. Through PCA, empirical models for analyzing patterns can be trained. When a single fault occurs, the pattern generated by PCA can be obtained and this is used to identify a fault. The performance of the proposed approach is illustrated in the actuator benchmark problem.