• The Transactions of the Korean Institute of Power Electronics
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• v.20 no.4
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• pp.305-312
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• 2015

In this study, inter-turn short fault models of interior permanent magnet synchronous motors (IPMSM) are developed by adding saliency modeling to surface-mounted permanent magnet motor models. The saliency model is obtained using the deformed flux models based on both fault-winding flux information and inductance variations caused by cross-flux linkages that depend on the distribution of the same phase windings. By assuming the balanced three-phase current injection, we obtain the positive and negative sequence voltages and the fault current in the positive and the negative synchronous reference frames. The output torque model is developed by adding the magnet and the reluctance torque, which are derived from the developed models. To verify the proposed IPMSM model with an inter-turn short fault, finite element method-based simulation and experimental measurement results are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.6
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• pp.635-640
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• 2014

Detection and diagnosis of incipient bearing fault in an induction motor is important for the prevention of serious motor failure. This paper presents an analysis of the effect of a faulty bearing on the stator current of an induction motor. A bearing fault leads to torque oscillations which result in phase modulation of the stator current. Since the torque oscillations cause specific frequency components at the stator current spectrum to rise sharply, the bearing fault can be detected by checking out the faultrelated frequency. In this paper, a mathematical model of the load torque oscillation caused by a bearing fault is presented. The proposed model can be used to analyze the physical phenomenon of a bearing fault in an induction motor. In order to represent the bearing fault effect, the proposed model is combined with an existing model of vector-controlled induction motors. A set of simulation results demonstrate the effectiveness of the proposed model and represent that bearing fault detection using a stator current is useful for vector-controlled induction motors.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.12
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• pp.1090-1095
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• 2012

In this paper, we propose a model-based fault diagnosis method applied to vibration data. The fault detection is performed by comparing estimated parameters with normal parameters and deciding if the observed changes can be explained satisfactorily in terms of noise or undermodelling. The key feature of this method is that it accounts for the effects of noise and model mismatch. And we aslo design a classifier for the fault isolation by applying the multiclass SVM (Support Vector Machine) to the estimated parameters. The proposed fault detection and isolation methods are applied to an engine vibration data to show a good performance. The proposed fault detection method is compared with a signal-based fault detection method through a performance analysis.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1145-1153
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• 2011

EMB (Electro-Mechanical Brake) systems can provide improved braking and stability functions such as ABS, EBD, TCS, ESC, BA, ACC, etc. For the implementation of the EMB systems, reliable and robust fault detection algorithm is required. In this study, a model-based fault detection algorithm is designed based on the analytical redundancy method in order to monitor current and force sensor faults in EMB systems. A state-space model for the EMB is derived including faulty signals. The fault diagnosis algorithm is constructed using the analytical redundancy method. Observer is designed for the EMB and the fault detectability condition is examined based on the residual analysis. The performance of the proposed model-based fault detection algorithm is verified in simulations. The effectiveness of the proposed algorithm is demonstrated in various faulty cases.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1419-1422
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• 1997

This paper deals with the Appliation of robust fault detection problem in uncertain linear systems, having both model mismatch and noise. A robust fault detection method presented by Kwon et al.(1994) for SISO uncertain systems. Here we experimented this method to the diesel engine systems described by difference ARMA models. The model mismatch includes here linearization error as well as undermodeling. Comparisons are made with alternative fault detection method which do not account noise. The new method is shown to have good performance.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.4
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• pp.168-178
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• 2002

The CNC machine toots has two kinds of fault. One is the fault due to degraded parts and the other is the fault due to operation disability. The phenomena of degradation is predictable but the operational fault is unpredictable because it occurred without any warning. The major faults of CNC machine tool are operational faults which are charged over 70%. This paper describes the model of remote service and the intelligent fault diagnosis system to diagnosis operational faults of CNC machine tools. To generalize fault diagnosis, two diagnosis models such as SF(Switching Function) and SSF(Step Switching Function) are proposed. The SF is static model and SSF is dynamic model for expression of fault. The SF and SSF model can be generated using SFG(Switching Function Generator) which is developed in this research. The three major operational faults such as emergency stop error, cycle start disability and machine ready disability are applied to experiment of fault modeling. To remote service of faults fur CNC machine tool, the web server and client system based internet are proposed as the suitable environment. The developed two technologies are implemented with the internal function of open architecture controller. The implemental results for two technologies are presented to validate the proposed scheme.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.9
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• pp.814-822
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• 2013

For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1106-1116
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• 2011

LINS (Laser Inertial Navigation System) consists of RLG (Ring Laser Gyroscopes)/accelerometers and provides real-time navigation information to the target system. Therefore it is very important to make a decision in the real time whether LINS is in the normal operation or not. That is called a fault detection method. In this paper, we propose the fault detection method of LINS based on the overlapping model. We also show that the fault detection probability is increased through overlapping the hardware model and the software model. It is verified through the long-term operation and RAM (Reliability Availability Maintainability) analysis of LINS that the fault detection method proposed in this paper is able to detect about 97% of probable system failures.

• Journal of Electrical Engineering and Technology
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• v.12 no.2
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• pp.886-889
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• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

• Earthquakes and Structures
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• v.19 no.6
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• pp.445-457
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• 2020

An ultra-high voltage (UHV) transmission system has the advantages of low circuitry loss, high bulk capacity and long-distance transmission capabilities over conventional transmission systems, but it is easier for this system to cross fault rupture zones and become damaged during earthquakes. This paper experimentally and numerically investigates the seismic responses and collapse failure of a UHV transmission tower-line system crossing a fault. A 1:25 reduced-scale model is constructed and tested by using shaking tables to evaluate the influence of the forward-directivity and fling-step effects on the responses of suspension-type towers. Furthermore, the collapse failure tests of the system under specific cross-fault scenarios are carried out. The corresponding finite element (FE) model is established in ABAQUS software and verified based on the Tian-Ma-Qu material model. The results reveal that the seismic responses of the transmission system under the cross-fault scenario are larger than those under the near-fault scenario, and the permanent ground displacements in the fling-step ground motions tend to magnify the seismic responses of the fault-crossing transmission system. The critical collapse peak ground acceleration (PGA), failure mode and weak position determined by the model experiment and numerical simulation are in relatively good agreement. The sequential failure of the members in Segments 4 and 5 leads to the collapse of the entire model, whereas other segments basically remain in the intact state.