• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.18 no.10
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• pp.791-799
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• 2006

The fault diagnosis technologies may be applied in order to decrease the energy consumption and the maintenance cost of an air-conditioning system. In this paper, a fault diagnosis algorithm was developed by using a neural no-fault model and a dual fuzzy logic. Five different faults, such as the compressor valve leakage, the liquid line blockage, the condenser fouling, the evaporator fouling, and the refrigerant leakage of an air-conditioning system, were considered. The fault diagnosis algorithm was tested by using a fault simulation facility. Test results showed that the algorithm developed for this study was effective to detect and diagnose various faults. Therefore, this algorithm may be practically used for the fault diagnosis of an air-conditioning system.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.1
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• pp.99-111
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• 2016

This paper presents an integrated fault diagnosis algorithm for driving motor of In-wheel independent drive electric vehicle. Especially, this paper proposes a method that integrated the high level fault diagnosis and the low level fault diagnosis in order to improve a robustness and performance of the fault diagnosis system. The high level fault diagnosis is performed using the vehicle dynamics analysis and the low level fault diagnosis is carried using the motor system analysis. The validity of the high level fault diagnosis algorithms was verified through $Carsim^{(R)}$ and MATLAB/$Simulink^{(R)}$ cosimulation and the low level fault diagnosis's validity was shown by applying it to a MATLAB/$Simulink^{(R)}$ interior permanent magnet synchronous motor control system. Finally, this paper presents a fault diagnosis strategy by combining the high level fault diagnosis and the low level fault diagnosis.

• Journal of Power Electronics
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• v.10 no.6
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• pp.702-708
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• 2010

In this paper, a fault diagnosis algorithm for brushless DC (BLDC) motor drives is proposed to maintain control performance under switch open-damage. The proposed fault diagnosis algorithm consists of a simple algorithm using measured phase current information and it detects open-circuit faults based on the operating characteristic of BLDC motors. The proposed algorithm quickly recovers control performance due to its short detection time and its reconfiguration of the system topology. It can be embedded into existing BLDC drive software as a subroutine without additional sensors. The feasibility of the proposed fault diagnosis algorithm is proven by simulation and experimental results.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.1
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• pp.216-221
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• 2004

This paper describes a fault detection and fault handling algorithm to be used in a longitudinal vehicle cruise control systems. The fault diagnosis system consists of two structures to generate proper residuals and to find that which component has a fault. A systematic design of the fault diagnosis system using model-based techniques is presented. A linear observer is used to create a set of signals sensitive to faults in a radar sensor. The fault handling system consists of two structures to compensate for faults and degraded system performance. Simulation results show that the algorithm is effective for a fault diagnosis and handling in a longitudinal vehicle cruise control system.

• Transactions of the Korean Society of Automotive Engineers
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• v.12 no.1
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• pp.215-215
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• 2004

This paper describes a fault detection and fault handling algorithm to be used in a longitudinal vehicle cruise control systems. The fault diagnosis system consists of two structures to generate proper residuals and to find that which component has a fault. A systematic design of the fault diagnosis system using model-based techniques is presented. A linear observer is used to create a set of signals sensitive to faults in a radar sensor. The fault handling system consists of two structures to compensate for faults and degraded system performance. Simulation results show that the algorithm is effective for a fault diagnosis and handling in a longitudinal vehicle cruise control system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.4
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• pp.192-200
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• 2001

As the high density memory is widely used in the various applications, the need for reproduction of memory is increased. In this paper we propose an efficient fault diagnosis algorithm of linear order O(n) that enables the reproduction of memory. The new algorithm can distinguish various fault models and identify all the cells related to the faults. In addition, a new BIST architecture for fault diagnosis is developed. Using the new algorithm, fault diagnosis can be performed efficiently. And the performance evaluation with previous approaches proves the efficiency of the new algorithm.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.4
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• pp.62-71
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• 2009

In this paper, we propose a fault diagnosis algorithm for BLDC motors by principle component analysis (PCA) and implement a real-time fault diagnosis system for BLDC motors. To verify the proposed diagnosis algorithm, various faulty data are acquired by Lab VIEW program from experimental system. We extract a fault feature using principle component analysis after preprocessing and then finally the fault diagnosis is performed by Euclidean similarity. Also, we embed the PCA algorithm and k-NN classification algorithm into a digital signal processor. From various experiments, we found that the proposed algorithm can be used as a powerful technique to classify the several fault signals acquired from BLDC motors.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.244-251
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• 1997

In this paper, we propose a fault diagnosis algorithm to detect and isolate multiple faults in a system. The proposed fault diagnosis algorithm is based on a multiple fault classifier which consists of two ART2 NN(adaptive resonance theory2 neural network) modules and the algorithm is composed of three main parts - parameter estimation, fault detection and isolation. When a change in the system occurs, estimated parameters go through a transition zone in which residuals between the system output and the estimated output cross the threshold, and in this zone, estimated parameters are transferred to the multiple faults classifier for fault isolation. From the computer simulation results, it is verified that when the proposed diagnosis algorithm is performed successfully, it detects and isolates faults in the position control system of a DC motor.

• ETRI Journal
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• v.30 no.4
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• pp.555-564
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• 2008

This paper proposes a test algorithm that can detect and diagnose all the faults occurring in dual-port memories that can be accessed simultaneously through two ports. In this paper, we develop a new diagnosis algorithm that classifies faults in detail when they are detected while the test process is being developed. The algorithm is particularly efficient because it uses information that can be obtained by test results as well as results using an additional diagnosis pattern. The algorithm can also diagnose various fault models for dual-port memories.

• Proceedings of the KIEE Conference
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• 2005.05a
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• pp.24-26
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• 2005

At present, KS-1000 which is one of a commercial measurement instrument for motor fault diagnosis has been used in industrial field. The measurement system of KS-1000 is composed of three part : harmonic acquisition, signal processing by KS-1000 algorithm, diagnosis for motor fault. First of all, voltage signal taken from harmonic sensor is analysed for frequency by KS-1000 algorithm. Then, based on the result values of analysis skilled expert makes a judgment about whether motor system is the abnormality or degradation state. But the expert system such a motor fault diagnosis is very difficult to bring the expectable results by mathematical modeling due to the complexity of judgment process. In this reason, we propose an automation system using fuzzy model based on genetic algorithm(GA) that builded a qualitative model of a system without priori knowledge about a system provided numerical input output data.