• 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 Information Processing Systems
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• v.16 no.3
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• pp.530-540
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• 2020

In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis of complex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) is proposed. By optimizing the low confidence particles to replace the resampling process, this paper improved the problem of sample impoverishment caused by the sample updating based on risk and cost of CRPF algorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtaining samples. Then, we study the correlation between the current observation value and the prior state. By adjusting the density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noises can be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulation analysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improved obviously under the background of complex noise.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.87-91
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• 2019

The diagnosis of the failure for the existing electrical facilities was based on regular preventive maintenance, but this preventive maintenance was limited in preventing a lot of cost loss and sudden system failure. To overcome these shortcomings, fault prediction and diagnostic techniques are critical to increasing system reliability by monitoring electrical installations in real time and detecting abnormal conditions in the facility early. As the performance and quality deterioration problem occurs frequently due to the increase in the number of users of the motor pump, the purpose is to build an intelligent control system that can control the motor pump to maximize the performance and to improve the quality and reliability. To this end, a vibration sensor, temperature sensor, pressure sensor, and low water level sensor are used to detect vibrations, temperatures, pressures, and low water levels that can occur in the motor pump, and to build a system that can identify and diagnose information to users in real time.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.49-53
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• 2008

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.