• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.84-89
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• 2014

Data-driven method for fault diagnostics system often use machine learning technique. To use such technique proper signal processing should be implemented such as time synchronous averaging (TSA) for ball bearing systems. However, for journal bearing diagnostics systems not much has been researched, and yet a proper signal processing method has not been studied. Therefore, in this research an optimal datum unit for a reliable journal bearing diagnostics system along with angular resampling process is being suggested. Before extracting time and frequency domain features, angular resampling is applied to each cycle of vibration data. As to preserve the characteristics of vibration signal, averaging method is replaced by finding the optimal datum unit which strengthens statistical characteristics of vibration signal. Then 20 features were extracted for various cases, and those features are being evaluated by two criteria, separability and classification accuracy.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.420-422
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• 1998

All Fault Detection and Diagnosis(FDD) methods utilize classification techniques. The objective of this study was to demonstrate the application of classification techniques to the problem of diagnosing faults in data generated by a variable-air-volume(VAV) air-handling unit(AHU) simulation model and to describe the characteristics of the techniques considered. Artificial neural network classifier and fuzzy clustering classifier were considered for fault diagnostics.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.19 no.5
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• pp.80-86
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• 2005

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is includes of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module processes the stator current, voltage, temperatures, vibration signal of the motor.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.8
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• pp.1456-1463
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• 2010

The safety critical systems in nuclear power plants should be designed to have a high level of fault tolerant capability because those systems are used for protection or mitigation of the postulated accidents of nuclear reactor. Due to increasing of the system complexity of the digital based system in nuclear fields, the reliability of the digital based systems without an auto-test or a self-diagnostic feature is generally lower than those of analog system. To overcome this problem, additional redundant architectures in each redundant channel and self-diagnostic features are commonly integrated into the digital safety systems. The self diagnostic function is a key factor for increasing fault tolerant capabilities in the digital based safety system. This paper presents an availability and safety evaluation model to analyze the effect to the system's fault tolerant capabilities depending on self-diagnostic features when the loss or erroneous behaviors of self-diagnostic function are expected to occur. The analysis result of the proposed model on the several modules of a safety platform shows that the improvement effect on unavailability of each module has generally become smaller than the result of usage of conventional models and the unavailability itself has changed significantly depending on the characteristics of failures or errors of self-diagnostic function.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.10
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• pp.799-807
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• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.285-292
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• 2016

By adding short-range wireless communication function such as Bluetooth and Wi-Fi to the last vehicle in conjunction with a smart phone, a modern automobile is becoming entertainment screen to determine a variety of information such as car location information, diagnosis information, etc. through the ECU vehicle electronic control unit. In this study, by utilizing short-range communications capability of the on-board diagnostic devices and smart phones in association with the on-board diagnostics, compatibility tests among a number of smart phone models, Bluetooth and NFC(Near Field Communication) were carried out and those results were analyzed. Furthermore, composition of on-board diagnostic device having Bluetooth and NFC interface function and the fault diagnosis function were implemented, and fault diagnosis debugging program was developed. In addition, fault diagnosis data of the vehicle via the OBD-II interface was extracted. Finally, the on-board diagnostics CAN Protocol implementation has been proposed, and the results of work was analyzed.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.706-709
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• 1996

In this paper, an algorithm for PLC(Programmable Logic Controller) fault diagnosis system is proposed and experimentation is conducted with a PLC and a virtual plant. Wrong output backward tracking algorithm is proposed in order to find the external faults of PLC. And query with keywords of the fault systems and specially designed test sequence programs are used. We lay emphasis on the backward tracking algorithm to diagnose the faults of PLC. It is shown experimentally that the proposed algorithm can find the faults which a typical self diagnostics in the-commercially available PLC cannot.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2006.05a
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• pp.218-221
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• 2006

High reliability and minimization of operating cost are important problems for both engine-manufacturer and user in operation of gas-turbine engine, for which various performance diagnostics including a fault identification have been a major issue nowadays. Performance estimation in the off-design conditions, however, encounters problems of large errors and of poor convergence because of much required data to be evaluated. In this study, a diagnostics code of engine performance has been developed by using GPA(Gas Path Analysis). Quantitative performance deterioration of the turbo-shaft engine for SUAV has been estimated with altitude variation and is compared with that obtained by GSP code.

• Journal of Power Electronics
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• v.18 no.5
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• pp.1567-1576
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• 2018

The reliability of grid-connected inverters can be improved by algorithms capable of diagnosing faults in LCL filters. A fault diagnostic method during inverter startup is proposed. The proposed method can accurately generate and monitor information on the peak value and the location of the peak frequency component of the step response of a damped LCL filter. To identify faults, the proposed method compares the evaluated response with the response of a healthy higher-order damped LCL filter. The frequency components in the filter voltage response are first analytically obtained in closed form, which yields the expected trends for the filter faults. In the converter controller, the frequency components in the filter voltage response are computed using an appropriately designed fast Fourier transform and compared with healthy LCL response parameters using a finite state machine, which is used to sequence the proposed startup diagnostics. The performance of the proposed method is validated by comparing analytical results with the simulation and experimental results for a three-phase grid-connected inverter with a damped LCL filter.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.29-37
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• 2016

Fast and accurate fault diagnosis of the position sensor is of great significance to ensure the reliability as well as sensor fault tolerant operation of the Switched Reluctance Wind Generator (SRWG). This paper presents a fault diagnostic scheme for a SRWG based on the residual between the estimated rotor position and the actual output of the position sensor. Extreme Learning Machine (ELM), which could build a nonlinear mapping among flux linkage, current and rotor position, is utilized to design an assembled estimator for the rotor position detection. The data for building the ELM based assembled position estimator is derived from the magnetization curves which are obtained from Finite Element Analysis (FEA) of an SRWG with the structure of 8 stator poles and 6 rotor poles. The effectiveness and accuracy of the proposed fault diagnosis method are verified by simulation at various operating conditions. The results provide a feasible theoretical and technical basis for the effective condition monitoring and predictive maintenance of SRWG.