• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.994-1002
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• 2013

This study suggests a generalized availability and safety evaluation model to evaluate the influences to the system's fault tolerant capabilities depending on automatic fault detection function such as the automatic periodic testings. The conventional evaluation model of automatic fault detection function deals only with the self diagnostics, and supposes that the fault detection coverage of self diagnostics is always constant. But all of the fault detection methods could be degraded. For example, the periodic surveillance test has the potential human errors or test equipment errors, the self diagnostics has the potential degradation of built-in logics, and the automatic periodic testing has the potential degradation of automatic test facilities. The suggested evaluation models have incorporated the loss or erroneous behaviors of the automatic fault detection methods. The availability and the safety of each module of the safety grade platform have been evaluated as they were applied the automatic periodic test methodology and the fault tolerant evaluation models. The availability and safety of the safety grade platform were improved when applied the automatic periodic testing. Especially the fault tolerant capability of the processor module with a weak self-diagnostics and the process parameter input modules were dramatically improved compared to the conventional cases. In addition, as a result of the safety evaluation of the digital reactor protection system, the system safety of the digital parts was improved about 4 times compared to the conventional cases.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.39 no.7
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• pp.665-671
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• 2015

The performance of fault diagnostics for a planetary gearbox depends on vibration modulation characteristics, which can vary with manufacturing & assembly tolerance, and load condition. In this paper, a fault diagnostics technique that considers vibration modulation characteristics is proposed for the effective fault detection of planetary gearboxes in wind turbines. For identifying the vibration modulation characteristics in practice, re-sampled vibration signals are processed with narrow band-pass filters. Thereafter, the optimal position of the vibration extraction window is identified for effective detection of faulty signals under the varying vibration modulation characteristics. The proposed diagnostics technique makes it possible to perform robust diagnostics of the planetary gearbox with regard to the changeable vibration modulation effect. For demonstrating the proposed fault diagnostics technique, a 2-kW WT testbed is designed with two DC motors and gearboxes. A faulty gear with partial tooth breakage is machined and assembled into the gearbox.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1463-1467
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• 2005

This paper proposes an analytical redundancy technique for fault diagnostics of the sensor in steer-by-wire system. We use incorporating vehicle dynamics modeling into the design of a diagnostic system for steer-by-wire system. The use of a model of vehicle dynamics improves the speed and accuracy of the diagnoses. The proposed fault diagnostics algorithm is based on parity-space methods to generate residuals. To reduce the effects of modeling uncertainty and dynamic transients, the residuals are subject to filtering. We construct diagnostic system consisting residual threshold for detection and isolator with using the directional residual vector.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.505-508
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• 2014

Transmission error (TE) is defined as "the angular difference between the ideal output shaft position and actual position". As TE is one of the major source of the noise and vibration of gears, it is originally studied with relation of the noise and vibration of the gears. However, recently, with the relation of mesh stiffness, TE has been studied for fault detection of spur gear sets. This paper presents a physics-based theory on fault diagnostics of a planetary gear using transmission error. After constructing the lumped parameter model using DAFUL, multi-body dynamics software, we developed a methodology to diagnose the faults of the planetary gear including phase calculation, signal processing. Using developed methodology, we could conclude that TE could be a good signal for fault diagnostics of a planetary gear.

• 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.

• Proceedings of the IEEK Conference
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• summer
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• pp.371-375
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• 2004

Fault diagnosis is one of the most studied problems in process engineering. Recently, great research interest has been devoted to approaches that use classification methods to detect faults. This paper presents an application of a newly developed classification method - support vector machines - for fault diagnosis in an industrial case. A real set of operation data of a motor pump was used to train and test the support vector machines. The experiment results show that the support vector machines give higher correct detection rate of faults in comparison to rule-based diagnostics. In addition, the studied method can work with fewer training instances, what is important for online diagnostics.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10b
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• pp.1705-1711
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• 1991

On-line fault detection and diagnosis has an increasing interest in a chemical process industry, especially for a process control and automation. The chemical process needs an intelligent operation-aided workstation which can do such tasks as process monitoring, fault detection, fault diagnosis and action guidance in semiautomatic mode. These tasks can increase the performance of a process operation and give merits in economics, safety and reliability. Aiming these tasks, series of researches have been done in our lab. Main results from these researches are building appropriate knowledge representation models and a diagnosis mechanism for fault detection and diagnosis in a chemical process. The knowledge representation schemes developed in our previous research, the symptom tree model and the fault-consequence digraph, showed the effectiveness and the usefulness in a real-time application, of the process diagnosis, especially in large and complex plants. However in our previous approach, the diagnosis speed is its demerit in spite of its merits of high resolution, mainly due to using two knowledge models complementarily. In our current study, new knowledge representation scheme is developed which integrates the previous two knowledge models, the symptom tree and the fault-consequence digraph, into one. This new model is constructed using a material balance, energy balance, momentum balance and equipment constraints. Controller related constraints are included in this new model, which possesses merits of the two previous models. This new integrated model will be tested and verified by the real-time application in a BTX process or a crude unit process. The reliability and flexibility will be greatly enhanced compared to the previous model in spite of the low diagnosis speed. Nexpert Object for the expert system shell and SUN4 workstation for the hardware platform are used. TCP/IP for a communication protocol and interfacing to a dynamic simulator, SPEEDUP, for a dynamic data generation are being studied.

• 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.

• Journal of Power Electronics
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• v.8 no.1
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• pp.101-107
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• 2008

In this paper, the features extracted from vibration time signals are used to detect the bearing fault condition. The decision tree is applied to diagnose the bearing status, which has the benefits of being an expert system that is based on knowledge history and is simple to understand. This paper also suggests a genetic algorithm (GA) as a method to reduce the number of features. In order to show the potentials of this method in both aspects of accuracy and simplicity, the reduced-feature decision tree is compared with the non reduced-feature decision tree and the PCA-based decision tree.