• Journal of the Korean Society of Manufacturing Process Engineers
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• v.15 no.4
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• pp.23-29
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• 2016

Rotary machine is major equipment in industry. The rotary machine is applied for a machine tool, ship, vehicle, power plant, and so on. But a spindle fault increase product's expense and decrease quality of a workpiece in machine tool. A turbine in power plant is directly connected to human safety. National crisis could be happened by stopping of rotary machine in nuclear plant. Therefore, it is very important to know rotary machine condition in industry field. This study mentioned fault diagnosis algorithm with statistical parameter and empirical mode decomposition. Vibration locations can be found by analyze kurtosis of data from triaxial axis. Support vector of data determine threshold using hyperplane with fault location. Empirical mode decomposition is used to find fault caused by intrinsic mode. This paper suggested algorithm to find direction and causes from generated fault.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.27 no.2
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• pp.189-194
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• 2017

An compressor that is combined with the rotor and pneumatic technology has been researching for the performance of pressure. However, the control of operations, an accurate diagnosis and the maintenance of compressor system are limited though the simple structure of compressor and compression are advantaged to reduce the energy. In this paper, the characteristic of the compressor operating under the normal or abnormal condition is realized. and the efficient diagnosis method is proposed through feature based analysis. Also, by using the GA (genetic algorithm) and SVM (support vector machine) of machine learning, the performance of feature analysis is conducted. Different misalignment mode of learning data for compressor is evaluated using the fault simulator. Therefore, feature based analysis is conducted considering misalignment mode of the compressor and the possibility of a diagnosis of misalignment is evaluated.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.3243-3244
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• 2000

From ancient times, the diagnosis method of the oriental medicine has been performed by curing diseases by means of rectifying and adjusting the unbalance in the physiological function of the five viscera and the six bowels of a human body. Diseases have been diagnosed by the condition of blood circulation that cycles a human body through blood vessels by dint of the vitality of the heart. Based on such a systematic pulse diagnosis method, the article presents parameters that will be beneficial to clinical application on the basis of its analysis of the filtering for eliminating noises from pulse signals inputted from sensor group, the digital hardware dealing with signals necessary for recognition algorithm, and the structure of diagnosis algorithm and components of pulse waveform.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.534-539
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• 2007

In this paper, we present the development of an intelligent diagnosis system for detecting faults of the low voltage wires. The wire detecting system based on the Time-Frequency Domain Reflectometry (TFDR) algorithm shows the condition of the wires. We analyze the reflected signal which is sent from the wire detecting system and classify the fault type of the wires by using the intelligent diagnosis system. Through the TFDR, generally, the conditions of the wires are classified into the three types - damage, open and short. In order to classify the fault type efficiently, we use the fuzzy classifier which is represented as IF-THEN rules. Finally, we show the utility of the proposed algorithm by performing the simulation which is based on the data of the coaxial cable.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.662-668
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• 2005

This paper presents a realization of check valve condition monitoring system based on fault diagnosis algorithm and Fieldbus communication. We first acquired AE(acoustic emission) sensor data at the check valve test loop, extract fault features through the teamed neural network, and send the processed data to a remote site. The overall system has been implemented and experimented results are given to show its effectiveness.

• Journal of the Korean Institute of Gas
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• v.28 no.2
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• pp.93-104
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• 2024

Facility diagnosis is essential for the smooth operation and life extension of rotating equipment used in industrial sites. Compared to other diagnostic methods, vibration diagnosis can find most of the initial defects, such as unbalance, alignment failure, bearing defects and resonance, compared to other diagnostic methods. Therefore, vibration analysis is the most commonly used facility diagnosis method in industrial sites, and is usefully used as a predictive preservation (PdM) technology to manage the condition of the facility. However, since the vibration diagnosis method is performed based on experience based on the standard, it is carried out by experts. Therefore, it is intended to contribute to the reliability of the facility by establishing a system that anyone can easily judge defects by establishing a vibration diagnosis method performed based on experience as a knowledgeable code system. An algorithm was developed based on the ISO-20816 standard for vibration measurement, and the reliability was verified by comparing the results of vibration measurement at various demonstration sites such as petrochemical plant compressors, hydrogen charging stations, and industrial machinery with the results of analysis using a development system. The developed algorithm can contribute to predictive maintenance (PdM) technology that anyone can diagnose the condition of the rotating machine at industrial sites and identify defects early to replace parts at the exact time of replacement. Furthermore, it is expected that it will contribute to reducing maintenance costs and downtime due to the failure of rotating machines when applied to various industrial sites such as oil refining facilities, transportation, production facilities, and aviation facilities.

• Journal of the Korean Society of Propulsion Engineers
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• v.12 no.3
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• pp.60-67
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• 2008

In this study, the genetic algorithm has been used for the real-time defect diagnosis on the operation of the aircraft gas-turbine engine. The component elements of the gas-turbine engine for consideration of the performance deterioration consist of the compressor, the gas generation turbine and the power turbine. Compared to the on-design point, the teaming data has been increased 200 times in case off-design conditions for the altitude, the flight mach number and the fuel consumption. Therefore, enormous learning time has been required for the satisfied convergence. The optimal division has been proposed for learning time decrease as well as the high accuracy. As results, the RMS errors of the defect diagnosis using the genetic algorithm have been confirmed under 5 %.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.967-982
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• 2011

This paper presents a FTNCS (Fault-Tolerant Networked Control System) that can tolerate control surface failure and packet delay/loss in an UAV (Unmanned Aerial Vehicle). The proposed method utilizes the benefits of self-diagnosis by smart actuators along with the control allocation technique. A smart actuator is an intelligent actuation system combined with microprocessors to perform self-diagnosis and bi-directional communications. In the event of failure, the smart actuator provides the system supervisor with a set of actuator condition data. The system supervisor then compensate for the effect of faulty actuators by re-allocating redundant control surfaces based on the provided actuator condition data. In addition to the compensation of faulty actuators, the proposed FTNCS also includes an efficient algorithm to deal with network induced delay/packet loss. The proposed algorithm is based on a Lagrange polynomial interpolation method without any mathematical model of the system. Computer simulations with an UAV show that the proposed FTNCS can achieve a fast and accurate tracking performance even in the presence of actuator faults and network induced delays.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.2073-2074
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• 2011

In this study, an algorithm for diagnosis of dc motor with winding open fault is suggested. A dc motor used in this paper, is consisted of a permanent magnet field stator, double 16-turn series winding rotating armature with 12-slot, brush and 12-commutator, etc. A current signal of dc motor which has brushes and commutatorswas considered for fault diagnosis. By commutation, this current signal shows different wave form according to the fault condition of the motor. In this study, operation of the data was easily through simplification of the current signal by the signal processing. Computation method is presented reference value($C_{dv}$) for diagnosis of winding open fault and verified through experiments that can be diagnosed using the reference value($C_{dv}$).

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.813-823
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• 2013

On-line monitoring system is important for high voltage vacuum circuit breakers (HVCBs) in operation condition assessment and fault diagnosis. A distributed multilayer system with client/server architecture is developed on rated voltage 10kV HVCB with spring operating mechanism. It can collect data when HVCB switches, calculate the necessary parameters, show the operation conditions and provide abundant information for fault diagnosis. Ensemble empirical mode decomposition (EEMD) is used to detect the singular point which is regarded as the contact moment. This method has been applied to on-line monitoring system successfully and its satisfactory effect has been proved through experiments. SVM and FCM are both effective methods for fault diagnosis. A combinative algorithm is designed to judge the faults of HVCB's operating mechanism. The system's precision and stability are confirmed by field tests.