• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.16 no.1 s.106
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• pp.27-33
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• 2006

Fault diagnosis and condition monitoring for rotating machines are important for efficiency and accident prevention. The process of fault diagnosis is to extract the feature of signals and to classify each state. Conventionally, fault diagnosis has been developed by combining signal processing techniques for spectral analysis and pattern recognition, however these methods are not able to diagnose correctly for certain rotating machines and some faulty phenomena. In this paper, we add a minimum detection error algorithm to the previous method to reduce detection error rate. Vibration signals of the induction motor are measured and divided into subband signals. Each subband signal is processed to obtain the RMS, standard deviation and the statistic data for constructing the feature extraction vectors. We make a study of the fault diagnosis system that the feature extraction vectors are applied to K-means clustering algorithm and minimum detection error algorithm.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.303-312
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• 2003

This paper suggests a fuzzy expert system for fault diagnosis of rotating machinery, based on modulated databases. In the proposed system, alarm and trip levels are set based on ISO, considering operating condition, machinery type and maintenance history. Input signals for diagnosis, such as sub- and super-harmonic components of vibration and mean value, are normalized from 0 to 1 under the threshold level and otherwise equal to one so that chronic faults slightly below the threshold level can be monitored. The database for diagnosis consists of two modules: the well-known Sohre's chart module and if-then type rules. The Sohre's chart is utilized for the most common problems of high-speed turbomachinery, while the rule-based module, which was collected from many papers and reports, is for diagnosing peculiar faults according to the type of machinery. To infer the results from two modules, a fuzzy operation of Yager sum was adopted. Using a simulator constructed in laboratory, experimental verification was performed for the cases of resonance and housing looseness which were intended. The experimental results show that the proposed fuzzy expert system has feasibility in practical diagnosis of rotating machinery.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1371-1374
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• 2007

A few intake stations have vibration problems caused by pipes. The vibration transffered from pipes excites building severely. Therefore, the crack is generated on building wall and people who work at intake station are damaged. In this paper, the vibration is measured and analysis is carried out for pipes at intake station in order to identify the usefulness and effectiveness of the solution proposed for pipe resonance avoidance. According to the result of analysis, bellows is reduced the vibration of pipes.

• Journal of Biosystems Engineering
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• v.32 no.3
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• pp.190-196
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• 2007

The purpose of this study is to develop a measurement system of cutter conditions for combine header diagnosis during rice harvesting. A load cell was installed at the locker-arm to measure load fluctuation and an acceleration senor was used to monitor vibration signal of cutter bar. The data were collected from a paddy field during harvesting. The tests were conducted with a normal cutter, a loosened cutter, a broken cutter, and a worn-out connecter pin at the field. The vibration signals converted by FFT (Fast Fourier Transformation), filtered, and normalized. The load data and peak values of vibration signals in four different frequency ranges were used to determine the cutting operation and the cutter conditions of combine. The multiple comparison tests showed that the load data and peak values of vibration signals were important to monitor the cutting operation and cutter conditions of combine header.

• Journal of Mechanical Science and Technology
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• v.20 no.7
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• pp.972-980
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• 2006

In general, to send out natural gas via a pipeline network across the nation in LNG terminal, high-pressure cryogenic pump supply highly compressed LNG to high-pressure vaporization facilities. The Number of cryogenic pumps determined the send-out amount in LNG receiving terminal. So it is main equipment at LNG production process and should be maintained on best conditions. In this paper, to find out the cause of high vibration at cryogenic pumps-motor system in LNG terminal, vibration spectrum analysis and motor current signature analysis have been performed together. Through the analysis, motor rotor bar problems are estimated by the vibration analysis and confirmed by the current analysis. So, it is demonstrated through the case study in this paper, how performing vibration analysis and current signature analysis together can reliable diagnosis rotor bar problems in pump-motor system.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.7
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• pp.679-686
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• 2013

The reduction unit is one of the most important components in railway cars, due to the transmission of torque from the motor to the wheels. Faulty reduction gears in high-speed trains result from excessive wear on the gear or damage to the gear. These types of gear defects have a significant effect on high-speed rail operation and safety; thus, a diagnosis system for the reduction unit is needed. Vibration diagnosis technology is one of the most effective diagnostics. In this paper, the vibration parameters of a reduction unit were evaluated during a driving-gear test and a full-vehicle test, using kurtosis and the crest factor. These tests were performed under normal operating conditions; a specimen tester was used to diagnose problems in defective gears.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.9
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• pp.814-822
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• 2013

For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

• Advances in Computational Design
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• v.8 no.3
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• pp.255-271
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• 2023

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.11
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• pp.1091-1100
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• 2004

This paper presents an approach for the monitoring and detection of faults in induction machine by using data fusion technique and Dempster-Shafer theory Features are extracted from motor stator current and vibration signals. Neural network is trained and Hosted by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electric and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real time application.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.107-112
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• 2012

All chemical processes are centered in the chemical reactor. Chemical reactions are often accompanied by mixing. Mixing can influence not just reaction rate but also product distribution if more than one product is possible. So, reactor agitator is more critical in industrial reactions. High vibration was occurred on reactor agitator which is critical equipment in HDPE plant. This paper describes analysis and diagnosis for agitator vibration throughout some tests. High vibration has been occurred by misalignment and resonance. Through the correction it could be stable.