• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.420-424
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• 2008

The new MOV(motor-operated valve) diagnostic system MOVIDS(MOV intelligent diagnostic system) was developed. Its remote diagnostic methods with high accuracy have improved the applicability. This enables the diagnostic tests and the evaluations of many MOVs not only at the valves but also in remote places like MCCs(motor control centers). The remote diagnostic ability of the new system reduces the cost and the man power for diagnosing MOVs and eliminates the danger of the toxic or radioactive environments to which workers might be exposed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.416-419
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• 2008

In order to ensure the safety, the performance evaluation of the safety-related components in a nuclear power plant such as air-operated valves. In this paper, the diagnostic system(MOVIDS $A^+$) for the performance of air-operated valves was developed. For this purpose, the characteristics of their operation and the methods of the diagnostic tests were reviewed. The setup and diagnostic functions of the system were mentioned. Its applicability was validated through the diagnostic tests of air-operated valves in nuclear power plants. This diagnostic system is now applied in nuclear power plants for performance tests.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.9 s.102
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• pp.1108-1117
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• 2005

A robust damage detection method using short-time Fourier transform and beating phenomena is presented as an estimating tool of the healthiness of large structures. The present technique makes use of beating phenomena that manifest themselves when two signals of similar frequencies are added or subtracted. Unlike most existing methods based on vibration signals, the present approach does not require an analytic model for target structures. Furthermore, the main advantage of the proposed method compared to the competing diagnostic method using vibration data is its robustness. The proposed method is not affected by the amplitude of exciting signals and the location of exciting points. From a measuring view point. the location of sensing point have no influence on the performance of the present method. With a view to verifying the effectiveness of this method. a series of experiments are made and the results show its possibility as a robust damage diagnostic method.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2008.03a
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• pp.16-21
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• 2008

Aero-engine, as one kind of rotating machinery with complex structure and high rotating speed, has complicated vibration faults. Therefore, condition monitoring and fault diagnosis system is very important for airplane security. In this paper, a vibration data acquisition and intelligent fault diagnosis system is introduced. First, the vibration data acquisition part is described in detail. This part consists of hardware acquisition modules and software analysis modules which can realize real-time data acquisition and analysis, off-line data analysis, trend analysis, fault simulation and graphical result display. The acquisition vibration data are prepared for the following intelligent fault diagnosis. Secondly, two advanced artificial intelligent(AI) methods, mapping-based and rule-based, are discussed. One is artificial neural network(ANN) which is an ideal tool for aero-engine fault diagnosis and has strong ability to learn complex nonlinear functions. The other is data mining, another AI method, has advantages of discovering knowledge from massive data and automatically extracting diagnostic rules. Thirdly, lots of historical data are used for training the ANN and extracting rules by data mining. Then, real-time data are input into the trained ANN for mapping-based fault diagnosis. At the same time, extracted rules are revised by expert experience and used for rule-based fault diagnosis. From the results of the experiments, the conclusion is obvious that both the two AI methods are effective on aero-engine vibration fault diagnosis, while each of them has its individual quality. The whole system can be developed in local vibration monitoring and real-time fault diagnosis for aero-engine.

• Journal of Mechanical Science and Technology
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• v.18 no.9
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• pp.1537-1548
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• 2004

This paper presents a novel diagnostic technique for monitoring the system conditions and detecting failure modes and precursors based on wavelet-packet analysis of external noise/vibration measurements. The capability is based on extracting relevant features of noise/vibration data that best discriminate systems with different noise/vibration signatures by analyzing external measurements of noise/vibration in the time-frequency domain. By virtue of their localized nature both in time and frequency, the identified features help to reveal faults at the level of components in a mechanical system in addition to the existence of certain faults. A prima-facie case is made via application of the proposed approach to fault detection in scroll and rotary compressors, although the methods and algorithms are very general in nature. The proposed technique has successfully identified the existence of specific faults in the scroll and rotary compressors. In addition, its capability of tracking the severity of specific faults in the rotary compressors indicates that the technique has a potential to be used as a prognostic tool.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.6
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• pp.797-804
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• 2013

In this study, the authors develop a methodology for a diagnostic system with a vibration parameter that is influenced by environmental factors. The data tends to have a varying average over time. Often, these features are found in statistical data retrieved from a production line. If we utilize existing statistical techniques for these features, we could derive an incorrect diagnostic conclusion based on the different average values. To overcome the limitations of previous methods, the authors apply a function analyzed through regression analysis to predict the mean value and corresponding upper and lower limits at each stage. This technique also provides corresponding statistical parameters in varying dynamic means. To validate the proposed methods, we retrieve data from the engine assembly line of H Motors and verify the results.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.9 s.252
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• pp.1154-1159
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• 2006

In this paper, time-frequency analysis and multi-dimensional spectral analysis methods are applied to source identification and diagnostic of non-stationary sound vibration signals. By checking the coherences for concerned time, this simulation is very well coincident to expected results. The proposed method analyzes the signal instantaneously in both time and frequency domains. The MDSA (Multiple Dimensional Spectral Analysis) analyzes the signal in the plane of instantaneous time and instantaneous frequency at the same time. And it was verified by using the 1500cc passenger car which is accelerated from 70Hz to 95Hz in 4 seconds, the proposed method is effective in determining the vehicle diagnostic problems.

• Proceedings of the Korean Nuclear Society Conference
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• 1996.05a
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• pp.587-593
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• 1996

Vibration analysis is one of the most powerful tools available for the detection and isolation of incipient faults in mechanical systems. The methods of vibration analysis in use today and under continuous study are broad band vibration monitoring, time domain analysis, and frequency domain analysis. In recent years, great interest has been generated concerning the use of time-frequency representation and its application for a machinery diagnostics and condition monitoring system. The objective of the research described in this paper was to develop a new diagnostic tool for the rotating machinery. This paper introduces a new time-frequency representation, Directional Wigner-Ville Distribution, which analyses the time- frequency structure of the rotating machinery vibration.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.770-773
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• 2007

As higher reliability and accuracy on production facilities are required to detect incipient faults, a diagnostic system for predictive maintenance of the facility is highly recommended. In this paper, it presents a study on the application of vibration signals to diagnose faults for a Rotating Machinery using the Mahalanobis Distance-Taguchi System. RMS, Crest Factor and Kurtosis that is known as the Statistical Methods and the spectrum analysis are used to diagnose faults as parameters of Mahalanobis distance.