• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.11a
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• pp.674-677
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• 2000

This paper describes a remote monitoring and control system using mobile internet environments through TCP/IP. The server program acts as the main interface between the machine system and the internet technology. The client program is to be distributed to the remote users such as PDA who want to monitor the machine status. A server system is designed to send information of the faults and status of application system to the client part. Therefore, The client parts can take measures and control the system remotely according to the error and faults of system. We developed the client- serversystem for remote control of a green house environment.

• Smart Structures and Systems
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• v.1 no.2
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• pp.121-140
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• 2005

This paper presents a systematic investigation of the effect of sensor location on the data quality and subsequently, on the effectiveness of machine health monitoring. Based on an analysis of the signal propagation process from the defect location to the sensor, numerical simulations using finite element modeling were conducted on a bearing test bed to determine the signal strength at several representative sensor locations. The results showed that placing sensors closely to the machine component being monitored is critical to achieving high signal-to-noise ratio, thus improving the data quality. Using millimeter-sized piezoceramic plates, the obtained results were evaluated experimentally. A comparison with a set of commercial vibration sensors verified the developed structural dynamics-based sensor placement strategy. It further demonstrated that the proposed shock wave-based sensing technique provided an effective alternative to vibration measurement, while requiring less space for sensor installation.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.2
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.22 no.12
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• pp.1227-1236
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• 2012

The low speed machinery faults are usually caused by the bearing failure of the rolling elements. As the life time of the bearing is limited, the condition monitoring of bearing is very important to maintain the continuous operation without failures. A few monitoring techniques using time domain, frequency domain and fuzzy neural network vibration analysis are introduced to detect and diagnose the faults of the low speed machinery. This paper presents a method of fault detection for the rolling element bearing in the low speed machinery using the Wiener filtering and shock pulse counting techniques. Wiener filter is used for noise cancellation and it clearly makes the shock pulse emerge from the time signal with the high level of noise. The shock pulse counting is used to determine the various faults obviously from the shock signal with transient pulses not related with the bearing fault. Machine fault simulator is used for the experimental measurement in order to verify this technique is the powerful tool for the low speed machine compared with the frequency analysis. The test results show that the method proposed is very effective parameter even for the signal with high contaminated noise, speed variation and very low energy. The presented method shows the optimal tool for the condition monitoring purpose to detect the various bearing fault with high accuracy.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.05a
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• pp.53-62
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• 2002

Condition monitoring plays a vital role since it sustains reliable operation of industrial plants and machinery in the pursuit of economic whole life operation. In order to achieve this goal, it is needed to monitor various parameters of mechanical system such as vibration, wear, temperature and etc., and finally to diagnosis the root causes of any possible abnormal machine condition. In this work, a machine failure caused by mechanical looseness was experimentally simulated and on-line measurement of the vibration, wear and temperature were simultaneously measured. For the quantitative analyses of machine wear, several statistical parameters of the wear particle size distribution were obtained through the center moment method of the Weibull distribution function, and they were compared to vibrational characteristics. Results showed that the wear and vibrational characteristics did not reveal a strong correlation each other in a loosely supported proceeding bearing.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.20 no.2
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• pp.185-190
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• 2010

Condition monitoring(CM) is a method based on non-destructive test(NDT). So, recently many kind of NDT were applied for CM. Acoustic emission(AE) is widely used for the early detection of faults in rotating machinery in these days because of high sensitivity than common accelerometers and detectable low energy vibration signals. And crack is considered one of severe fault in the rotating machine. Therefore, in this paper, study on early detection using AE has been accomplished for the crack of the low-speed shaft. There is a seeded initial crack on the shaft then the AE signal had been measured with low-speed rotation as the applied load condition. The signal detected from crack in rotating machine was detected by the AE transducer then the trend of crack growth had found out by using some of feature values such as peak value, skewness, kurtosis, crest factor, frequency center value(FC), variance frequency value(VF) and so on.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.85-89
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• 1996

This paper was undertaken to analyze the morphology of wear debris for operating condition evaluatio of lubricated machine surfaces. The lubricating wear test was carried out under different experimental conditions using tile wear test device was made in our laboritory and wear testing spcimen of the pin on disk type was rubbed in paraffine series base oil, by varying specimen, applied load, sliding distance. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe morphology of wear debris have been developed and are outlined in tile paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1996.04b
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• pp.60-67
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• 1996

This paper was undertaken to analyze the morphology of wear debris on operating condition of lubricated machine surfaces. The lubricating wear test was carried out under different experimental conditions using the wear test device was made in our laboritory and wear testing spcimen of the pin on disk type was rubbed in paraffine series base oil, by varying specimen, applied load, sliding distance. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe morphology of wear debris have been developed and are outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology for machine condition monitoring.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.394-399
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• 2012

In recent years, MEMS sensors show huge attraction in machine condition monitoring, which have advantages in power, size, cost, mobility and flexibility. They can integrate with smart sensors and MEMS sensors are batch product. So the prices are cheap. And the suitability of it for condition monitoring is researched by experimental study. This paper presents a comparative study and performance test of classification of MEMS sensors in target machine fault classification by 3 intelligent classifiers. We attempt to signal validation of MEMS sensor accuracy and reliability and performance comparisons of classifiers are conducted. MEMS accelerometer and MEMS current sensors are employed for experiment test. In addition, a simple feature extraction and cross validation methods were applied to make sure MEMS sensors availabilities. The result of application is good for using fault classification.

• Proceedings of the Korean Society of Marine Engineers Conference
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• 2012.06a
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• pp.119-120
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• 2012

In the various industry plant, it need a condition monitoring system for an active machine that used an detachable wireless sensors. In this paper, IEEE802.15.4 standard based detachable wireless sensor monitoring system configure results will be introduced.