• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.271-274
• /
• 2005

For noise and vibration order-analysis on rotating machinery, it is compulsory to measure the RPM of the rotating part of the machine. Normally the RPM is measured using dedicated tacho-probes. In this paper we describe a new method that in real-time synthesizes a tacho signal from the measured noise or vibration signal thus eliminating the tacho probe. The method strengths and weaknesses are evaluated on practical signals.

• Journal of KSNVE
• /
• v.10 no.2
• /
• pp.331-337
• /
• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• The KSFM Journal of Fluid Machinery
• /
• v.15 no.6
• /
• pp.31-38
• /
• 2012

Rotor of rotating machinery is the highly damaged part. Fault of 7 different types was confirmed as the main causes of rotor damage from the pump failure history data in domestic and U.S. nuclear. For each fault types, simulation testing was performed and fault signals were collected form the sensors. To calculate the statistical parameters of time-domain & frequency-domain, measured signals were analyzed by using the discrete wavelet transform, fast fourier transform, statistical analysis. Total 84 parameters were obtained. And Effectiveness factor were used to evaluate the discrimination capacity of each parameter. From the effectiveness factor, RAW-P4/RAW-P7/WT2-NNL/WT2-EE/WT1-P1 showed high ranking. Finally, these parameters were selected as the feature parameters of intelligent fault diagnostics for rotor.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.12 no.10
• /
• pp.799-807
• /
• 2002

The vibration signal can give an indication of the condition of rotating machinery, highlighting potential faults such as unbalance, misalignment and bearing defects. The features in the vibration signal provide an important source of information for the faults diagnosis of rotating machinery. When additional training data become available after the initial training is completed, the conventional neural networks (NNs) must be retrained by applying total data including additional training data. This paper proposes the fault diagnostics algorithm using the ART-Kohonen network which does not destroy the initial training and can adapt additional training data that is suitable for the classification of machine condition. The results of the experiments confirm that the proposed algorithm performs better than other NNs as the self-organizing feature maps (SOFM) , learning vector quantization (LYQ) and radial basis function (RBF) NNs with respect to classification quality. The classification success rate for the ART-Kohonen network was 94 o/o and for the SOFM, LYQ and RBF network were 93 %, 93 % and 89 % respectively.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 1990.06a
• /
• pp.7-31
• /
• 1990

Tribologicsl diagnostics as the ensemble of means and methods of continuous monitoring of the state of friction characteristics of moving junctions is playing an ever important part in the development of friction, lubrication, and wear theory end practice. The scheme presenting the main areas of tribological diagnostics is given in Fig. I. This growing part of TD is determined by the general tendency of modern technology, expressed in an attempt to organically combine the functions of measuring, evaluating,and predicting the parameters and characteristics of the processee taking place in the operating device. The logical result of this integration in future is the closed system correcting its operation in accordance with sn established program. Unfortunately, tribotechnicsl devices are still very far from such an ideal system at the present time. While in the friction assemblies with hydrodynamic lubrication it is possible in the first approximation to realize feed-backs in the lubricant circulation system with the aid of monitoring of the pressure, temperature and filtration, in the systems operating without lubrication and with boundary lubricetion even the process of selection of the diagnostic parameters has not been completed.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.10a
• /
• pp.773-776
• /
• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.10a
• /
• pp.772-772
• /
• 2012

• Tribology and Lubricants
• /
• v.11 no.5
• /
• pp.15-19
• /
• 1995

Learning from the failure of mechanical systems is a necessity, given that it is the understanding of how and why things fail that generates effective redesign. This subsequently enables the technology that surrounds us to become more reliable, safer, and more economical by extending component life and minimizing the wasteful decisions made to replace systems that am either sound for continued operation could be easily repaired. Considerations for cost-effective decision making, so as to promote healthy machinery, equipment, and structures, are discussed in terms of learning from failure analysis, improving via reliability engineering, and achieving longevity through integrated diagnostics.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2014.10a
• /
• pp.97-98
• /
• 2014

Vibration signal has been widely utilized in the diagnostics of rotating mechanical system. Diagnostics systems in rotating machinery are depends on the vibration data which are acquired from the system. However, the characteristics of acquired data can be vary according to the position of anomaly installed or the position of data acquired. In this research, the coordinate transform of journal bearing vibration signal was proposed to overcome the limitation mentioned above. Journal bearing systems are equipped two gap sensors with ninety degree angles, and it can enable to generate coordinate transformed signals in arbitrary angle domain. More abundant information for each normal or anomaly conditions are obtained from coordinate transformation than only the data of the existing measuring position, then we have developed a reliable and robust diagnosis algorithm for journal bearing system.

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