• Journal of KSNVE
• /
• v.8 no.5
• /
• pp.929-935
• /
• 1998

Safety diagnosis of electric train driving system is performed using vibration signals of running electric train. Safety diagnosis is tried on the viewpoints of the appreciation of superannuation and the fault diagnosis of motor, reduction gear and bogie. The appreciation of superannuation is checked by the vibration levels of driving parts and the fault diagnosis is done by analyzing the frequencies of the vibration signals which are measured directly from a running electric train. The results shows that the vibration levels of each parts increase as the train gets older and each parts have their own frequency patterns of the vibration. Vibration propagation path is also investigated using calculated the coherence value between bogie and driving system. As the results, it is known that vibration signal can be utilized successfully for the safety diagnosis of the driving part of electric train.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2000.06a
• /
• pp.1046-1050
• /
• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. If rotating machinery has fault, we can detect fault using vibration or noise. But, in diagnosing rotating machinery, the end user who doesn't have expert knowledge needs the help of vibration diagnosis expert. However, vibration diagnosis experts who well satisfy the demand of end user are rare. So, this paper propose a development of the case-based reasoning system for abnormal vibration diagnosis of rotating machinery we construct the past experiences of vibration diagnosis expert into case base and shear the experiences of diagnosis expert with the end user. In this paper, we describe that process of structured system and adapting result of abnormal vibration diagnosis of electric motor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 1997.10a
• /
• pp.210-215
• /
• 1997

Safety diagnosis on the driving system of electric train is performed using the vibration and noise signals of running railway train. Safety diagnosis is tried on the viewpoints of the appreciation of superannuation and the fault diagnosis of motor, reduction gear and boggie. The appreciation of superannuation is checked by the rms vibration levels of driving parts and the fault diagnosis is done by analyzing the frequencies of the vibration signals. The methods of measuring and analyzing the signals are decided on the basis of field 1-measured signals. The results shows that the vibration levels of each parts increase as the train goes older and each parts have their own frequency patterns of the vibration. As the results, the vibration and noise can be utilized successfully for the safety diagnosis of the driving part of electric train.

• 제어로봇시스템학회:학술대회논문집
• /
• 1997.10a
• /
• pp.852-855
• /
• 1997

In this paper, the vibration diagnosis system of a rotating machinery is introduced, in which the vibration signals of multiple accelerometers and displacement sensors are used combinedly as input parameters and their characteristics of the vibration response and mutual relationships between each sensor signal are considered to improve the reliability of the diagnosis system. The fuzzy logic is utilized for inferencing the fault from the vibration signal patterns.

• Transactions of the Korean Society for Noise and Vibration Engineering
• /
• v.16 no.5 s.110
• /
• pp.495-502
• /
• 2006

A fault diagnosis method is developed in this study using automaton through quantization of vibration signals for normal and faulty conditions, respectively. Automaton is a kind of qualitative model which describes the system behaviour at the level of abstraction. The system behavior was extracted from the probability of the output sequence of vibration signals. The sequence was made as vibration levels by reconstructing the originally measured vibration signals. As an example, a fault diagnosis for the bearing of ATM machine was done, which detected the bearing fault with confident level compared to any other existing methods of kurtosis or spectrum analysis.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2012.10a
• /
• pp.169-174
• /
• 2012

Reduction unit is one of the most important components for railway vehicle because torque of motor must be transmitted to wheels of vehicle by reduction unit. However, According to advanced studies, it has been often broke down due to the damage, fatigue and wear of gear. To solve this problem, defect diagnosis methods of gear have been mainly using the vibration diagnosis technology through vibration waveform and frequency analysis. However, We should know vibration characteristics of normal state reduction unit prior to defect diagnosis. So in this paper, We had analyzed vibration characteristics of reduction unit in order to utilize monitoring system development. Comparison of targets is the vibration characteristics of normal state reduction unit about Electric Multiple Unit(EMU) and the High-speed trains(KTX, KTX II).

• Journal of Advanced Marine Engineering and Technology
• /
• v.32 no.1
• /
• pp.42-49
• /
• 2008

Most of vessels are not evaluated for their vibration and noise effects to human body. The vibration and noise generated by engine and auxiliary machine in vessel is a negative element for seamen. Therefore, in this paper, the diagnosis and evaluation of vibration and noise from vessel is accomplished by a shipbuilding corporation. The vibration and noise transferred from engine room and auxiliary machine was measured during the steady-state operation, and the vibration and noise map of vessel was made. Also, in order to evaluate the ship environment for human, the diagnosis is carried out on the base of measurement results.

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

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2005.11a
• /
• pp.279-284
• /
• 2005

Knowledge based fault diagnosis has a limitation in determining the cause and scheme for the fault, because it detects faults from signal pattern only Therefore, model-based fault diagnosis is requested to determine the fault by analyzing output of the equipment from its dynamic model. This research shows a method how to devise the automaton of system as a model for normal and faulty condition through the reduction of handling data by quantization of vibration signals and the example which is concerning to the bearing of ATM. The developed model based fault diagnosis was applied to detect the faulty bearing of ATM, which results.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2001.11b
• /
• pp.1067-1072
• /
• 2001

In this study, the vibration diagnosis program for onboard machinery has been developed. The developed program includes signal monitoring module, system diagnosis module, and system modification module. The signal monitoring module is to monitor the vibration signal in time and frequency domains. And the system diagnosis module, which is developed by using Neural Network with error back propagation algorithm, can detect the abnormal symptom indicating the malfunction of the machinery onboard ships. The investigations of the developed system are presented through the experiment using Rotor Kit. Abnormal vibration signals are created by adding additional weight, manually misaligning the shaft, and loosening the bolts.