• 유체기계공업학회:학술대회논문집
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• 2006.08a
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• pp.57-60
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• 2006

The purpose of this study is to explain the importance of Vibration Monitoring Device by introducing an example of Predictive Maintenance System using Condition Monitoring System of Hydro-turbine generator. Confirming vibration of generation equipment is commissioning procedure during equipment completion for checking guaranteed items. Data from Generator output range are used to determine output band to continue the performance of equipment. The Vibration Monitoring System is not absolute method of maintenance, but if it is used well with expert, it will be visible, data-analyzed, scientific maintenance more than others. And also, Condition Monitoring System is very important for remote controlled small hydro-power plant although most of it is installed in Large hydro-power plant.

• The KSFM Journal of Fluid Machinery
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• v.13 no.1
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• pp.29-34
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• 2010

The purpose of this study is to explain the importance of Vibration Monitoring Device by introducing an example of Predictive Maintenance System using Condition Monitoring System of Hydro-turbine generator. Confirming vibration of generation equipment is commissioning procedure during equipment completion for checking guaranteed items. Data from Generator output range are used to determine output band to continue the performance of equipment. The Vibration Monitoring System is not absolute method of maintenance, but if it is used well with expert, it will be visible, data-analyzed, scientific maintenance more than others. And also, Condition Monitoring System is very important for remote controlled small hydro-power plant although most of it is installed in Large hydro-power plant.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1996.05a
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• pp.53-59
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• 1996

The new trends in main spindle design of Machining Center are focused on high-speed, high-precision and high-stiffness. As a main spindle bearing, the angular-contact ball bearing is well used. A rolling bearing is usually only a small part of a larger mechanical system, but its performance can have a great influence on the functioning of the whole machine. This work is about fatigue life tester design and monitoring of defected rolling-element bearings. The major work is done via experiments and the vibration signal is analysised by means of frequency spectrum technique. By analyzing the frequency spectrum, it is possible to view the condition of the bearings.

• Journal of Advanced Marine Engineering and Technology
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• v.33 no.5
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• pp.672-678
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• 2009

Acoustic Emission (AE) technique is a non-destructive testing method and widely used for the early detection of faults in rotating machines in these days, because the sensitivity of AE transducers is higher than normal accelerometers. So it can detect low energy vibration signals. The faults in the rotating machines are generally occurred at bearings and gearboxes which are the principal parts of the machines. It was studied to detect the bearing faults by envelop analysis in several decade years. And the researches showed that AE had a possibility of the application in condition monitoring system(CMS) using the envelope analysis for the rolling bearing. And peak ratio (PR) was developed for expression of the bearing condition in condition monitoring system using AE. Noise level is needed to reduce to take exact PR value because the PR is calculated from total root mean square (RMS) and the harmonics peak levels of the defect frequencies of the bearing. Therefore, in this paper, the discrete wavelet transform (DWT) was added in the envelope analysis to reduce the noise level in the AE signals. And then, the PR was calculated and compared with general envelope analysis result and the result of envelope analysis added the DWT. In the experiment result about inner fault of bearing, defect frequency was difficult to find about only envelop analysis. But it's easy to find defect frequency after wavelet transform. Therefore, Envelop analysis added wavelet transform was useful method for early detection of default in signal process.

• Journal of the Korean Society for Nondestructive Testing
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• v.35 no.3
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• pp.185-192
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• 2015

Since real-time monitoring systems, such as early fault detection, have been very important, an infrared thermography technique was proposed as a new diagnosis method. This study focused on damage detection and temperature characteristic analysis of ball bearings using the non-destructive, infrared thermography method. In this paper, for the reliability assessment, infrared experimental data were compared with finite element analysis (FEA) results from ANSYS. In this investigation, the temperature characteristics of ball bearing were analyzed under various loading conditions. Finally, it was confirmed that the infrared thermography technique was useful for the real-time detection of damage to bearings.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.1955-1962
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• 2017

Induction motors are a workhorse for the industry. The condition monitoring and fault analysis are the main concern for the engineers. The bearing is one of the vital segment of the induction machine and the condition of the whole machine is decided based on the condition of the bearing. In the present paper, the vibration signal of the bearing has been used for the analysis. The first line of action is to perform a statistical analysis of the vibration signal which gives trends in signal. To get the location of a fault in the bearing the second action is to develop an index based on Wavelet Packet Transform node energy named as Bearing Damage Index (BDI). Further, Teager-Kaiser Energy Operator (TKEO) has been calculated from higher index value to get the envelope and finally Power Spectral Density (PSD) has been applied to identify the fault frequencies. A performance index has also been developed to compare the usefulness of the proposed method with other existing methods. The result shows that the strong amplitude of fault characteristics and its side bands help to decide the type of fault present in the recorded signal obtained from the bearing.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.110-123
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• 1994

The development of automatic production systems have required inteligent diagnostic and monitoring function to repair system failure and reduce production loss by the failure. In order to perform accurate functions of intelligent system, inferencing about total system failure and fault analysis due to each mechanical component failures are required. Also the solution about repair and maintenance can be suggested from these analysis results. As an essential component of mechanical system, a bearing system is investigated to define the failure behavior. The bearing failure is caused by lubricant system failure, metallurgical defficiency, mechanical condition(vibration, overloading, misalignment) and environmental effect. This study described roller bearing fault train due to stress variation and metallurgical defficiency from lubricant failure by using FTA.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2004.05a
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• pp.395-400
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• 2004

For the purpose of monitoring by ultrasonic test of the ball bearing conditions in rotating machinery, a system for their diagnosis was developed. ultrasonic technique is used to detect abnormal conditions in the bearing system. And various data such as frequency spectrum, energy and amplitude of ultrasonic signals, and ultrasonic parameters were acquired during experiments with the simulated ball bearing system. Based on the above results and practical application for power plant, algorithms and judgement criteria for diagnosis system was established. Bearing diagnosis system is composed of four parts as follows : sensing part for ultrasonic sensor and preamplifier, signal processing part for measuring frequency spectrum, energy and amplitude, interface part for connecting ultrasonic signal to PC using A/D converter, graphic display and software part for display of bearing condition and for managing of diagnosis program.

• Structural Engineering and Mechanics
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• v.86 no.6
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• pp.715-730
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• 2023

Broad studies have addressed the issue of structural element damage identification, however, rubber bearing, as a key component of load transmission between the superstructure and substructure, is essential to the operational safety of a bridge, which should be paid more attention to its health condition. However, regarding the limitations of the traditional bearing damage detection methods as well as few studies have been conducted on this topic, in this paper, inspired by the model updating-based structural damage identification, a two-stage bearing damage identification method has been proposed. In the first stage, we deduce a novel bearing damage localization indicator, called element relative MSE, to accurately determine the bearing damage location. In the second one, the prior knowledge of bearing damage localization is combined with sailfish optimization (SFO) to perform the bearing damage estimation. In order to validate the feasibility, a numerical example of a 5-span continuous beam is introduced, also the noise robustness has been investigated. Meanwhile, the effectiveness and engineering applicability are further verified based on an experimental simply supported beam and actual engineering of the I-40 Bridge. The obtained results are good, which indicate that the proposed method is not only suitable for simple structures but also can accurately locate the bearing damage site and identify its severity for complex structure. To summarize, the proposed method provides a good guideline for the issue of bridge bearing detection, which could be used to reduce the difficulty of the traditional bearing failure detection approach, further saving labor costs and economic expenses.

• 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.