• Tribology and Lubricants
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• v.36 no.6
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• pp.307-314
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• 2020

In this paper, studies on application cases of machine condition monitoring using oil sensors are reviewed. Owing to rapid industrial advancements, maintenance strategies play a crucial role in reducing the cost of downtime and improving system reliability. Consequently, machine condition monitoring plays an important role in maintaining operation stability and extending the period of usage for various machines. Machine condition monitoring through oil analysis is an effective method for assessing a machine's condition and providing early warnings regarding a machine's breakdown or failure. Among the three prevalent methods, the online analysis method is predominantly employed because this method incorporates oil sensors in real-time and has several advantages (such as prevention of human errors). Wear debris sensors are widely employed for implementing machine condition monitoring through oil sensors. Furthermore, various types of oil sensors are used in different machines and systems. Integrated oil sensors that can measure various oil attributes by incorporating a single sensor are becoming popular. By monitoring wear debris, machine condition monitoring using oil sensors is implemented for engines, automotive transmission, tanks, armored vehicles, and construction equipment. Additionally, such monitoring systems are incorporated in aircrafts such as passenger airplanes, fighter airplanes, and helicopters. Such monitoring systems are also employed in chemical plants and power plants for managing overall safety. Furthermore, widespread application of oil condition diagnosis requires the development of diagnostic programs.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.1541-1542
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• 2013

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2003.11a
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• pp.307-312
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• 2003

The average frictional coefficient was used generally to analyze the moving state of lubricated machine. But It is difficult of getting the correct friction coefficient because the average frictional coefficient of it is progressed always unstably with a large amplitute on driving condition. If correct analysis of frictional coefficient on working condition for lubricated machine can be possible, it can be effect on diagnosis of lubricated machine. The purpose of this study is carried out to get the working condition with a minimum frictional coefficient of transmission gear materials using statistical techniques.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.53-59
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• 2015

Noise from the surrounding environment, building structures and machine equipment have significant effects on daily life. Many solutions to this problem have been suggested by analyzing causes of noise generated from particular locations in general buildings or machine equipment and detecting defects of buildings or equipment. Therefore, this paper suggests a visualization technique of sounds by using the microphone array to measure sound sources from machines and perform the visual machine condition monitoring (VMCM). By analyzing sound signals and presenting effective sound visualization methods, it can be applied to identify machine's conditions and correct errors through real-time monitoring and visualization of noise generated from the plant machine equipment.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.5
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• pp.92-97
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• 1998

The morpholgies of the wear particles are directly indicative of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the machine driving system. The four parameters(50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction condition of five values(material 3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different patter characteristic and recognized the friction condition and materials very well by artificial neural network. We discussed how the network determines differencee in wear debris feature, and this approach can be applied to foreseeability and decisio for moving condition of the Machine driving system.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.10 no.5
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• pp.39-44
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• 2001

In this paper, drill fred mechanism, cutting depth measuring device and sensing buzzer of drill contact were investigated in order to develop the micro-hole drilling machine. Also, measuring device of cutting resistance was developed in order to estimate cutting resistance from change of cutting condition. The results show that extremely-low fled rate(less then $17{\mu}m/S$${\mu}{\textrm}{m}$ /s) can be done and cutting depth can be measured by up to 1${\mu}{\textrm}{m}$ with developed drilling machine. Accordingly we could assemble a very cheap micro-hole drilling machine($\phi$ 0.05~0.5 mm). Also we got the some properties of cutting performance i.e. under the same condition, cutting torque decreases as increase of spindle speed and rapid fled of drill brings about the inferior cutting state under low spindle speed.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.27 no.4
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• pp.157-164
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• 2004

Trouble prevention of facilities in operation process plays an important role for improving facilities productivity as production systemization is installed with development of facilities automatization. Condition monitoring predicts machine's internal changes by periodically recording vibration occurred in the machine. This article considers a method of establishing statistical criteria for facilities troubles by utilizing machine condition evaluation and operation limit standards of ISO 10816-3.

• Journal of KSNVE
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• v.6 no.3
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• pp.325-332
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• 1996

One of the greatest shortcoming in today's predictive maintenance program is the ability to diagnose the mechanical and electrical problems within the machine when the vibration exceeds preset overall and spectral alarm levels. In this study, auto-diagnosis system is constructed by using A/D converter to convert analog to digital singal. With this device the system analyses input signal to diagonosis machine condition. Many plots, which display machine condition, and input values of every channel are calculated in this system. If the falut is found, the system diagnoses automatically using fuzzy algorithm and trend monitoring. Prediction is also performed by the grey system theory. Operator finds out eh machine operating condition intuitively based on with personal computer CRT in using this system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.2 no.1
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• pp.31-35
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• 2001

This study is object to structural analysis of running machine frame. The finite element model was developed to compute the stress. strain and natural frequency for running machine frame. The FEM code was used for this analysis. In other to structural analysis of running machine frame, many variables such as load condition, boundary condition and weight condition were considered.

• 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.