• Journal of the Korean Society for Precision Engineering
• /
• v.20 no.4
• /
• pp.136-142
• /
• 2003

The morphological analysis of wear debris can provide early a failure diagnosis in lubricated moving system. It can be effective to analyze operating conditions of oil-lubricated tribological system with shape characteristics of wear debris in a lubricant. But, in order to predict and recognize an operating condition of lubricated machine, it is needed to analyze and to identify shape characteristics of wear debris. Therefore, If the morphological characteristics of wear debris are recognized by computer image analysis using the neural network algorithm, it is possible to recognize operating condition of hydraulic driving members. In this study, wear debris in the lubricating oil are extracted by membrane filter (0.45 ${\mu}{\textrm}{m}$), and the quantitative values of shape parameters of wear debris are calculated by the digital image processing. This shape parameters are studied and identified by the artificial neural network algorithm. The result of study could be applied to prediction and to recognition of the operating condition of hydraulic driving members in lubricated machine systems.

• KSTLE International Journal
• /
• v.3 no.1
• /
• pp.54-59
• /
• 2002

It can be effective on failure diagnosis of oil-lubricated tribological system to analyze operating conditions with morphological characteristics of wear debris in a lubricated machine. And it can be recognized that results are processed threshold images of wear debris. But it is needed to analyse and identify a morphology of wear debris in order to predict and estimate a operating condition of the lubricated machine. If the morphological characteristics of wear debris are identified by the computer image analysis and the neural network, it is possible to recognize the friction condition. In this study, wear debris in the lubricating oil are extracted from membrane filter (0.45 ${\mu}m$) and the quantitative value fur shape parameters of wear debris was calculated through the computer image processing. Four shape parameters were investigated and friction condition was recognized very well by the neural network.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 1997.10a
• /
• pp.135-144
• /
• 1997

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surfaces from which the particles originated. The morphologies of the wear particles are therefore directly indica- rive of wear processes occuring in machinery and their severity. The neural network was applied to identify wear debris generated from the lubricated moving system. The four parameter(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 parameter learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We dicuss how the network determines difference in wear debris feature, and this approach can be applied to condition diagnosis of the lubricated moving system.

• International Journal of Precision Engineering and Manufacturing
• /
• v.4 no.6
• /
• pp.44-49
• /
• 2003

The morphological analysis of wear debris can provide early a failure diagnosis in lubricated moving system. It can be effective to analyze operating conditions of oil-lubricated tribological system with shape characteristics of wear debris in a lubricant. But, in order to predict and recognize an operating condition of lubricated machine, it is needed to analyze and to identify shape characteristics of wear debris. Therefore, If the morphological characteristics of wear debris are recognized by computer image analysis using the neural network algorithm, it is possible to recognize operating condition of hydraulic driving members. In this study, wear debris in the lubricating oil are extracted by membrane filter (0.45$\mu\textrm{m}$), and the quantitative values of shape parameters of wear debris are calculated by the digital image processing. This shape parameters are studied and identified by the artificial neural network algorithm. The result of study could be applied to prediction and to recognition of the operating condition of hydraulic driving members in lubricated machine systems.

• Transactions of the Korean Society of Machine Tool Engineers
• /
• v.12 no.5
• /
• pp.53-58
• /
• 2003

Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. 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 pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.

• Journal of the Korean Society for Precision Engineering
• /
• v.19 no.9
• /
• pp.111-117
• /
• 2002

In most machining companies, operators decide the cutting condition, a pair of spindle speed (5) and table federate (F) by experience and subjective judgment. As cutting conditions are determined by operators' experience and ability, inconsistent cutting conditions are given in same operating conditions. The objective of this study is to develop the cutting condition decision system which utilizes shop data and predicts tool life by neural network and eventually leads to the optimal cutting condition. The production time per piece is considered for an optimization object. We will discuss the process of an optimal cutting condition decision by neural network. By this process, a series of shop data is stored. And neural network is constructed for prediction of tool life and the optimal cutting condition is recommended from a cutting condition decision system using the stored shop data. The results show that the developed system is rational in searching the optimal cutting conditions on job operations.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2006.05a
• /
• pp.309-314
• /
• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is applied to IPMSM drive system controlled adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.28 no.1
• /
• pp.175-183
• /
• 2022

The collision between a ship and bridge across a waterway may result in extremely serious consequences that may endanger the safety of life and property. Therefore, factors affecting ship bridge collision must be investigated, and the impact force should be discussed based on various collision conditions. In this study, a finite element model of ship bridge collision is established, and the peak impact force of a ship bridge collision based on 50 operating conditions combined with three input parameters, i.e., ship loading condition, ship speed, and ship bridge collision angle, is calculated via numerical simulation. Using neural network models trained with the numerical simulation results, the prediction model of the peak impact force of ship bridge collision involving an extremely short calculation time on the order of milliseconds is established. The neural network models used in this study are the basic backpropagation neural network model and Elman neural network model, which can manage temporal information. The accuracy of the neural network models is verified using 10 test samples based on the operating conditions. Results of a verification test show that the Elman neural network model performs better than the backpropagation neural network model, with a mean relative error of 4.566% and relative errors of less than 5% in 8 among 10 test cases. The trained neural network can yield a reliable ship bridge collision force instantaneously only when the required parameters are specified and a nonlinear finite element solution process is not required. The proposed model can be used to predict whether a catastrophic collision will occur during ship navigation, and thus hence the safety of crew operating the ship.

• Journal of Mechanical Science and Technology
• /
• v.20 no.5
• /
• pp.583-590
• /
• 2006

It is hard to inspect the state of lubrication of an automobile transmission visually. Thus, it is necessary to develop a new inspection method. Wear debris can be collected from the lubricants of an operating transmission of an automobile, and its morphology will be directly related to the friction condition of the interacting materials from which the wear debris originated in the lubricated transmission. In this study, wear debris in lubricating oil are extracted by membrane filter $(0.45{\mu}m)$, and the quantitative values of shape parameters of wear debris are calculated by digital image processing. These shape parameters are studied and identified by an artificial neural network algorithm. The results of the study may be applicable to the prediction and diagnosis of the operating condition of transmission gear.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
• /
• 2004.11a
• /
• pp.312-315
• /
• 2004

A lubricated state of an automobile transmission can not be inspected directly with eyes. Thus, it needs to develop a more general method. Wear debris can be collected from the lubricants of operating transmission of an automobile and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated transmission. In this paper, to identify the friction condition for transmission gear by neural network, the wear test of ball-on-disk type and the analysis of friction state were carried out for carburized SCM420 and nitrocarburized NT100 under different experimental conditions. The four shape parameters($50\%$ volumetric diameter, aspect, roundness and reflectivity) of wear debris were calculated by the image processing system. They were used as input values to identify the moving condition of transmission gear by the neural network.