• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.82-87
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• 2000

This paper deals with monitoring of grinding wheel wear in grinding process. A monitoring system is developed in which a laser scanning micrometer is used to measure the circumferential shape as well as the axial shape of grinding wheel. The monitoring system is applied to grinding machines. The experiment results show that the monitoring system is useful not only for monitoring the amount of wear in grinding wheel but also for measuring the apparent diameter of the grinding wheel.

• International Journal of Precision Engineering and Manufacturing
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• v.2 no.1
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• pp.81-86
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• 2001

This paper deals with the monitoring of grinding wheel wear in surface grinding process. A monitoring system, which makes use of a laser scanning micrometer, is developed to measure the circumferential shape as well as the axial profile of grinding wheel. The monitoring system is applied to surface grinding processes. The experimental results show that the developed monitoring system is useful not only for monitoring the amount of wear in grinding wheel but also for evaluation the quality of ground surface and determining proper derssing time for the grinding wheel.

• Smart Structures and Systems
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• v.30 no.3
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• pp.303-315
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• 2022

The degradation of wheel tread may result in serious hazards in the railway operation system. Therefore, timely wheel defect diagnosis of in-service trains to avoid tragic events is of particular importance. The focus of this study is to develop a novel wheel defect detection approach based on the relevance vector machine (RVM) which enables online detection of potentially defective wheels with trackside monitoring data acquired under different running-speed conditions. With the dynamic strain responses collected by a trackside monitoring system, the cumulative Fourier amplitudes (CFA) characterizing the effect of individual wheels are extracted to formulate multiple probabilistic regression models (MPRMs) in terms of multi-kernel RVM, which accommodate both variables of vibration frequency and running speed. Compared with the general single-kernel RVM-based model, the proposed multi-kernel MPRM approach bears better local and global representation ability and generalization performance, which are prerequisite for reliable wheel defect detection by means of data acquired under different running-speed conditions. After formulating the MPRMs, we adopt a Bayesian null hypothesis indicator for wheel defect identification and quantification, and the proposed method is demonstrated by utilizing real-world monitoring data acquired by an FBG-based trackside monitoring system deployed on a high-speed trial railway. The results testify the validity of the proposed method for wheel defect detection under different running-speed conditions.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.613-616
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• 2000

This paper deals with monitoring of grinding wheel wear in surface grinding process. A laser scanning micrometer is used to measure the circumferential shape as well as the axial shape of grinding wheel. The monitoring system is applied to two kinds of grinding methods: plunge and traverse grinding. Through experiments, it is found that measurement of grinding wheel wear reveals information of roughness of ground surface and the adequate dressing time. In addition, monitoring of grinding wheel wear makes it possible to identify abnormal grinding conditions.

• Smart Structures and Systems
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• v.21 no.5
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• pp.687-694
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• 2018

The problem of wheel tread defects has become a major challenge for the health management of high-speed rail as a wheel defect with small radius deviation may suffice to give rise to severe damage on both the train bogie components and the track structure when a train runs at high speeds. It is thus highly desirable to detect the defects soon after their occurrences and then conduct wheel turning for the defective wheelsets. Online wheel condition monitoring using wheel impact load detector (WILD) can be an effective solution, since it can assess the wheel condition and detect potential defects during train passage. This study aims to develop an FBG-based track-side wheel condition monitoring method for the detection of wheel tread defects. The track-side sensing system uses two FBG strain gauge arrays mounted on the rail foot, measuring the dynamic strains of the paired rails excited by passing wheelsets. Each FBG array has a length of about 3 m, slightly longer than the wheel circumference to ensure a full coverage for the detection of any potential defect on the tread. A defect detection algorithm is developed for using the online-monitored rail responses to identify the potential wheel tread defects. This algorithm consists of three steps: 1) strain data pre-processing by using a data smoothing technique to remove the trends; 2) diagnosis of novel responses by outlier analysis for the normalized data; and 3) local defect identification by a refined analysis on the novel responses extracted in Step 2. To verify the proposed method, a field test was conducted using a test train incorporating defective wheels. The train ran at different speeds on an instrumented track with the purpose of wheel condition monitoring. By using the proposed method to process the monitoring data, all the defects were identified and the results agreed well with those from the static inspection of the wheelsets in the depot. A comparison is also drawn for the detection accuracy under different running speeds of the test train, and the results show that the proposed method can achieve a satisfactory accuracy in wheel defect detection when the train runs at a speed higher than 30 kph. Some minor defects with a depth of 0.05 mm~0.06 mm are also successfully detected.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.46-46
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• 2000

A quadruped monitoring robot is introduced. The robot has several features that poses arbitrary position thanks to a 4-wheel hive mechanism, transmits an image and command data via RF wireless communication, and moreover, the imaged date are transferred through a network communication. The robot plays a role in monitoring what is happening around the robot and covers wide range due to a moving camera operated by the 4-wheel mechanism. The robot system can be applied k versatile models based the distinguished techniques introduced in this paper

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.219-224
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• 1997

In surface grinding, the contact between the grinding wheel and the workpiece introduces heat and resistance, which restrict the self-dressing of the grits and result in burrs and cracks on the workpiece. Therefore, before or during th grinding wheel for more accurate performance. In order to determine the dressing time monitoring method of grinding wheel in surface grinding, a three-dimensional computer simulation of the grinding operation has been attempted based on the contact mechanism and surface-shaping system between the grinding wheel and the workpiece. The optimal dressing time is determined based on the amount of the grain wear and work surface roughness.

• Journal of the Korean Society of Industry Convergence
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• v.13 no.4
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• pp.205-209
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• 2010

Even the domestically small-and medium-sized companies progressed development in the monitoring equipment of inspecting run out in steel wheel aiming to supply and generalize through developing the monitoring equipment for securing quality technology and enhancing quality-test level. Run-Out value in vehicle wheel was measured with Computer & Servo system by using Low-Point marking machine control system, which is this development product. Low-Point value was operated and calculated. It marked by revolving wheel as much as the demanded measurement value based on 1ST harmony curve. Thus, the shipment of inferior product, which occurs in the measurement by the existing worker, could be blocked in advance. In the existing case, 60 sec. was required for inspecting 1 product. However, it came to bring about a rise in production volume through shortening inspection time to 8 seconds and improving workers' operating environment.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.1
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• pp.112-118
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• 1998

In surface grinding, the contact between the grinding wheel and workpiece introduce heat and resistance, which restrict the self-dressing of the grits and result in burrs and cracks on the workpiece. Therefore, before or during the grinding operation, it is necessary to self-dress the grinding wheel for more accurate performance. In order to determine the dressing time monitoring method of grinding wheel in surface grinding, a three-dimensional computer simulation of the grinding operation has been attempted based on the contact mechanism and surface-shaping system between the grinding wheel and the workpiece. The optimal dressing time is determined based on the grain wear and work surface roughness.

• Journal of the Korean Society of Safety
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• v.31 no.3
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• pp.28-33
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• 2016

Since defects in the wheels of railway vehicles, which occur due to wears with the rail, cause serious damage to the running device, the diagnostic monitoring system for condition-based maintenance is required to secure the driving safety. In this paper, we studied to apply a useful Cepstrum analysis to detect periodic structure in spectrum among the vibration signal processing techniques for the fault diagnosis of a rotating body such as wheel. In order to analyze in variations of train velocity, the Cepstrum analysis was performed after a domain change of the vibration signal from time domain to rotation angle domain. When domains change, it is important to use a interpolation for a uniform interval of the rotation angle. Finally, the Cepstrum analysis for wheel flat detection was verified by using the vibration signal including the disturbance resulting from the rail irregularities and the vibration of bogie components.