• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.452-461
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• 2008

Previous settlement prediction method based on settlement monitoring such as hyperbolic, monden method were developed under instantaneous loading condition and have restriction to be applied to soft ground under ramp loading condition. In this study, settlement prediction method under ramp loading was developed. New settlement prediction method under ramp loading considers influence factors of consolidation settlement and increase accuracy of settlement prediction using field monitoring data after ramp loading. Large consolidation tests for ideally controlled one dimensional consolidation under ramp loading condition were performed and the settlement behavior was predicted based on the monitoring data. As a result, new prediction method is expected to have great applicability and practicability for the prediction of settlement behavior.

• Tribology and Lubricants
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• v.18 no.2
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• pp.117-126
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• 2002

Abstract - An integrated condition monitoring algorithm based on an expert system was implemented in this work in order to monitor effectively the machine conditions. The knowledge base was consisted of numeric data which meant the posterior probability of each measurement parameter for the representative machine failures. Also the inference engine was constructed as a series of statistical process, where the probable machine fault was inferred by a mapping technology of pattern recognition. The proposed algorithm was, through the user interface, applied for an air compressor system where the temperature, vibration and wear properties were measured simultaneously. The result of the case study was found fairly satisfactory in the diagnosis of the machine condition since the predicted result was well correlated to the machine fault occurred.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.1
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• pp.77-85
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• 2008

The objective of the study is to construct a sensor fusion system for tool-condition monitoring (TCM) that will lead to a more efficient and economical drill usage. Drill-wear monitoring has an important attribute in the automatic machining processes as it can help preventing the damage of tools and workpieces, and optimizing the drill usage. In this study, we present the architectures of a multi-layer feed-forward neural network with Levenberg-Marquardt training algorithm based on sensor fusion for the monitoring of drill-wear condition. The input features to the neural networks were extracted from AE, vibration and current signals using the wavelet packet transform (WPT) analysis. Training and testing were performed at a moderate range of cutting conditions in the dry drilling of steel plates. The results show good performance in drill- wear monitoring by the proposed method of sensor fusion and neural network analysis.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.19 no.4
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• pp.393-399
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• 2009

Historically, industrial condition monitoring has been performed by costly hard-wired sensors or infrequent checks by maintenance personnel equipped with hand held monitoring equipment. Self- powered wireless condition monitoring systems provides on-line monitoring of critical plant and machinery providing major operating cost benefits. A vibration energy harvester(VEH) is a device that converts kinetic energy occurred by machine vibration into useable electrical energy. Using VEHs to power wireless monitoring systems can yield significant benefits: increased reliability, lower life time costs and no battery disposal issues, etc. This paper proposes the novel prototype design and manufacturing of a VEH that can eliminate the effect by failed batteries.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.3
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• pp.65-78
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• 2013

To analyze influences under open fault conditions in switching devices, an integrated simulation and condition monitoring scheme for a permanent magnet synchronous generator (PMSG) based variable speed grid-connected wind turbine system are presented. Among various faults in power electronics components, the open fault in switching devices may arise when the switches are destructed by an accidental over current, or a fuse for short protection is blown out. Under such a faulty condition, the grid-side inverter as well as the generator-side converter does not operate normally, producing an increase of current harmonics, and a reduction in output and efficiency. As an effective way for a condition monitoring of generation system by online basis without requiring any diagnostic apparatus, the estimation schemes for generated voltage, flux linkage, and stator resistance are proposed and the validity of the proposed scheme is proved through comparative simulations.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.2
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• pp.245-252
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• 2020

Unexpected equipment defects can cause a huge economic losses in the society at large. Although condition monitoring can provide solutions, the signal processing algorithms must be developed to predict mechanical failures using data acquired from various sensors attached to the equipment. The signal processing algorithms used in a condition monitoring requires high computing efficiency and resolution. To improve condition monitoring on a wireless sensor network(WSN), data visualization can maximize the expressions of the data characteristics. Thus, this paper proposes the extraction of visual feature from temperature data over time using condition monitoring based on a WSN to identify environmental conditions of equipment in a large-scale infrastructure. Our results show that time-frequency analysis can visually track temperature changes over time and extract the characteristics of temperature data changes.

• Proceedings of the KSME Conference
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• 2008.11a
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• pp.1069-1074
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• 2008

Tool condition monitoring (TCM) is crucial for improvement of productivity in manufacturing process. However, TCM techniques have not been applied to monitor tool failure in an industrial gear shaving application. Therefore, this work studied a statistical TCM method for monitoring gear shaving tool condition. The method modeled the shaving process using beta probability distribution in order to extract the effective features. Modeling includes rectifying for converting a bi-modal distribution into a unimodal distribution, estimating parameters of beta probability distribution based on method of moments. The usefulness of features obtained from the proposed method was evaluated and discussed.

• Proceedings of the KSME Conference
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• 2005.11a
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• pp.196.1-196.1
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• 2005

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.703-704
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• 2013