• Smart Media Journal
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• v.6 no.4
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• pp.9-16
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• 2017

The railway point machine is an especially important component that changes the traveling direction of a train. Failure of the point machine may cause a serious railway accident. Therefore, early detection of failures is important for the management of railway condition monitoring systems. In this paper, we propose a noise-robust anomaly detection method in railway condition monitoring systems using sound data. First, we extract feature vectors from the spectrogram image of sound signals and convert it into modulation feature to ensure robust performance, and lastly, use the support vector machine (SVM) as an early anomaly detector of railway point machines. By the experimental results, we confirmed that the proposed method could detect the anomaly conditions of railway point machines with acceptable accuracy even under noisy conditions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.2 no.3
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• pp.34-40
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• 2003

This paper describes evaluation and monitoring methods of machining characteristics for developed micro grooving machine. Experiments were conducted under various process conditions such as spindle revolution speed, feed rates and depth of groove V and U shape of blade and STD11 were used in this experiment. The status of grooving was evaluated through analysis of the Acoustic emission (AE) signal resulted in each process condition. Based on the analysis, this paper examined the possibility of monitoring adapting fuzzy logic. In conclusion, this paper presented the possibility of monitoring in process adapting AE technology and appropriate micro grooving condition.

• 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 Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.448-452
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• 2007

Many industrial operations require continuous or nearly-continuous operation of machines, which if interrupted can result in significant financial loss. The condition monitoring of these machines has received considerable attention recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is the development of smart sensor using which can on-line perform condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This sensor can receive data in real-time or periodic time from MEMS accelerometer. Furthermore, this system is capable for signal preprocessing task (High Pass Filter, Low Pass Filter and Gain Amplifier) and analog to digital converter (A/D) which is controlled by CPU. A/D converter that converts 10bit digital data is used. This sensor communicates with a remote site PC using TCP/IP protocols. Wireless LAN contain IEEE 802.11i-PSK or WPA (PSK, TKIP) encryption. Developed sensor executes performance tests for data acquisition accuracy estimations.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2005.05a
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• pp.162-167
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• 2005

Recently, the advanced industries using micro parts are rapidly growing. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro to micro parts. Also, the method of micro-grooving using micro endmilling is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This study deals with condition monitoring using acoustic emission(AE) signal in the micro-grooving. First, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by using the fuzzy C-means algorithm, which is one of the methods to recognize data patterns. These result is effective monitoring method of micro endmill state by the AE sensing techniques which can be expected to be applicable to micro machining processes in the future.

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2315-2325
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• 2015

Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. Experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.636-640
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• 2002

In contrast to conventional CNC, PC-NC opens a new era for machine tools to be more intelligent. For instance, machine tool with PC-NC can be a machine agent system with capability of reacting autonomously to changing operating conditions. This paper introduces a concept of intelligent machine agent system, composed of machine agent and cell manager. Machine agent performs the functions such as process monitoring, diagnosis, maintenance management, condition assessment and schedule negotiation, while cell manager coordinates the negotiation process among multiple machine agents.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2000.05a
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• pp.639-642
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• 2000

Chemical facility system have dangerous elements that can injure the human like an explosion and a fire, gas poisoning by a leakage of the harmful chemical material. In addition to a vibration of the machine occurs the leakage. Therefore, the chemical factory requires for periodic monitoring of the vibration. But, until now, the operator has executed a monitoring of the machine by the senses. So, the diagnostic expert system by which the operator can judge easily and expertly a condition of the machine is developed. This paper describes the structure of diagnostic system and the diagnostic algorithm using fuzzy inference

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.10a
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• pp.454-459
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• 2000

The high hardened materials that are remarkable in aspects of durability have been used for die and mold industry. As the high hardened materials are hard to machine, the high-speed machining is essential to manufacture these materials. Currently, in the general turning and milling, experiments to the tool wear monitoring have studied, but those have not applied in high-speed machining. In this study, the cutting mechanism was analysed by the cutting force according to cutting conditions, and the parameters to monitor the tool wear were selected from the tendency of the cutting force and acceleration according to cutting length in the high-speed machining of the high hardened materials(STD11).

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.143-149
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• 1997

This paper was undertaken to do image analysis of wear debris on operating condition of the lubricated moving surfaces. This lubricating wear test was performed under different experimental conditions using the wear test device was made in our laboratory and wear testing specimen of the pin on dist type was rubbed in paraffine series base oil, by materials, varying applied load, sliding distance. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe wear debris have been developed and are outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology for machine condition monitoring, this to overcome many of the difficulties with current methods and facilitating wider use of wear particle analysis in machine condition monitouing.