• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.374-379
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• 2016

On-line process monitoring schemes are designed to give early warnings of process faults. In the artificial intelligence and machine learning fields, reliable approaches have been utilized, such as kernel-based nonlinear techniques. This work presents a kernel-based empirical monitoring scheme with a small sample problem. The measurement data of normal operations are easy to collect, whereas special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing the process monitoring performance. This can be achieved by the preprocessing of raw process data and eliminating unwanted variations of data. In this work, the performance of several monitoring schemes was demonstrated using three-dimensional batch process data. The results showed that the monitoring performance was improved significantly in terms of the detection success rate.

• The KIPS Transactions:PartD
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• v.13D no.4 s.107
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• pp.613-618
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• 2006

As the embedded software spreads in various areas, many development tools have been made to minimize the developing time. But these tools cannot be applicable to all the environment because they have been created for the specific platform. As such, this paper proposes an Embedded Monitoring System, which supports the various communication environment and removes the limitation of adaptability to the various platforms. Using the Code Inline technique, this system can perform the monitoring process. However, we should consider the optimization for the monitoring process and monitoring sensors because the technique has the monitoring sensor overhead. As such, this paper proposes an approach for initializing the monitoring process and a bit-masking technique for optimizing the monitoring sensor. The Embedded Monitoring System will be applicable to all the areas using embedded systems.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.14 no.6
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• pp.1-6
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• 2015

Tool condition monitoring plays one of the most important roles in the improvement of both machining quality and productivity. In this regard, various process signals and monitoring methods have been developed. However, most of the existing studies used cutting force or acoustic emission signals, which posed risks of interference with the machining system in dynamics, fixturing, and machining configuration. In this study, a feed motor current signal is used as a process signal representing process and tool states in tool breakage monitoring based on an adaptive autoregressive model and unsupervised neural network. From the experimental results using various cases of tool breakage, it is shown that the developed system can successfully detect tool breakage before two revolutions of the spindle after tool breakage.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.2
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• pp.103-111
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• 2002

Out of all metal-cutting process, the hole-making process is the most widely used. It is estimated to be more than 30% of the total metal-cutting process. It is therefore desirable to monitor and detect drill wear during the hole-drilling process. One important aspect in controlling the drilling process is monitoring drill wear status. There are two systems, Basic system and Online system, to detect the drill wear. Basic system comprised of spindle rotational speed, feed rates, thrust torque and flank wear measured by tool microscope. Outline system comprised of spindle rotational speed feed rates, AE signal, flank wear area measured by computer vision, On-line monitoring system does not need to stop the process to inspect drill wear. Backpropagation neural networks (BPNs) were used for on-line detection of drill wear. The output was the drill wear state which was either usable or failure. This paper deals with an on-line drill wear monitoring system to fit the detection of the abnormal tool state.

• Transactions of Materials Processing
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• v.31 no.1
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• pp.5-10
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• 2022

In multi-stage cold forging process, to enhance the productivity and product quality, in-site process monitoring technique by implanting sensors such as piezo-sensor and acoustic emission sensor has been continuously studied. For accurate analysis of the process, the selection of appropriate sensors and implantation positions are very important. Until now, in a multi-state forging machine, wedge parts located at the end of punch-set are used but it is difficult to analyze minute changes in die block-set. In this study, we also implanted sensors to the die part (die spacer) and compared signals from both sensors and found that sensing signals from die part showed enhanced process monitoring results.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2390-2392
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• 1998

In this paper, we discuss the development of monitoring system with data process equipment which transfers data from Remote Terminal Unit(RTU) to monitoring computer. The RTUs sense temperature, pressure and PLC(Programmable Logic Controller) nodes conditions of scrubber in semiconductor manufacture processing. The data Process equipment is connected every RTU and a monitoring computer through serial communication. This equipment receives informations from RTU, process data, and transfers them to monitoring computer. To avoid congestion in data communication, task scheduling algorithm used RT O/S(Real-Time Operating System) is embedded in ROM which is a part of data Process equipment.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.8 no.5
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• pp.1082-1086
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• 2007

This research has a purpose of constructing the data monitoring system that makes two-tier work state in the brick production factory to unification by reusing cinder. Monitoring system automatically manages data by using data managing processes such as a state managing process, a location managing process, a badness managing process, a circumstances managing process. In this research, the data management monitoring system manufactures state information of each processes received from RFID and transmits them to data monitoring system. Analyzed data through this system reuses the cinder, so it can effectively manage the production process of the factory which produces bricks through processing automation, faulty-ratio minimization, real-time monitoring and loading managing.

• Communications for Statistical Applications and Methods
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• v.7 no.3
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• pp.773-788
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• 2000

Statistical process control (SPC) and engineering process control (EPC) are based on different strategies for processes quality improvement. SPC reduces process variability by detecting and eliminating special causes of process variation. while EPC reduces process variability by adjusting compensatory variables to keep the quality variable close to target. Recently there has been needs for a process control proceduce which combines the tow strategies. This paper considers a combined scheme which simultaneously applies SPC and EPC techniques to reduce the variation of a process. The process model under consideration is an integrated moving average(IMA) process with a step shift. The EPC part of the scheme adjusts the process back to target at every fixed monitoring intervals, which is referred to a repeated adjustment scheme. The SPC part of the scheme uses an exponentially weighted moving average(EWMA) of observed deviation from target to detect special causes. A Markov chain model is developed to relate the scheme's expected cost per unit time to the design parameters of he combined control scheme. The expected cost per unit time is composed of off-target cost, adjustment cost, monitoring cost, and false alarm cost.

• International Journal of Contents
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• v.3 no.1
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• pp.24-28
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• 2007

This paper embodies the agent based cinder monitoring system which supports PDA(Personal Digital Assistant). Monitoring system automatically manages data by using data managing agents such as a state managing agent, a location managing agent, a badness managing agent, a circumstances managing agent, etc, and uses a massive data processing agent to manage massive data. The development of agent based data monitoring system for the stable cinder reuse will be an epoch-making method to develop the process mechanized or manual-labored that widely spreads into the real-time automated process.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.102.1-102
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• 2001

In this work, extensions to partial least squares (PLS) for wastewater treatment (WWT) process monitoring are discussed. Conventional data gathered by monitoring WWT systems are usually time varying, high dimensional, correlated and nonlinear, PLS has been shown to be an efficient approach in modeling and monitoring high dimensional and correlated data. To represent dynamic and nonlinear features of the data several kinds of dynamic nonlinear PLS (DNLPLS) models have been proposed. However, the complexity and ambiguity of the models make them unsuitable for WWT monitoring, Recently, dynamic fuzzy PLS (DFPLS) was proposed ...