• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2079-2086
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• 2006

A fundamental study for developing a fault diagnosis system of a pump is performed by using neural network. Acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. And the codes of pump malfunctions were selected as units of output layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. Neural network trained by acoustic signals can detect malfunction or diagnose fault of a given machine from the results.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.27 no.10
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• pp.69-74
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• 2013

There is needed the triple pyroelectric Infrared flame detector to really recognize problem, for the prevention and early suppression of fire. This system recognizes the characteristics of fire sources in various type and is communicated the message to the operators. Therefore, the prevention and early suppression of fire is available. Especially this paper focuss on development of the self diagnosis type flame detector for preventing malfunction comparing of basic and detected values.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.

• Proceedings of the Korean Nuclear Society Conference
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• 1997.10a
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• pp.443-448
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• 1997

The intelligent software system for nuclear power plants (NPPs) has been conceptually designed in this study. Its design goals are to operate NPPs in n improved manner and to support operators' cognitive tasks. It consists of six major modules such as "Information Processing," "Alarm Processing," "Procedure Tracking," "Performance Diagnosis," and "Event Diagnosis" modules for operators and "Malfunction Diagnosis" module for maintenance personnel. Most of the modules have been developed for several years and the others are under development. After the completion of development, they will be combined into one system that would be main parts of advanced control rooms in NPPs. that would be main parts of advanced control rooms in NPPs.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.597-608
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• 1998

A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide early warnings in rolling mill. Because dynamics of rolling mill is non-linear. This paper proposes a new method for diagnosis of rolling mill using wavelet to solve this problem. Proposed method that measures the vibration signals of rolling mill on-line and analyze it using wavelet to acquire pattern datas. And we design a nero-fuzzy model that diagnose a rolling mill using this data. Validity of the new method is asserted by numerical simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.32.5-32
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• 2002

Processing plasmas are playing a crucial role in either depositing thin films or etching fine patterns. Any variability in process factors (such as radio frequency power or pressure) can cause a significant shift in plasma state. When this shift becomes large enough to change operating condition beyond an acceptable level, overall product quality can greatly be jeopardized. Thus, timely and accurate diagnosis of plasma malfunction is crucial to maintaining device yield and throughput. Many diagnostic systems have been developed, including HIPOCRATES [1] and PIES [2]. Plasma equipment was also diagnosed by combining neural network and expert system called Dempster-Schafer Theory [3]. A fact c...

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1114-1117
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• 1993

The paper presents an effective method for finding the propagation structure of the real origin of a system malfunction. It uses a combined system model consisting of Structural Model (SM) in the form of Fuzzy Directed Graph and Behavior Model (BM) as a set of Fuzzy Relational Equations $A\;{\circ}\;R\;=\;B$. Here a specially proposed fuzzy inference technique is checked and investigated. Finally a test example for fault diagnosis of an industrial system is given and analyzed.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.4 no.5
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• pp.951-961
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• 2000

A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide early warnings in rolling mill. Because dynamics of rolling mill is non-linear. This Paper proposes a new method for diagnosis of rolling mill using wavelet transform(W) to solve this problem. Proposed method that measures the vibration signals of rolling mill on-line and analyze it using wavelet to acquire pattern data. And we design a fault-diagnosis system that diagnose a rolling mill using this data. Validity of the new method is asserted by real numerical data experiment.

• International Journal of Naval Architecture and Ocean Engineering
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• v.8 no.3
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• pp.243-251
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• 2016

Autonomous Underwater Vehicles (AUVs) generally work in complex marine environments. Any fault in AUVs may cause significant losses. Thus, system reliability and automatic fault diagnosis are important. To address the actuator failure of AUVs, a fault diagnosis method based on the Gaussian particle filter is proposed in this study. Six free-space motion equation mathematical models are established in accordance with the actuator configuration of AUVs. The value of the control (moment) loss parameter is adopted on the basis of these models to represent underwater vehicle malfunction, and an actuator failure model is established. An improved Gaussian particle filtering algorithm is proposed and is used to estimate the AUV failure model and motion state. Bayes algorithm is employed to perform robot fault detection. The sliding window method is adopted for fault magnitude estimation. The feasibility and validity of the proposed method are verified through simulation experiments and experimental data.

• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.3
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• pp.1-7
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• 2011

As the technologies of electronic device have advanced these days, most of mechanical systems are designed with electronic control unit to take advantage of control parameter adaption to operating conditions and firmware flexibilities as well. On-board diagnosis, which detects the system malfunction and identifies potential source of error with its own diagnostic criteria, and fail-safe that can switch the mode of operation in view of recognized error characteristics enables easy maintenance and troubleshooting as well as system protection. This paper dealt with the development of diagnosis and fail-safe function for proportional flow control valve. All type of errors related to valve control system components are investigated and assigned to a specific hexadecimal codes. Cumulative error detection algorithm is applied in order for the sensitivity and reliability to be appropriate. Embedded simulator which runs simultaneously with system program provides the virtual error simulation environment for expeditious development of error detection algorithm. The diagnosis function was verified both with solenoid valve and embedded simulator test and it will enhance the valve control system monitoring function.