• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2003년도 추계학술대회
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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.

• 한국공작기계학회논문집
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• 제13권5호
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• pp.81-86
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• 2004

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 for a detection of machine malfuction or fault diagnosis.

• Journal of Mechanical Science and Technology
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• 제20권12호
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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.

• 로봇학회논문지
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• 제3권1호
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• pp.9-15
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• 2008

Unlike robotic systems, humans excel at a variety of tasks by utilizing their intrinsic impedance, force sensation, and tactile contact clues. By examining human strategy in arm impedance control, we may be able to teach robotic manipulators human''s superior motor skills in contact tasks. This paper develops a novel method for estimating and predicting the human joint impedance using the electromyogram(EMG) signals and limb position measurements. The EMG signal is the summation of MUAPs (motor unit action potentials). Determination of the relationship between the EMG signals and joint stiffness is difficult, due to irregularities and uncertainties of the EMG signals. In this research, an artificial neural network(ANN) model was developed to model the relation between the EMG and joint stiffness. The proposed method estimates and predicts the multi joint stiffness without complex calculation and specialized apparatus. The feasibility of the developed model was confirmed by experiments and simulations.

• 전기의세계
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• 제29권7호
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• pp.460-470
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• 1980

The design of a control unit for a playback-type industrial robot is studied. Implemented for the cylindrical-coordinate type industrial robot with 5 degrees of freedom, the control unit constructed for the study consists of (i) z-80 .mu.p-based .mu.-computer control system (ii) Teach-Box for work command, and (iii) various softwares for generating signals for servo driving unit and operating the robot as playback-type. Softwares are developed by using high level Basic Language and low level z-80 Assembly Language for ease of programming and speed of program execution. To show the effectiveness, and example is included.

• 한국생산제조학회지
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• 제25권4호
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• pp.271-280
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• 2016

Predicting cutting forces in machine tools is essential to productivity improvement and process control in the manufacturing field. Furthermore, milling machining is more complicated than turning machining. Therefore, several studies have been conducted previously to simulate milling forces; this study aims to simulate the cutting forces in milling machines using multi-layered neural networks. In the experiments, the number of layers in these networks was 3 and 4 and the number of neurons in the hidden layers was varied from 20 to 200. The root mean square errors of simulated cutting force components were obtained from taught and untaught data for the various neural networks. Results show that the error trends for untaught data were non-uniform because of the complex nature of the cutting force components, which was caused by different cutting factors and nonlinear characteristics coming into play. However, trends for taught data showed a very good coincidence.

• 한국자동차공학회논문집
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• 제19권4호
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• pp.54-63
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• 2011

An educational network system based on CAN protocol internal to a passenger ground vehicle has been developed. The developed network system has been applied to a commercial plug-in electrical vehicle and verified the educational applicability. To apply this in-vehicle network technology based on CAN, a suitable electric vehicle has been chosen and a CAN network structure has been designed, developed and manufactured. Since the commercial electric vehicle chosen as a test bed has its own proprietary electric network, we explain how the original electric network has been utilized and how the new network system has been designed. The developed network system on a real vehicle has been tested to show the applicability and the performance. Finally, the system has been applied at few classrooms to demonstrate how the in-vehicle network system works and to teach how to analyse the CAN signals. The developed system proven to be effective for educational purpose.

• 한국구조물진단유지관리공학회 논문집
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• 제14권5호
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• pp.179-186
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• 2010

한 구조물이 손상을 입으면 그 구조물의 동적응답(고유진동수, 가속도, 변형률)이 변하게 된다. 이와 같이 변하는 동적응답을 응답신호로 계측하고 이들 데이터를 신경망에 적용하여 구조물의 손상을 평가하는 방법이 신경망손상평가법이다. 현재까지 정형화된 특정한 경우의 연구가 주로 이루어져 있지만 일반적인 신경망손상평가법의 특성에 관한 연구나 실용 가능성과 장단점에 관한 충분한 연구가 부족하다. 따라서 본 연구는 신경망에 다양한 동적응답을 적용하는데 있어 신경망손상평가법의 일반적인 특성과 적용의 문제점을 연구하였다. 신경망손상평가법은 일정한 가진력을 손상이 있는 구조물에 가하고 그로부터 얻은 응답신호를 이용하여 신경망을 학습을 시킨 후, 임의의 손상이 있는 구조물에 동일한 가진력을 가하여 얻은 응답신호를 이용하여 손상의 위치와 정도를 찾는 것이 현재까지의 연구였다. 그러나 일반적으로 구조물에 작용하는 가진력은 일정하지 않다. 따라서 동일한 가진력에 의해 학습된 신경망에 가진력의 변화가 있는 경우에도 손상을 파악하는지 평가하였다. 모든 응답신호는 모형실험을 통하여 획득하였다.