• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.569-572
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• 1992

This paper describes a new method of pseudorandom testing of a digital circuit by use of correlation method and a neural network. The authors have recently proposed a new method of fault diagnosis of logical circuit by applying a pseudorandom M-sequence to the circuit under test, calculating the crosscorrelation function between the input and the output, and comparing the crosscorrelation functions with the references. This method, called MSEC method, is further extended by using a neural network in order to not only detect the existence of faults but also find the place or location of the faults. An experiment by using a simple digital circuit shows enough applicability of this method to industrial testing of circuit board.

• 한국안전학회지
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• 제15권2호
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• pp.36-45
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• 2000

Automated production system is composed of many complicated techniques and it become a very difficult task to control, monitor and diagnose this compound system. Moreover, it is required to develop an effective diagnosing technique and reduce the diagnosing time while operating the system in parallel under many faults occurring concurrently. This study develops a Modular Artificial Neural Network(MANN) which can perform a diagnosing function of multiple faults with the following steps: 1) Modularizing a complicated system into subsystems. 2) Formulating a hierarchical structure by dividing the subsystem into many detailed elements. 3) Planting an artificial neural network into hierarchical module. The system developed is implemented on workstation platform with $X-Windows^{(r)}$ which provides multi-process, multi-tasking and IPC facilities for visualization of transaction, by applying the software written in $ANSI-C^{(r)}$ together with $MOTIF^{(r)}$ on the fault diagnosis of PI feedback controller reactor. It can be used as a simple stepping stone towards a perfect multiple diagnosing system covering with various industrial applications, and further provides an economical approach to prevent a disastrous failure of huge complicated systems.

• 전력전자학회논문지
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• 제21권6호
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• pp.481-485
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• 2016

When analyzing economic feasibility for installing a PV generation plant at a certain location, the prediction of possible annual power production at the site using the target PV panels should be conducted on the basis of the local weather data provided by a local weather forecasting office. In addition, the prediction of PV generating power under certain weather conditions is useful for fault diagnosis and performance evaluation of PV generation plants during actual operation. This study analyzes PV cell characteristics according to a variety of weather conditions, including ambient temperature and irradiance level. From the analysis and simulation results, this work establishes a proper model that can predict the output characteristics of PV cells under changes in weather conditions.

• Journal of Electrical Engineering and Technology
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• 제4권2호
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• pp.219-228
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• 2009

The growing application and the numerous qualities of induction motors (1M) in industrial processes that require high security and reliability levels has led to the development of multiple methods for early fault detection. However, various faults can occur, such as stator short-circuits and rotor failures. Traditionally the diagnosis machine is done through a sinusoidal power supply, in the present paper we study experimentally the effects of the rotor failures, such as broken rotor bars in function of the ac supplying, the load and show the impact of the converter from diagnosis of the machine. The technique diagnosis used is based on the spectral analysis of stator currents or stator voltages respectively according to the types of induction motor ac supplying. So, four different ac supplying are considered: ${\odot}$ the IM is directly by the balanced three-phase network voltage source, ${\odot}$ the IM is fed by a sinusoidal current source given the controlled by hysteresis, ${\odot}$ the IM is fed (in open loop) by a scalar control imposing through ratio V/f=constant, ${\odot}$ the IM is controlled through a vector control using space vector pulse width modulation (SVPWM) technique inverter with an outer speed loop.

• 한국해양공학회지
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• 제35권4호
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• 한국진공학회:학술대회논문집
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• 한국진공학회 2012년도 제42회 동계 정기 학술대회 초록집
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• pp.241-241
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• 2012

Semiconductor industry has been taking the advantage of improvements in process technology in order to maintain reduced device geometries and stringent performance specifications. This results in semiconductor manufacturing processes became hundreds in sequence, it is continuously expected to be increased. This may in turn reduce the yield. With a large amount of investment at stake, this motivates tighter process control and fault diagnosis. The continuous improvement in semiconductor industry demands advancements in process control and monitoring to the same degree. Any fault in the process must be detected and classified with a high degree of precision, and it is desired to be diagnosed if possible. The detected abnormality in the system is then classified to locate the source of the variation. The performance of a fault detection system is directly reflected in the yield. Therefore a highly capable fault detection system is always desirable. In this research, time series modeling of the data from an etch equipment has been investigated for the ultimate purpose of fault diagnosis. The tool data consisted of number of different parameters each being recorded at fixed time points. As the data had been collected for a number of runs, it was not synchronized due to variable delays and offsets in data acquisition system and networks. The data was then synchronized using a variant of Dynamic Time Warping (DTW) algorithm. The AutoRegressive Integrated Moving Average (ARIMA) model was then applied on the synchronized data. The ARIMA model combines both the Autoregressive model and the Moving Average model to relate the present value of the time series to its past values. As the new values of parameters are received from the equipment, the model uses them and the previous ones to provide predictions of one step ahead for each parameter. The statistical comparison of these predictions with the actual values, gives us the each parameter's probability of fault, at each time point and (once a run gets finished) for each run. This work will be extended by applying a suitable probability generating function and combining the probabilities of different parameters using Dempster-Shafer Theory (DST). DST provides a way to combine evidence that is available from different sources and gives a joint degree of belief in a hypothesis. This will give us a combined belief of fault in the process with a high precision.

• 전력전자학회논문지
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• 제26권6호
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• pp.381-389
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• 2021

Most diagnosis approaches rely on historical failure data that might not be feasible in real operating conditions because the battery voltage and internal parameters are nonlinear according to various operating conditions, such as cell-to-cell configuration and initial condition. To overcome this issue, the estimator and the predictor require integrated approaches that consider comprehensive data, with the degradation process and measured data taken into account. In this paper, vector autoregressive models (VAR) with various parameters that affect overdischarge to the cell in the battery pack were constructed, and the cell-to-cell parameters were identified using an adaptive model to analyze the influence of failure prognosis. The theoretical analysis is validated using experimental results in terms of the feasibility and advantages of fault prognosis.

• 문화기술의 융합
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• 제10권3호
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• pp.801-807
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• 2024

선형 로봇은 자동화 시스템에서 부품의 이송이나 위치 결정에 널리 사용되며 보통 높은 정밀도가 요구된다. 선형 로봇을 응용한 시스템의 제작회사에서는 로봇의 이상 유무를 작업자가 판단하는데, 작업자의 숙련도에 따라 이상 상태를 판단하는 정확도가 달라진다. 최근에는 인공지능 등의 기술을 사용하여 로봇 스스로 이상을 검출하는 방법에 관한 연구가 진행되고 있다. 본 논문에서는 전이 학습을 이용하여 선형 로봇의 볼 스크류 정렬 이상과 선형 레일 정렬 이상을 검출하는 시스템을 제안하고 가속도 센서와 토크 센서 정보를 이용한 별개의 실험을 통해 제안한 시스템의 이상 검출 성능을 검증 및 비교한다. 센서로부터 얻어진 신호를 스펙트로그램 이미지로 변환한 후, 영상 인식 인공지능 분류기를 사용하여 이상의 종류를 진단하였다. 제안한 방법은 선형 로봇뿐만 아니라 일반적인 산업용 로봇에도 적용할 수 있을 것으로 기대한다.

• Nuclear Engineering and Technology
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• 제55권5호
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• pp.1901-1910
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• 2023

The safety-class (1E) digital control system (DCS) of nuclear power plant characterized structural multiple redundancies, therefore, it is important to quantitatively evaluate the reliability of DCS in different degree of backup loss. In this paper, a reliability evaluation model based on T-S fuzzy fault tree (FT) is proposed for 1E DCS of nuclear power plant, in which the connection relationship between components is described by T-S fuzzy gates. Specifically, an output rejection control system is chosen as an example, based on the T-S fuzzy FT model, the key indicators such as probabilistic importance are calculated, and for a further discussion, the T-S fuzzy FT model is transformed into Bayesian Network(BN) equivalently, and the fault diagnosis based on probabilistic analysis is accomplished. Combined with the analysis of actual objects, the effectiveness of proposed method is proved.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2004년도 학술대회 논문집
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• pp.371-374
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• 2004

Induction motors are a critical component of many industrial processes and are frequently integrated in commercially available equipment. Safety, reliability, efficiency, and performance are some of the major concerns of induction motor applications. Preventive maintenance of induction motors has been a topic great interest to industry because of their wide range application of industry. Since the use of mechanical sensors, such as vibration probes, strain gauges, and accelerometers is often impractical, the motor current signature analysis (MACA) techniques have gained murk popularity as diagnostic tool. Fault tolerant control (FTC) strives to make the system stable and retain acceptable performance under the system faults. All present FTC method can be classified into two groups. The first group is based on fault detection and diagnostics (FDD). The second group is independent of FDD and includes methods such as integrity control, reliable stabilization and simultaneous stabilization. This paper presents the fundamental FDD-based FTC methods, which are capable of on-line detection and diagnose of the induction motors. Therefore, our group has developed the embedded distributed fault tolerant and fault diagnosis system for industrial motor. This paper presents its architecture. These mechanisms are based on two 32-bit DSPs and each TMS320F2407 DSP module is checking stator current, voltage, temperatures, vibration and speed of the motor. The DSPs share information from each sensor or DSP through DPRAM with hardware implemented semaphore. And it communicates the motor status through field bus (CAN, RS485). From the designed system, we get primitive sensors data for the case of normal condition and two abnormal conditions of 3 phase induction motor control system is implemented. This paper is the first step to drive multi-motors with serial communication which can satisfy the real time operation using CAN protocol.