• Journal of the Korea Society of Computer and Information
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• v.3 no.2
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• pp.147-154
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• 1998

As technical systems in building are being developed, the processes and systems get more difficult for the average operator to understand. When operating a complex facility, it is beneficial in equipment management to provide the operator with tools which can help in dicision making for recovery from a failure of the system. The main object of the study is to develop real-time automatic fault detection and diagnosis system for optimal operation of IBS building.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.6
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• pp.52-60
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• 2003

In aviation navigation using GPS, requirements on availability and integrity must be absolutely satisfied. Current study on accomplishing this integrity includes RAIM(Receiver Autonomous Integrity Monitoring), monitoring integrity internaIly in GPS receiver itself. Parity space technique as one of RAIM techniques has shown the advantages in fault detection and isolation due to each use of its magnitude and direction under the assumption of one fault. ln case of multiple fault, as biases in errors interact decreasing the effect of multiple fault in parity space, the exact fault detection and identification(FDI) may be difficult to be conducted. This paper focuses on FDI study on two faults and explains why parity space techniques applied on single fault is not adequate to the application of multiple fault case and shows that extended parity space technique may improve the performance of RAIM on two faults.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.29 no.11 s.242
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• pp.1220-1228
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• 2005

Fault detection and diagnostic (FDD) systems have the potential to reduce equipment downtime, service costs, and utility costs. In this study, model based algorithm and fuzzy algorithm were used to detect and diagnose various fault at System multi-air conditioner. various fault include the Refrigerant Low charging, Fouling of Indoor Heat Exchanger, Fouling of Outdoor Heat Exchanger A experimental verification was conducted in the 6HP System multi-air conditioner on an 8-floor building. Test results showed diagnosis result about 78 $\~$ 90$\%$ for given faults. This Study lays the foundation fur future work on develope the real-time fault detection and diagnosis system for the System multi-air conditioner.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2022.06a
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• pp.1232-1235
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• 2022

최근 자동차, 철도차량 등 사용자가 있는 기계 시스템에서의 고장 발생 시 사용자의 안전과 관련된 사고로 이어질 수 있어 부품에 대한 모니터링 및 고장 여부 판단은 매우 중요하다. 이러한 부품 중에서 베어링은 회전체와 회전하지 않는 물체 사이에서 회전이 원활하게 이루어질 수 있도록 하는 부품인데, 베어링에 결함이 발생하게 될 경우, 기계 시스템이 정지하거나, 마찰 열에 의해 화재 등의 치명적인 위험이 발생한다. 본 논문에서는 Resnet과 오토인코더를 활용하여 진동 신호 기반의 베어링의 고장을 감지하고 분류할 수 있는 모델을 제안한다. 제안 방법은 raw data를 이미지로 변환하여 입력으로 사용하는데, 이러한 접근을 통해 수집된 데이터의 손실을 최소화하고 데이터가 가지는 정보를 최대한 분석에 활용할 수 있다. 제안 모델의 검증을 위하여 공개된 데이터셋으로 학습/검증 하였고, 제안 방법이 기존 방법과 비교하여 더 높은 F1 Score와 정확도를 보임을 확인하였다.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2003.06a
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• pp.157-160
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• 2003

발전소에서 운전중인 기계들의 안전운전과 예지 보전을 위하여 발전설비의 고장 감지 및 진단과 상태 모니터링은 중대한 역할을 담당하고 있다. 이 연구에서는 설비의 안전하고 신뢰적인 운전을 위한 기계의 작동상태를 사운드 정보로 획득하고 분석하는 시스템을 제안하였다. 사운드 정보의 사용은 적은 양의 채널의 사용으로 많은 기계 및 설비의 이상 유무의 판별을 가능케 하며, 이를 획득하기 위하여 3개의 마이크로폰, 다채널 A/D변환기, 다채널 I/O Sound Card(Soundtrack DSP24) 및 PC로 시스템을 구성하였다. 소프트웨어 개발언어로서 Microsoft Visual C＋＋ 및 MATLAB을 이용하였다. 화력 발전소에 운전중인 주요기계들의 사운드 정보를 취득하여 취득한 기계별 사운드 정보를 이용하여 주파수 특성을 파악하고, 이를 이용하여 기기의 운전 상태진단을 가능하게 한다.

• Journal of Navigation and Port Research
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• v.46 no.4
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• pp.367-374
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• 2022

The purpose of this study was to propose a deep learning algorithm that applies to the fault diagnosis of fuel pumps and purifiers of autonomous ships. A deep learning algorithm reflecting the time dependence of the measured signal was configured, and the failure pattern was trained using the vibration signal, measured in the equipment's regular operation and failure state. Considering the sequential time-dependence of deterioration implied in the vibration signal, this study adopts Conv1D with sliding window computation for fault detection. The time dependence was also reflected, by transferring the measured signal from two-dimensional to three-dimensional. Additionally, the optimal values of the hyper-parameters of the Conv1D model were determined, using the grid search technique. Finally, the results show that the proposed data preprocessing method as well as the Conv1D model, can reflect the sequential dependency between the fault and its effect on the measured signal, and appropriately perform anomaly as well as failure detection, of the equipment chosen for application.

• Proceedings of the Acoustical Society of Korea Conference
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• spring
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• pp.341-344
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• 2004

발전소에서 운전 중인 발전 설비의 장비 및 기계의 동작, 감시, 진단은 매우 중요한 일이다. 발전소의 이상 감지를 위해 상태 모니터링이 사용되며, 이상이 발생되었을 때 고장의 원인을 분석하고 적절한 조치를 계획하기 위한 이상 진단 과정을 따르게 된다. 본 논문에서는 산업 현장에서 기기들의 운전시에 발생하는 기기 발생 음을 획득하여 정상/비정상을 판정하기 위한 알고리듬에 대하여 연구하였다. 사운드 감시(Sound Monitoring) 기술은 관측된 신호를 acoustic event로 분류하는 것과 분류된 이벤트를 정상 또는 비정상으로 구분하는 두 가지 과정으로 진행할 수 있다. 기존의 기술들은 주파수 분석과 패턴 인식의 방법으로 간단하게 적용되어 왔으며, 본 논문에서는 K-means clustering 알고리듬을 이용하여 사운드를 acoustic event로 분류하고 분류된 사운드를 정상 또는 비정상으로 구분하는 알고리듬을 개발하였다.

• Journal of IKEEE
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• v.25 no.4
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• pp.689-696
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• 2021

In this paper, in order to apply Prognostics and Health Management(PHM) to an electronic system or circuit, a circuit capable of detecting and predicting defect characteristics inside the system or circuit is implemented, and the results are described. In the previous study, we demonstrated that the frequency of the amplitude of S-parameter changed as the circuit defect progressed. These characteristics were measured by network analyser. but in this study, even if the same defect detection method is used, a circuit is proposed to check the progress of the defect, the remaining time, and the occurrence of the defect without large measurement devices. The circuit is designed to detect the change in impedance that generates changes of S-parameter, and it is verified through simulation using the measurement results of Bond-wires.

• Journal of the Institute of Convergence Signal Processing
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• v.23 no.2
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• pp.84-90
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• 2022

Machine tool state monitoring is a process that automatically detects the states of machine. In the manufacturing process, the efficiency of machining and the quality of the product are affected by the condition of the tool. Wear and broken tools can cause more serious problems in process performance and lower product quality. Therefore, it is necessary to develop a system to prevent tool wear and damage during the process so that the tool can be replaced in a timely manner. This paper proposes a method for diagnosing five tool states using a deep learning-based hierarchical convolutional neural network to change tools at the right time. The one-dimensional acoustic signal generated when the machine cuts the workpiece is converted into a frequency-based power spectral density two-dimensional image and use as an input for a convolutional neural network. The learning model diagnoses five tool states through three hierarchical steps. The proposed method showed high accuracy compared to the conventional method. In addition, it will be able to be utilized in a smart factory fault diagnosis system that can monitor various machine tools through real-time connecting.