• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.1
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• pp.1-8
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• 2004

In this paper, we propose neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.53-62
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• 2023

Due to the recent increase in new and renewable energy, gas turbine generators start and stop every day to supply high-quality power, and accordingly, the life span of high-temperature parts is shortened and the failure of combustion chamber temperature sensors increases. Therefore, in this study, we proposed a fuzzy logic-based failure diagnosis algorithm that can accurately diagnose and systematically detect the failure of the sensor when the dual temperature sensor used for gas turbine control fails, and to confirm the usefulness of the proposed algorithm We tried to confirm the usefulness of the proposed algorithm by performing various simulations under the matlab/simulink environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.1
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• pp.29-35
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• 2019

As the fourth industrial revolution is emerging, many companies are increasingly interested in smart factories and the importance of sensors is being emphasized. In the case that sensors for collecting sensing data fail, the plant could not be optimized and further it could not be operated properly, which may incur a financial loss. For this purpose, it is necessary to diagnose the status of sensors to prevent sensor' fault. In the paper, we propose a scheme to diagnose digital-sensor' fault by analyzing the rising time and falling time of digital sensors through the LSTM(Long Short Term Memory) of Deep Learning RNN algorithm. Experimental results of the proposed scheme are compared with those of rule-based fault diagnosis algorithm in terms of AUC(Area Under the Curve) of accuracy and ROC(Receiver Operating Characteristic) curve. Experimental results show that the proposed system has better and more stable performance than the rule-based fault diagnosis algorithm.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.2
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• pp.283-288
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• 2015

The possible erroneous output data of the sensor nodes can cause the performance limit or the degradation of the reliability in the whole wireless sensor networks(WSN). In this paper, we propose a new sensor node scheme with multiple sensors and a new fault diagnostic algorithm. The algorithm can increase the reliability of the whole WSNs by utilizing measurements of the multiple sensors on the node and by determining the validity of the date by comparing the value of each sensor. It can increase the cost and complexity of the node, but is suitable for the area where the high reliability is critical.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.118-120
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• 2019

최근 IoT 모듈 즉, 사물 인터넷 기반 기술에 대한 연구가 활발히 진행되고 있으며, 다양한 제품들이 출시되고 있다. 대표적인 IoT 제품으로 가정에서 인터넷과 모바일 앱을 이용하여 카메라, 전등, 보일러 등을 제어하는 것을 볼 수 있다. 하지만 보통의 IoT모듈은 센서로 값을 추출하는 것이 목적이기 때문에 센서의 정확성과 고장 유 무를 판단하기 힘들다. 본 논문에서는 아두이노와 무선통신, 웹서버, 안드로이드 어플리케이션을 이용해 센서에서 추출된 값을 비교 분석한 표준편차를 이용해 센서의 고장 유 무를 판단 할 수 있는 모바일 앱을 설계 및 구현하고자 한다. 아두이노의 와이파이, 온습도센서 모듈 등을 이용하여 통신 연결을 하고 각종 환경, 제어정보들을 HTTP통신을 이용하여 웹서버와 통신하여 전달하고 제어한다. 이로써 사용자가 직접 IoT모듈에 가지 않아도 스마트폰 어플리케이션을 통해 센서 상태를 모니터링 및 진단하고 고장 유 무를 파악하여 교체시기를 알려주는 기능을 구현하였다.

• Journal of Advanced Navigation Technology
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• v.7 no.2
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• pp.171-179
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• 2003

In this paper a neural network-based fault tolerant control system for aircraft sensor and actuator failures is considered. By exploiting flight dynamic relations a set of neural networks is constructed to detect sensor failure and give alternative signal for the faulty sensor. For actuator failures another set of neural networks is designed to perform fault detection, identification, and accomodation which returns the aircraft to a new stable trim. Integrated system is simulated to show the performance of the system with sensor and control surface failures.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2011.11a
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• pp.599-602
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• 2011

A propulsion controller for one-time flight vehicles should be designed robustly so that it can complete its missions even in case sensor failures. These vehicles improve their fault tolerance by back-up sensors prepared for the failure of major sensors, which raises the total cost. This paper presents the NARX model which substitutes vehicles' velocity sensors, and detects failure of sensor signals by using model based fault detection. The designed NARX model and fault detection algorithm were optimized and installed in TI's TMS320F2812 so that they were linked to HILS instruments in real-time. The designed propulsion controller made the vehicle to have better fault tolerance with fewer sensors and to complete its missions under a lot of complicated failure situations. The controller's applicability was finally confirmed by tests under the HILS environment.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.05a
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• pp.437-441
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• 1999

In this paper, a fault diagnosis method using unknown-input proportional integral (PI) observers including the magnitude of actuator failures is proposed. It is shown that actuator failures are detected and isolated perfectly by monitoring the integrated error between the actual output and the estimated output using an unknown-input PI observer. Also in presence of complex actuator and sensor failures, these failures are detected and isolated by multiple unknown-input PI observers perfectly.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2022.06a
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• pp.54-55
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• 2022

해양에 설치된 항로표지는 선박의 안전한 항해를 위해 위치 정보를 제공하고, 항로표지에 부착된 센서를 통해 다양한 해양 정보를 수집하고 있다. 하지만 항로표지는 육지와 멀리 떨어진 해상이라는 특수한 작업환경으로 인해 항로표지 유지보수를 위한 많은 시간과 비용이 발생하게 된다. 현재 항로표지에 부착된 센서를 통해 다양한 정보를 수집하고 있지만, 정상 데이터와 비정상 데이터를 구분할 수 있는 정보가 없어 고장진단에 어려움이 있다. 따라서 본 연구에서는 항로표지 센서 고장진단을 위해 머신러닝 비지도학습 중 하나인 K-means 알고리즘을 활용하여 정상 데이터와 비정상 데이터로 군집화하였으며, 분류가 잘 되는 것을 확인할 수 있었다. 향후 연구방향으로는 2개의 클러스터로 구분된 데이터가 실제로 정상 데이터인지, 비정상 데이터인지에 대한 비교·분석이 필요하다.

• 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.