• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2198-2202
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• 2005

In this paper we have presented a condition monitoring method of check valve using neural network. The acoustic emission sensor was used to acquire the condition signals of check valve in direct vessel injection (DVI) test loop. The acquired sensor signal pass through a signal conditioning which are consisted of steps; rejection of background noise, amplification, analogue to digital conversion, extract of feature points. The extracted feature points which represent the condition of check valve was utilized input values of fault diagnosis algorithms using pre-learned neural network. The fault diagnosis algorithm proceeds fault detection, fault isolation and fault identification within limited ranges. The developed algorithm enables timely diagnosis of failure of check valve’s degradation and service aging so that maintenance and replacement could be preformed prior to loss of the safety function. The overall process has been experimented and the results are given to show its effectiveness.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.320-326
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• 2008

This paper studies the problem of fault estimation using adaptive fault diagnosis observer. A fast adaptive fault estimation (FAFE) approximator is proposed to improve the rapidity of fault estimation. Then based on linear matrix inequality (LMI) technique, a feasible algorithm is explored to solve the designed parameters. Furthermore, an extension to sensor fault case is investigated. Finally, simulation results are presented to illustrate the efficiency of the proposed FAFE methodology.

• Journal of the Korean Solar Energy Society
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• v.36 no.5
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• pp.91-100
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• 2016

This paper proposes the PV module fault diagnosis sensor which is applied to Zigbee wireless network, and monitoring system using the developed sensor. It is designed with embedded sensor in junction box. The diagnosis elements for algorithm were voltage and temperature. For that reason, It is able to reduce the price and separate the fault of bypass diode from shading differently from other monitoring systems. This fault diagnosis algorithm verified through the Field-installed operations of PV module.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.536-564
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• 2019

Soft faults are inherent in wireless sensor networks (WSNs) due to external and internal errors. The failure of processes in a protocol stack are caused by errors on various layers. In this work, impact of errors and channel misbehavior on process execution is investigated to provide an error classification mechanism. Considering implementation of WSN protocol stack, inter-process correlations of stacked and peer layer processes are modeled. The proposed model is realized through local and global decision trees for fault diagnosis. A hybrid framework is proposed to implement local decision tree on sensor nodes and global decision tree on diagnostic cluster head. Local decision tree is employed to diagnose critical failures due to errors in stacked processes at node level. Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. The framework implementation has been done in Castalia. Simulation results validate the inter-process correlation model-based fault diagnosis. The hybrid framework distributes processing load on sensor nodes and diagnostic cluster head in a decentralized way, reducing communication overhead.

• Transactions of the Korean Society of Automotive Engineers
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• v.15 no.6
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• pp.9-16
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• 2007

This paper describes the design and implementation of the fault diagnosis on the Electronic Throttle Control(ETC) System. The proposed fault diagnosis consists of an input signal, actuator and a processor diagnosis. The input signal diagnosis can detect the faults of the ETC system's input signals such as the position sensor fault, source voltage fault, load current fault, and desired position fault. The actuator diagnosis is able to detect the actuator fault due to the actuator aging and an obstacle which interfere in the movement of the actuator. The processor diagnosis detects the fault which prevents the microprocessor from operating the ETC software. In order to protect the breakdown of the ETC system and assure the driving safety, appropriate reactions are also proposed according to the detected faults. The safety and reliability of the ETC system can be improved by the proposed fault diagnosis.

• Journal of Electrical Engineering and Technology
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• v.8 no.4
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• pp.752-759
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• 2013

This paper proposes a method for fault diagnosis and fault-tolerant control of DC-link voltage sensor for two-stage three-phase grid-connected PV inverters. Generally, the front-end DC-DC boost converter tracks the maximum power point (MPP) of PV array and the rear-end DC-AC inverter is used to generate a sinusoidal output current and keep the DC-link voltage constant. In this system, a sensor is essential for power conversion. A sensor fault is detected when there is an error between the sensed and estimated values, which are obtained from a DC-link voltage sensorless algorithm. Fault-tolerant control is achieved by using the estimated values. A deadbeat current controller is used to meet the dynamic characteristic of the proposed algorithm. The proposed algorithm is validated by simulation and experiment results.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.1
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• pp.138-145
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• 2000

This paper presents a robust fault diagnosis and fault tolerant control method for the control systems in closed-loop affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the disturbance-decoupled state estimation using a 2-stage state observer for state, actuator bias and sensor bias. The estimated bias show the occurrence time, location and type of the faults directly. The estimated state is used for state feedback to achieve fault tolerant control against the faults. Simulation results show that the method has definite fault tolerant ability against actuator and sensor faults, moreover, the faults can be detected on-line, isolated and estimated simultaneously.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10a
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• pp.287-290
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• 1996

Fault diagnosis problem is currently the subject of extensive research and numerous survey paper can be found. Although several works are studied on the fault detection and isolation observers and the residual generators, those are concerned with only the detection of actuator failures or sensor failures. So, the perfect detection and isolation is strongly required for practical applications. In this paper, a, strategy of fault diagnosis using multiple proportional integral (PI) observers including the magnitude of actuator failures is provided. It is shown that actuator failures are detected and isolated perfectly by monitoring the integrated error between actual output and estimated output by a PI observer. Also in presence of complex actuator and sensor failures, these failures are detected and isolated by multiple PI observers.

• 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.

• Journal of the Korean Society of Safety
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• v.19 no.1
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• pp.23-30
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• 2004

Modern production systems are very complex by request of automation, and failure modes that occur in thisautomatic system are very various and complex. The efficient fault diagnosis for these complex systems is essential for productivity loss prevention and cost saving. Traditional fault diagnostic system which perforns sequential fault diagnosis can cause catastrophic failure during diagnosis when fault propagation is very fast. This paper describes the Real-time Intelligent Multiple Fault Diagnosis System (RIMFDS). RIMFDS assesses current machine condition by using sensor signals. This system deals with multiple fault diagnosis, comprising of two main parts. One is a personal computer for remote signal generation and transmission and the other is a host system for multiple fault diagnosis. The signal generator generates various faulty signals and image information and sends them to the host. The host has various modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault modules and agents for efficient multiple fault diagnosis. A SUN workstation is used as a host for multiple fault diagnosis and graphic representation of the results. RIMFDS diagnoses multiple faults with fast fault propagation and complex physical phenomenon. The new system based on multiprocessing diagnoses by using Hierarchical Artificial Neural Network (HANN).