• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권2호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.130-130
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• 2000

This paper presents a robust fault diagnosis and fault tolerant control lot the actuator and sensor faults in the closed-loop systems affected by unknown inputs or disturbances. The fault diagnostic scheme is based on the residual set generation by using robust Parity space approach. Residual set is evaluated through the threshold test and then fault is isolated according to the decision logic table. Once the fault diagnosis module indicates which actuator or sensor is faulty, the fault magnitude is estimated by using the disturbance-decoupled optimal state estimation and a new additive control law is added to the nominal one to override the fault effect on the system. Simulation results show that the method has definite fault diagnosis and fault tolerant control ability against actuator and sensor faults.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1999-2005
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• 2005

Collaboration in wireless sensor networks must be fault-tolerant due to the harsh environmental conditions in which such networks can be deployed. This paper focuses on finding signal processing algorithms for collaborative target detection based on the generalized approach to signal processing in the presence of noise that are efficient in terms of communication cost, precision, accuracy, and number of faulty sensors tolerable in the wireless sensor network. Two algorithms, namely, value fusion and decision fusion constructed according to the generalized approach to signal processing in the presence of noise, are identified first. When comparing their performance and communication overhead, decision fusion is found to become superior to value fusion as the ratio of faulty sensors to fault free sensors increases. The use of the generalized approach to signal processing in the presence of noise under designing value and decision fusion algorithms in wireless sensor networks allows us to obtain the same performance, but at low values of signal energy, as under the employment of universally adopted signal processing algorithms widely used in practice.

• 제어로봇시스템학회논문지
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• 제10권11호
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• pp.1037-1043
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• 2004

In this paper, a new fault detection and isolation algorithm fur inertial sensor system is proposed. To identify the inertial sensor fault, single antenna GPS receiver is used as an effective redundancy source. To use GPS receiver as redundancy for the inertial sensors, the algorithm to estimate the attitude and acceleration using single antenna GPS receiver is adopted. By using Doppler shift of carrier phase signal and kinetic characteristics of aircraft, attitude information of aircraft can be obtained at the coordinated flight condition. Based on this idea, fault diagnosis algorithm for inertial sensors using single antenna GPS based attitude is proposed. For more effective FDI, decision variables considering the aircraft maneuver are proposed. The effectiveness of the proposed algorithm is verified through the numerical simulations.

• International Journal of Automotive Technology
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• 제7권6호
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• pp.729-739
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• 2006

This paper describes an active fault-tolerant control system for an induction motor drive that propels an Electrical Vehicle(EV) or a Hybrid one(HEV). The proposed system adaptively reorganizes itself in the event of sensor loss or sensor recovery to sustain the best control performance given the complement of remaining sensors. Moreover, the developed system takes into account the controller transition smoothness in terms of speed and torque transients. In this paper which is the sequel of (Diallo et al., 2004), we propose to introduce more advanced and intelligent control techniques to improve the global performance of the fault-tolerant drive for automotive applications(e.g. EVs or HEVs). In fact, two control techniques are chosen to illustrate the consistency of the proposed approach: sliding mode for encoder-based control; and fuzzy logics for sensorless control. Moreover, the system control reorganization is now managed by a fuzzy decision system to improve the transitions smoothness. Simulations tests, in terms of speed and torque responses, have been carried out on a 4-kW induction motor drive to evaluate the consistency and the performance of the proposed fault-tolerant control approach.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 춘계학술대회
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• pp.355-357
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• 2017

최근 자동화 기술의 발전으로 산업체에는 대규모 자동화 시스템으로 변화되고 있다. 그에 따른 시스템 감시와 파라미터 측정에 사용되는 센서 또한 자동화 시스템에서 중요한 요소로 차지하고 있다. 따라서, 자동화 시스템에서 센서의 고장으로 인한 시스템 오작동으로 고장이 발생한다면 경제적 피해 및 인명피해를 초래할 수 있다. 본 논문에서는 센서의 직접적인 고장진단을 위해 Kung's Algorithm을 이용하여 센서 출력값 모델링과 결정이론을 통한 고장진단방법에 대해 제시한다.

• 한국추진공학회지
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• 제20권2호
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• pp.56-66
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• 2016

모델기반 FDI 과정에서 모델오차와 센서잡음은 피할 수 없으므로 견실성은 모델기반 FDI에서 매우 중요하다. 본 연구에서는 이러한 선형모델 오차 및 신호잡음으로 인하여 고장진단 과정에서 발생하는 결함판단 오류들을 비선형 NARX (Nonlinear Auto Regressive eXogenous) 모델과 칼만추정기를 적용하여 개선하는 방법을 제안하였다. 최종 고장판단은 퍼지로직을 이용하여 발생하는 오차의 추이에 대한 확률로 결정하여 순간적인 신호잡음에 강인하도록 설계하였다. 시뮬레이션을 통하여 운용 환경조건에서 엔진제어기의 고장허용에 따른 성능을 확인하였다.

• Nuclear Engineering and Technology
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• 제54권9호
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• pp.3283-3292
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• 2022

Based on the Deep Q-Network(DQN) algorithm of reinforcement learning, an active fault-tolerance method with incremental action is proposed for the control system with sensor faults of the once-through steam generator(OTSG). In this paper, we first establish the OTSG model as the interaction environment for the agent of reinforcement learning. The reinforcement learning agent chooses an action according to the system state obtained by the pressure sensor, the incremental action can gradually approach the optimal strategy for the current fault, and then the agent updates the network by different rewards obtained in the interaction process. In this way, we can transform the active fault tolerant control process of the OTSG to the reinforcement learning agent's decision-making process. The comparison experiments compared with the traditional reinforcement learning algorithm(RL) with fixed strategies show that the active fault-tolerant controller designed in this paper can accurately and rapidly control under sensor faults so that the pressure of the OTSG can be stabilized near the set-point value, and the OTSG can run normally and stably.

• International Journal of Control, Automation, and Systems
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• 제6권3호
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• pp.351-363
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• 2008

This paper is concerned with tasking and guidance of networked airborne sensors to achieve fault-tolerant sensing. The sensors are coordinated to locate hostile transmitters by intercepting and processing their signals. Faults occur when some sensor-carrying vehicles engaged in target location missions are lost. Faults effectively change the network architecture and therefore degrade the network performance. The first objective of the paper is to optimally allocate a finite number of sensors to targets to maximize the network life and availability. To that end allocation policies are solved from relevant Markov decision problems. The sensors allocated to a target must continue to adjust their trajectories until the estimate of the target location reaches a prescribed accuracy. The second objective of the paper is to establish a criterion for vehicle guidance for which fault-tolerant sensing is achieved by incorporating the knowledge of vehicle loss probability, and by allowing network reconfiguration in the event of loss of vehicles. Superior sensing performance in terms of location accuracy is demonstrated under the established criterion.

• Smart Structures and Systems
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• 제29권1호
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.