• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.3
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• pp.57-64
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• 2005

In two-degree of freedom(TDOF) inertial sensors, two axes are mechanically correlated with each other. Fault source of one axis sensor may affect the other axis sensor, and therefore multiple fault detection and isolation(FDI) technique is required. Conventional FDI techniques using hardware redundancy need four TDOF inertial sensors for FDI. In this study, three TDOF inertial sensor redudancy case is considered, where conventional FDI technique can detect the fault, but cannot isolate the fault sensor. Hybrid FDI technique is proposed to solve this problem. Hybrid FDI technique utilizes the analytic redundancy by utilizing the unscented kalman filter as well as hardware redundancy for FDI. To verify the effectiveness of the proposed FDI technique, numerical simulations are performed using six degree of freedom nonlinear aircrft dynamics.

• International Journal of Control, Automation, and Systems
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• v.5 no.2
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• pp.155-160
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• 2007

A fault detection and isolation (FDI) problem is considered for inertial sensors, such as gyroscopes and accelerometers and a new FDI method for double faults is proposed using reduced-order parity vector. The reduced-order parity vector (RPV) algorithm enables us to isolate double faults with 7 sensors. Averaged parity vector is used to reduce false alarm and wrong isolation, and to improve correct isolation. The RPV algorithm is analyzed by Monte-Carlo simulation and the performance is given through fault detection probability, correct isolation probability, and wrong isolation probability.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.04a
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• pp.363-367
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• 1996

The robustness issues in fault detection and isolation(FDI) have received considerable attenuation in recent years, due to the increasing demand for safe and reliable operation of uncertain and complex dynamic systems. The aim of this paper is to present the FDI method by using proportional integral(PI) observer and unknown input observer(UIO) under the faults of actuators and sensors. Due to this simple residual generator, the PI observer can easily detect the both faults of actuator and sensor. A simulation results show the effectiveness of this methods.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.1
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• pp.1-9
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• 2004

Inertial navigation system with hardware redundancy must use FDI(Fault Detection and Isolation) method to remove the influence of faulty sensors. Until now, several FDI methods such as PSA(Parity Space Approach), GLT(Generalized Likelihood ratio Test) and OPT(Optimal Parity vector Test) method are generally used. However, because these FDI methods only consider the situation that the system has one faulty sensor, these methods cannot be directly adapted for the system with two faulty sensors. To solve this problem, in this paper, PSA method is analyzed and based on this result, new FDI method called EPSA is proposed to consider a detection and an isolation of two faulty sensors in inertial navigation system.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.146-154
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• 2019

An improved principal component analysis (PCA) method is applied for sensor fault detection and isolation (FDI) in a nuclear power plant (NPP) in this paper. Data pre-processing and false alarm reducing methods are combined with general PCA method to improve the model performance in practice. In data pre-processing, singular points and random fluctuations in the original data are eliminated with various techniques respectively. In fault detecting, a statistics-based method is proposed to reduce the false alarms of $T^2$ and Q statistics. Finally, the effects of the proposed data pre-processing and false alarm reducing techniques are evaluated with sensor measurements from a real NPP. They are proved to be greatly beneficial to the improvement on the reliability and stability of PCA model. Meanwhile various sensor faults are imposed to normal measurements to test the FDI ability of the PCA model. Simulation results show that the proposed PCA model presents favorable performance on the FDI of sensors no matter with major or small failures.

• Journal of the Korean Society of Propulsion Engineers
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• v.20 no.2
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• pp.56-66
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• 2016

Robustness is essential for model based FDI (Fault Detection and Isolation) and it is inevitable to have modeling errors and sensor signal noises during the process of FDI. This study suggests an improved method by applying NARX (Nonlinear Auto Regressive eXogenous) model and Kalman estimator in order to cope with problems caused by linear model errors and sensor signal noises in the process of fault diagnoses. Fault decision is made by the probability of the trend of gradually accumulated errors applying Fuzzy logic, which are robust to instantaneous sensor signal noises. Reliability of fault diagnosis is verified under various fault simulations.

• Smart Structures and Systems
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• v.16 no.2
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• pp.329-345
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• 2015

As commonly known, sensor errors and faulty signals may potentially lead structures in vibration to catastrophic failures. This paper presents a new approach to deal with sensor errors/faults in vibration control of structures by using the Fault detection and isolation (FDI) technique. To demonstrate the effectiveness of the approach, a space truss structure with semi-active devices such as Magneto-Rheological (MR) damper is used as an example. To address the problem, a Linear Matrix Inequality (LMI) based fixed-order $H_{\infty}$ FDI filter is introduced and designed. Modeling errors are treated as uncertainties in the FDI filter design to verify the robustness of the proposed FDI filter. Furthermore, an innovative Fuzzy Fault Tolerant Controller (FFTC) has been developed for this space truss structure model to preserve the pre-specified performance in the presence of sensor errors or faults. Simulation results have demonstrated that the proposed FDI filter is capable of detecting and isolating sensor errors/faults and actuator faults e.g., accelerometers and MR dampers, and the proposed FFTC can maintain the structural vibration suppression in faulty conditions.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.407-414
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• 2007

A sensor fault detection scheme(SFDS) for a class of nonlinear systems that can be represented by Takagi-Sugeno fuzzy model is proposed. Basically, the SFDS may be considered as a multiple observer scheme(MOS) in which the bank of state observers and the detection & isolation logic are included. However, the proposed scheme has two great differences from the conventional MOSs. First, the proposed scheme includes fuzzy fault detection observers(FFDO) that are constructed based on the T-S fuzzy model that provides very good approximation to nonlinear dynamic systems. Secondly, unlike the conventional MOS, the FFDOS are driven not parallelly but sequentially according to the predetermined sequence to avoid the massive computational burden, which is known to be the biggest obstacle to the practical application of the multiple observer based FDI schemes. During the operating time, each FFDO generates the residuals carrying the information of a specified fault, and the corresponding fault detection logic unit performs the logical operations to detect and isolate the fault of interest. The proposed scheme is applied to an inverted pendulum control system for sensor fault detection/isolation. Simulation study shows the practical feasibility of the proposed scheme.

• Journal of the Korea Institute of Military Science and Technology
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• v.3 no.2
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• pp.140-148
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• 2000

This paper presents a fault detection and isolation(FDI) method based on Generalized Likelihood Ratio(GLR) test for the tightly coupled SDINS/CPS system. The GLR test is known to have the capability of detecting an assumed change while estimating its occurrence time and magnitude, and isolating the changing part. Once a fault is detected even if we don't know if the fault occurrs at either INS or GPS, multi-hypothesized GLR scheme performs the fault isolation between INS and GPS, and find which satellite malfunctions. However, in the INS faulty case, it turned out to fail to accomodate the fault isolation between accelerometer and gyroscope due to the coupling effects and a poor observability of the system. Hence, to isolate the INS fault, it needs to change the attitude of the vehicle resulting in enhancing the degree of observability.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2183-2187
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• 2005

In this paper we propose a new fault detection and isolation (FDI) method for those faults of parameter change type. First, we design a residual generator based on the ${\delta}$-operator model of the plant by using the stable pseudo inverse system. Second, the parameter change is estimated by using the property of the block Hankel operator. Third, reliability with respect to stability is quantified. Fourth, the limitations for the meaningful diagnosis in our method are given. The numerical examples demonstrate the effectiveness of the proposed method.