• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.886-889
• /
• 2017

An active Fault Tolerant Model Predictive Control (FTMPC) using Fuzzy scheduler is developed. Fault tolerant Control (FTC) system stages are broadly classified into two namely Fault Detection and Isolation (FDI) and fault accommodation. Basically, the faults are identified by means of state estimation techniques. Then using the decision based approach it is isolated. This is usually performed using soft computing techniques. Fuzzy Decision Making (FDM) system classifies the faults. After identification and classification of the faults, the model is selected by using the information obtained from FDI. Then this model is fed into FTC in the form of MPC scheme by Takagi-Sugeno Fuzzy scheduler. The Fault tolerance is performed by switching the appropriate model for each identified faults. Thus by incorporating the fuzzy scheduled based FTC it becomes more efficient. The system will be thereafter able to detect the faults, isolate it and also able to accommodate the faults in the sensors and actuators of the Continuous Stirred Tank Reactor (CSTR) process while the conventional MPC does not have the ability to perform it.

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

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.819-821
• /
• 1998

This paper deals with a method or a residual generation for fault isolation in a centrifugal pump with a water circulation system, driven by a speed controlled dc motor. It is based on parity relations derived from the moving-average model of the system and is used to identify sensor faults and two possible brush and impeller faults, where the former is dealt with additive faults, while the latter characterized as discrepancies between the nominal and actual plant parameters of the system is modelled by multiplicative faults. We will represent the propagation of this uncertainty to the model matrices by the approximate handling of partial derivatives of polynomials. With multiplicative faults, the transformation matrix implemented in the residual generator are calculated on-line. The simulation studies demonstrate that small changes of the system can be detected and diagnosed by using the method.

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.2
• /
• pp.80-86
• /
• 2010

We propose a spacecraft attitude estimation algorithm using a federated unscented Kalman filter. For nonlinear spacecraft systems, the unscented Kalman filter provides better performance than the extended Kalman filter. Also, the decentralized scheme in the federated configuration makes a robust system because a sensor fault can be easily detected and isolated by the fault detection and isolation algorithm through a sensitivity factor. Using the proposed algorithm, the spacecraft can continuously perform a given mission despite navigation sensor faults. Numerical simulation is performed to verify the performance of the proposed attitude estimation algorithm.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.13 no.4
• /
• pp.278-286
• /
• 2008

This paper investigates the fault generation type and the cause of output interruptionsin bulky with $30^{\sim}500[KVA]$ double conversion UPS, and proposes the fault detection method to improve the reliability of power supply used in the critical load in industry. Identifies its existing way of detecting a quality of inverter output it to bypass when exceeds its expectation. Under a UPS managing system, when an inner (Power device, Controller, CPU) fault occurs it disrupts the power supply and these occurrences has been verified by the results of experiments and application results. To overcome these problems, the proposed method constructs independently a fault-detection, a bypass-control device and a triple power supply apart from the conventional UPS operation. Also the detection point is changed to the preceding of a circuit breaker, a reference of fault detection is modified to avoid any clash and the breaking equipment is attached to intercept a spread of accident. As a result of applications of these developed systems to 242 UPS which was installed purposefully to the communication power supply, the service errors has not occurred in the UPS for two years since 2006.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.10a
• /
• pp.204-210
• /
• 1998

This paper discusses an on-line fuzzy dynamic model(FDM) identification of nonlinear processes for the design of fuzzy model based fault detection and isolation(FDI). The dynamic behavior of a nonlinear process is represented by a fuzzy aggregation of a set of local linear models. The identification is divided into two procedures. The first is the off-line identification of membership function. The second is the on-line identification of the local linear models. Then, we propose a residual generation scheme based on the parameters of local linear models and show that the scheme can be used for the design of FDI

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.116.4-116
• /
• 2001

We consider a robust configuration problem of inertial sensors for inertial navigation system(INS). Fault detection and isolation(FDI) is necessary to improve reliability of the system. For FDI, there used to be more than three mutually orthogonal sensors and thus we have to consider configuration methods of sensors. Various studies in this area have been done, but the former results did not consider effect of uncertainty(misalignment, scale factor error) to determine the configuration of the sensors. In this paper robust configuration of sensors is proposed through sensitivity analysis. Also total least square(TLS) method ...

• 제어로봇시스템학회:학술대회논문집
• /
• 2001.10a
• /
• pp.116.5-116
• /
• 2001

When inertial navigation system(INS) employ more sensors that mutually orthogonal sets to three, the redundant sensor system can have improved reliability and accuracy. For the redundant system the placement of redundant sensors is related to the system performance and also the number and proper orientation of sensors are important. We consider INS sensor configurations using two IMUs comprised mutually orthogonal sets of three. We suggest several configurations using two IMUs and analyze the system performance and the FDI(fault detection and isolation) properties from suggested configurations.

• International Journal of Automotive Technology
• /
• v.8 no.5
• /
• pp.637-650
• /
• 2007

A new on-line fault detection and isolation(FDI) scheme has been proposed for engines using an adaptive neural network classifier; this paper investigates the robustness of this scheme by evaluating in a wide range of operational modes. The neural classifier is made adaptive to cope with the significant parameter uncertainty, disturbances, and environmental changes. The developed scheme is capable of diagnosing faults in the on-line mode and can be directly implemented in an on-board diagnosis system(hardware). The robustness of the FDI for the closed-loop system with crankshaft speed feedback is investigated by testing it for a wide range of operational modes, including robustness against fixed and sinusoidal throttle angle inputs, change in load, change in an engine parameter, and all changes occurring simultaneously. The evaluations are performed using a mean value engine model(MVEM), which is a widely used benchmark model for engine control system and FDI system design. The simulation results confirm the robustness of the proposed method for various uncertainties and disturbances.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.15 no.6
• /
• pp.673-681
• /
• 2005

As it is the most important to make sure security and reliability for nuclear Power Plant, it's considered the most crucial issues to develop a fault detective and diagnostic system in spite of multiple hardware redundancy in itself. To develop an algorithm for a fault diagnosis in the nuclear PWR steam generator, this paper proposes a method based on ART2(adaptive resonance theory 2) neural network that senses and classifies troubles occurred in the system. The fault diagnosis system consists of fault detective part to sense occurred troubles, parameter estimation part to identify changed system parameters and fault classification part to understand types of troubles occurred. The fault classification part Is composed of a fault classifier that uses ART2 neural network. The Performance of the proposed fault diagnosis a18orithm was corroborated by applying in the steam generator.