• Journal of IKEEE
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• v.20 no.3
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• pp.251-259
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• 2016

This paper describes that how to enhance the robustness of semiconductor TRM(Transmitter and Receiver Module) through the bias sequencing and tuning the switching time. Previous circuit designs focused on improving the MDS(Minimum Detection Signal) performance. Because TRM has critical problem which transmission output signal leak into receiver by it's compact design. Under this condition, TRM was frequently broken down within the MTBF(Mean Time Between Failure). This study proposes the bias sequencing and tuning the switching time to improve above problem. At first, we collected major failure symptom and infer it's cause. Second, we demonstrated it's effect by derive the improvement method and apply it to our system. And finally we can convinced that the proposed method clear the frequent failure problem with its lack of isolation.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.7
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• pp.669-672
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• 2012

This paper investigates an algorithm for robust fault diagnosis in robot manipulators. The TOSM (Third Order Sliding Mode observer) provides both theoretically exact observation and unknown fault identification without filtration. The EOI (Equivalent Output Injections) of the TOSM observers can be used as residuals for the problem of fault diagnosis and to identify the unknown faults. The obtained fault information can be used for fault detection, isolation as well as fault accommodation to the self-correcting failure system. The computer simulation results for a PUMA 560 robot are shown to verify the effectiveness of the proposed strategy.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.91-97
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• 2006

A major difficulty with the practical application of the multiple observer based IFDI schemes is the computational burden of the residual generation. In this paper, a new residual generator that employs function observers is proposed to reduce the computational burden, and the design methods of the IFDIS, equipped with the residual generator, are presented. The function observers employed in the residual generator can be considered as a dual of the unknown input (function) observer And it can be designed to estimate the measurement errors that are due to sensor faults. The error estimates are further processed to generate the residuals by which reliable fault detection/isolation result car be obtained. The proposed scheme is more useful, in real-time application, than any other multiple state observer based IFDISs. It can be effectively applied to fault tolerant control because the failure effects can be compensated by the use of the estimates of measurement errors. The proposed IFDI scheme is applied to an inverted pendulum control system for the IFDI of failed sensor and fault compensation.

• Korean Journal of Computational Design and Engineering
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• v.17 no.2
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• pp.97-110
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• 2012

High performance sensors and modern data logging technology with real-time telemetry facilitate system fault diagnosis in a very precise manner. Fault detection, isolation and identification in fault diagnosis systems are typical steps to analyze the root cause of failures. This systematic failure analysis provides not only useful clues to rectify the abnormal behaviors of a system, but also key information to redesign the current system for retrofit. The main barriers to effective failure analysis are: (i) the gathered data (event) logs are too large in general, and further (ii) they usually contain noise and redundant data that make precise analysis difficult. This paper therefore applies suitable pre-processing techniques to data reduction and feature extraction, and then converts the reduced data log into a new format of event sequence information. Finally the event sequence information is decoded to investigate the correlation between specific event patterns and various system faults. The efficiency of the developed pre-filtering procedure is examined with a terminal box data log of a marine diesel engine.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.114-123
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• 2002

Called VISTA (Variable-stability In-flight Simulator Test Aircraft), the one-of-a-kind NF-l6D has a simulation system that can mimic several aircraft. Though housed in an F-l6 Fighting Falcon airframe, VISTA can also act like the F-15 Eagle or the Navy's F-14 Tomcat. More importantly, such flexibility allows for improved training and consolidation of some sorties. Consequently USAF Test Pilot School students will have an opportunity to learn how to test future integrated cockpits. In this paper we will use the multiple model adaptive estimation (MMAE) and the multiple model adaptive controller (MMAC) techniques to model the aircraft's flight control system containing the longitudinal and lateral-directional axes. Single and dual actuator and sensor failures will also be included in the simulation. White Gaussian noise will be included to simulate the effects of atmospheric disturbances.

• Journal of the Korean Operations Research and Management Science Society
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• v.16 no.2
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• pp.148-158
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• 1991

The Built-In-Test (BIT) system is an integrated subsystem for the determination of the health status of any primary system. The BIT consists of hardware and software installations directed at performance of the functions of fault detection, diagnosis and isolation, as well as primary system record failure information. Evaluation of the difinitions appropriate to the BIT system, including system characteristics and parameters, is important to an understanding of system functions. The object of this paper is to present general definitions of the BIT diagnosis parameters and a semiquantiative evaluation method for BIT systems. Finally, two case studies for actual problem solutions are included.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.6
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• pp.765-771
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• 1998

An artificial neural network(NN) technique is developed for hardware redundant sensor validation. Since the measurement space is a continuous space with many operating regions, it is difficult to train a NN to correctly detect failure in an accurate measurement system. A conventional backpropagation NN is modified to include an additional preprocessing layer that extracts classification features from scalar measurements. This feature extraction means transform the measurement space to parity space. The NN is independent of the state variable being measured, the instrument range, and the signal tolerance. This NN resembles the parity space approach to signal validation, except that analytical parity equations are unneeded and the NN pattern recognition capability is utilized for decision making.

• Journal of Mechanical Science and Technology
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• v.19 no.8
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• pp.1529-1543
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• 2005

Recently an unmanned aerial vehicle (UAV) has been widely used for military and civil applications. The role of a navigation system in the UAV is to provide navigation data to the flight control computer (FCC) for guidance and control. Since performance of the FCC is highly reliant on the navigation data, a fault in the navigation system may lead to a disastrous failure of the whole UAV. Therefore, the navigation system should possess a fault detection and isolation (FDI) algorithm. This paper proposes an attitude determination GPS/INS integrated navigation system with an FDI algorithm for a UAV. Hardware for the proposed navigation system has been developed. The developed hardware comprises a commercial inertial measurement unit (IMU) and the integrated navigation package (INP) which includes an attitude determination GPS (ADGPS) receiver and a navigation computer unit (NCU). The navigation algorithm was implemented in a real-time operating system with a multi-tasking structure. To evaluate performance of the proposed navigation system, a flight test has been performed using a small aircraft. The test results show that the proposed navigation system can give accurate navigation results even in a high dynamic environment.

• 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.

• Advances in aircraft and spacecraft science
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• v.4 no.1
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• pp.53-64
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• 2017

Aerospace Launch Vehicles (ALV) are generally designed with high reliability to operate in complete security through fault avoidance practices. However, in spite of such precaution, fault occurring is inevitable. Hence, there is a requirement for on-board fault recovery without significant degradation in the ALV performance. The present study develops an advanced fault recovery strategy to improve the reliability of an Aerospace Launch Vehicle (ALV) navigation system. The proposed strategy contains fault detection features and can reconfigure the system against common faults in the ALV navigation system. For this purpose, fault recovery system is constructed to detect and reconfigure normal navigation faults based on the sliding mode observer (SMO) theory. In the face of pitch channel sensor failure, the original gyro faults are reconstructed using SMO theory and by correcting the faulty measurement, the pitch-rate gyroscope output is constructed to provide fault tolerant navigation solution. The novel aspect of the paper is employing SMO as an online tuning of analytical fault recovery solution against unforeseen variations due to its hardware/software property. In this regard, a nonlinear model of the ALV is simulated using specific navigation failures and the results verified the feasibility of the proposed system. Simulation results and sensitivity analysis show that the proposed techniques can produce more effective estimation results than those of the previous techniques, against sensor failures.