• Journal of Institute of Control, Robotics and Systems
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• v.22 no.8
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• pp.619-626
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• 2016

This paper proposes the design and performance evaluation of a marine engine fault detection system using a proximity sensor for marine engine. Non-linearity is greatly reduced by using the sensor without increasing the response time by applying the CANopen protocol. The CANopen protocol enables the sensor to send initial values and measurement data. The marine engine fault detection system measures crankshaft deflection and the bottom dead center of the crosshead in real-time, which maintains stability and prevents the serious breakdown of the marine engine by use of an interlocking alarm monitoring system.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.355-357
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• 2017

With the development of automation technology and the increase of large-scale automation projects, sensors used for state monitor and parameter measurement have become more and more important. Once the sensor faults occur, which will lead to the degradation of automation system's performance, and even disastrous consequences. In this paper, sensor output value modeling is performed using Kung's Algorithm for direct fault diagnosis of sensor, and fault diagnosis method based on decision theory is presented.

• Journal of Power Electronics
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• v.14 no.4
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• pp.695-703
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• 2014

In this paper, a fault detection scheme for DC-link voltage sensor and its fault tolerant control strategy for three-phase AC/DC/AC PWM converters are proposed, where the Luenberger observer is applied to estimate the DC-link voltage. The Luenberger observer is based on a converter model, which is derived from the voltage equations of a grid-side converter and the power balance on a DC link. A fault of the voltage sensor is detected by comparing the measured value of the DC-link voltage with the estimated one. When a sensor fault is detected, a fault tolerant control strategy is performed, where the estimated DC-link voltage is used for the feedback control. The estimation error from the observer is about 1.5 V, which is sufficiently accurate for feedback control. In addition, it is shown that the observer performance is robust to parameter variations of the converter. The validity of the proposed method has been verified by simulation and experimental results.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.11
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• pp.1117-1124
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• 2011

An integrated fault detection and isolation method is proposed in this paper. The main objective of this paper is development fault detection, isolation and diagnosis algorithm based on the DKF (Decentralized Kalman Filter) and the bank of IMM (Interacting Multiple Model) filters using penalty scalar for both partial and total faults and the outlier detection algorithm for preventing false alarm also included. The proposed FDI (Fault Detection and Isolation) scheme is developed in four phases. In the first phase, the outlier detection filter is designed to prevent false alarm as a pre-filter. In the second phases, two local filters and master filter are designed to detect sensor faults. In the third phases, the proposed FDI scheme checks sensor residual to isolate sensor faults and 11 EKFs actuator fault models are designed to detect wherever actuator faults occur. In the last phases, four filters are designed to identify the fault type which is either the total fault or partial fault. The developed scheme can deal with not only sensor and actuator faults, but also preventing false alarm. An important feature of the proposed FDI scheme can decreases fault isolation time and figure out not only fault detection and isolation but also fault type identification. To verify the proposed FDI algorithm performance, the Simulator is also developed under the Matlab/Simulink environment.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2005.06a
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• pp.1463-1467
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• 2005

This paper proposes an analytical redundancy technique for fault diagnostics of the sensor in steer-by-wire system. We use incorporating vehicle dynamics modeling into the design of a diagnostic system for steer-by-wire system. The use of a model of vehicle dynamics improves the speed and accuracy of the diagnoses. The proposed fault diagnostics algorithm is based on parity-space methods to generate residuals. To reduce the effects of modeling uncertainty and dynamic transients, the residuals are subject to filtering. We construct diagnostic system consisting residual threshold for detection and isolator with using the directional residual vector.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1009-1013
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• 2009

In this paper, a robust fault detection method for varying temperature based on fuzzy model is proposed. To develop a robust force estimation model, it needs temperature information because the output of force sensor is affected by a temperature variation. The nonlinear dynamic system, such as the parking force of the EPB (Electronic Parking Brake) system is necessary to have a higher order equation model. But, because of the calculation time, the higher order equation model is hard to be used in real application. In case of the lower order equation model, the result is not as accurate as acceptable. To solve this problem, the robust fuzzy model-based fault detection is developed. A proposed fault detection method for varying temperature is verified by HILS (hardware in the loop simulation).

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.197-201
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• 1988

A reliable Analytical Redundancy(AR) based Fault Detection Scheme(FDS) that can detect, discriminate sensor fault and process fault is presented. And a Fault Tolerant Control System ( FTCS ) with the FDS that performs original control objective without considerable loss of control performance in the face of sensor/process faults is constructed. These propositions are valuable in the sense that it resolves the well known sensitivity problem and that sensor/process faults can be detected, discriminated so that effects of any fault can be promptly accomodated by reconfiguring control system structure automatically.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.9A
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• pp.868-877
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• 2006

Sensor network applications need synchronized time to the highest degree such as object tracking, consistent state updates, duplicate detection, and temporal order delivery. In addition, reliability issues and fault tolerance in sophisticated sensor networks have become a critical area of research today. In this paper, we proposed a fault tolerant clock management scheme in sensor networks considering two cases of fault model such as network faults and clock faults. The proposed scheme restricts the propagation of synchronization error when there are clock faults of nodes such as rapid fluctuation, severe changes in drift rate, and so on. In addition, it handles topology changes. Simulation results show that the proposed method has about $1.5{\sim}2.0$ times better performance than TPSN in the presence of faults.