• Structural Monitoring and Maintenance
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• v.1 no.2
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• pp.213-230
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• 2014

A novel real-time actuator failure detection algorithm is developed in this paper. Actuator fails when the input to the structure is different from the commanded one. Previous research has shown that one error function can be formulated for each actuator through interaction matrix method. For output without noise, non-zero values in the actuator functions indicate the instant failure of the actuator regardless the working status of other actuators. In this paper, it is further demonstrated that the actuator's error function coefficients will be directly calculated from the healthy input of the examined actuator and all outputs. Hence, the need for structural information is no longer needed. This approach is termed as direct method. Experimental results from a NASA eight bay truss show the successful application of the direct method for isolating and identifying the real-time actuator failure. Further, it is shown that the developed method can be used for real-time sensor failure detection.

• Journal of information and communication convergence engineering
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• v.10 no.1
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• pp.11-20
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• 2012

In a marine ubiquitous sensor network (USN) system using expensive sensors in the harsh ocean environment, it is very important to discover failures and devise recovery techniques to deal with such failures. Therefore, in order to perform failure modeling, this study analyzes the USN-based real-time water quality monitoring service of the Gaduri Aqua Farms at Songdo Island of Yeosu, South Korea and devises methods of discovery and recovery of failure by classifying the types of failure into system element failure, communication failure, and data failure. In particular, to solve problems from the perspective of data, this study defines data integrity and data consistency for use in identifying data failure. This study, by identifying the exact type of failure through analysis of the cause of failure, proposes criteria for performing relevant recovery. In addition, the experiments have been made to suggest the duration as to how long the data should be stored in the gateway when such a data failure occurs.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.3
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• pp.241-247
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• 2011

Although intensive development continues on innovative sensor systems, there is still considerable uncertainty in deciding on the number of sensors required and their locations in order to obtain adequate information on structural behavior. This paper is concerned with the sensor locations on a beam-structure for prognostic structural health monitoring. The purpose of this study is to investigate how to determine optimal sensor placement(OSP) from the sensitivity information of a known failure mode. The sensitivity of the forced vibration response of a beam to the variation of stiffness due to a crack is calculated analytically and used to determine the optimal sensor locations for the specified failure mode. The results of this method compared with the results of different OSP methods. The results have shown that the proposed method on optimal sensor placement is very effective in structural health monitoring.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.175-177
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• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.

• Composites Research
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• v.17 no.2
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• pp.28-33
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• 2004

Failure detection in a cross-ply laminated composite beam under tensile loading were performed using a fiber Bragg grating (FBG) sensor. A Passive Mach-Zehnder interferometric demodulator was proposed to enhance sensitivity and bandwidth. The proposed FBG sensor system without active device such as a phase modulator is very simple in configuration, easy to implement and enables the measurement of high-frequency vibration with low strain amplitude such as impact or failure signal. Failure signals detected by a FBG sensor had offset value corresponding to the strain shift with vibration at a maximum frequency of several hundreds of kilohertz. at the instant of transverse crack propagation in the 90 degree layer of composite beam.

• The KIPS Transactions:PartA
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• v.16A no.1
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• pp.17-26
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• 2009

Large-scale wireless sensor network are expected to play an increasingly important role for the data collection in harmful area. However, the physical fragility of sensor node makes reliable routing in harmful area a challenging problem. Since several sensor nodes in harmful area could be damaged all at once, the network should have the availability to recover routing from node failures in large area. Many routing protocols take accounts of failure recovery of single node but it is very hard these protocols to recover routing from large scale failures. In this paper, we propose a routing protocol, which we refer to as LSFA, to recover network fast from failures in large area. LSFA detects the failure by counting the packet loss from parent node and in case of failure detection LSFAdecreases the routing interval to notify the failure to the neighbor nodes. Our experimental results indicate clearly that LSFA could recover large area failures fast with less packets than previous protocols.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.20 no.1
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• pp.163-169
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• 2020

Recently, research on automation and unmanned operation of machines in the industrial field has been conducted with the advent of AI, Big data, and the IoT, which are the core technologies of the Fourth Industrial Revolution. The machines for these automation processes are controlled based on the data collected from the sensors attached to them, and further, the processes are managed. Conventionally, the abnormalities of sensors are periodically checked and managed. However, due to various environmental factors and situations in the industrial field, there are cases where the inspection due to the failure is not missed or failures are not detected to prevent damage due to sensor failure. In addition, even if a failure occurs, it is not immediately detected, which worsens the process loss. Therefore, in order to prevent damage caused by such a sudden sensor failure, it is necessary to identify the failure of the sensor in an embedded system in real-time and to diagnose the failure and determine the type for a quick response. In this paper, a deep neural network-based fault diagnosis system is designed and implemented using Raspberry Pi to classify typical sensor fault types such as erratic fault, hard-over fault, spike fault, and stuck fault. In order to diagnose sensor failure, the network is constructed using Google's proposed Inverted residual block structure of MobilieNetV2. The proposed scheme reduces memory usage and improves the performance of the conventional CNN technique to classify sensor faults.

• Journal of the Society of Naval Architects of Korea
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• v.30 no.4
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• pp.8-16
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• 1993

Auto-Pilot System uses heading angle information via the position sensor and the rudder device to control the ship direction. Most of the control logics are composed of the state estimation and control algorithms assuming that the measurement device and the actuator have no fault except the measurement noise. But such asumptions could bring the danger in real situation. For example, if the heading angle measuring device is out of order the control action based on those false position information could bring serious safety problem. In this study, the control system including improved method for processing the position information is applied to the Auto-Pilot System. To show the difference between general state estimator and F.D.F., BJDFs for the sensor and the actuator failure detection are designed and the performance are tested. And it is shown that bias error in sensor could be detected by state-augmented estimator. So the residual confined in the 2-dim in the presence of the sensor failure could be unidirectional in output space and bias sensor error is much easier to be detected.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.208-211
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• 2002

In RC beams strengthened with Epoxy-Bonded Fiber, debonding failure happens frequently. Moreover, through the life cycle, it is difficult to recognize clacks and deflections on the surface of concrete members strengthened with Epoxy- Bonded Fiber. For these reasons, we must always monitor the state of RC beams. The Optical FBG sensor is broadly accepted as a structural health monitoring device. The main objective of this paper is that it's possible to monitoring the debonding failure of R.C. beams strengthened with Epoxy-Bonded Fiber. For that, we fixed two Optical FBG sensors at the center of the beam and another two sensors in the end of Epoxy-Bonded Fiber, According to the comparison micro-strain between embeded sensor in concrete and that on the fiber surface, we can find the point which debonding failure occurs

• Proceedings of the Korean Society For Composite Materials Conference
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• 2002.10a
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• pp.118-121
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• 2002

Polyvinylidene di-fluoride (PVDF) film sensor appeared to be practically useful for the structural health monitoring of composite materials and structures. PVDF film sensors were either attached to or embedded in the graphite/epoxy composite (CFRP) samples to detect the fatigue damage at the bondline of single-lap joints or the tensile failure of unidirectional laminates. PVDF sensors were sensitive enough to detect and determine the crack front in linear location since composites usually produce very energetic acoustic emission (AE). PVDF sensors are extremely cost-effective, as flexible as other plastic films, in low profile as thin as a few tens of microns, and have relatively wide-band response, all of which characteristics are readily utilized for the structural health monitoring of composite structures. Signals due to fatigue damage showed a characteristics of mode II (shear) type failure whereas those from fiber breakage at DEN notches showed that of mode I (tensile) type fracture.