Miniature mobile sensor platforms for condition monitoring of structures

In this paper, a wireless, multisensor inspection system for nondestructive evaluation (NDE) of materials is described. The sensor configuration enables two inspection modes-magnetic (flux leakage and eddy current) and noncontact ultrasound. Each is designed to function in a complementary manner, maximizing the potential for detection of both surface and internal defects. Particular emphasis is placed on the generic architecture of a novel, intelligent sensor platform, and its positioning on the structure under test. The sensor units are capable of wireless communication with a remote host computer, which controls manipulation and data interpretation. Results are presented in the form of automatic scans with different NDE sensors in a series of experiments on thin plate structures. To highlight the advantage of utilizing multiple inspection modalities, data fusion approaches are employed to combine data collected by complementary sensor systems. Fusion of data is shown to demonstrate the potential for improved inspection reliability

Enhanced Position Verification for VANETs using Subjective Logic

The integrity of messages in vehicular ad-hoc networks has been extensively studied by the research community, resulting in the IEEE~1609.2 standard, which provides typical integrity guarantees. However, the correctness of message contents is still one of the main challenges of applying dependable and secure vehicular ad-hoc networks. One important use case is the validity of position information contained in messages: position verification mechanisms have been proposed in the literature to provide this functionality. A more general approach to validate such information is by applying misbehavior detection mechanisms. In this paper, we consider misbehavior detection by enhancing two position verification mechanisms and fusing their results in a generalized framework using subjective logic. We conduct extensive simulations using VEINS to study the impact of traffic density, as well as several types of attackers and fractions of attackers on our mechanisms. The obtained results show the proposed framework can validate position information as effectively as existing approaches in the literature, without tailoring the framework specifically for this use case.Comment: 7 pages, 18 figures, corrected version of a paper submitted to 2016 IEEE 84th Vehicular Technology Conference (VTC2016-Fall): revised the way an opinion is created with eART, and re-did the experiments (uploaded here as correction in agreement with TPC Chairs

Managed information gathering and fusion for transient transport problems

This paper deals with vehicular traffic management by communication technologies from Traffic Control Center point of view in road networks. The global goal is to manage the urban traffic by road traffic operations, controlling and interventional possibilities in order to minimize the traffic delays and stops and to improve traffic safety on the roads. This paper focuses on transient transport, when the controlling management is crucial. The aim was to detect the beginning time of the transient traffic on the roads, to gather the most appropriate data and to get reliable information for interventional suggestions. More reliable information can be created by information fusion, several fusion techniques are expounded in this paper. A half-automatic solution with Decision Support System has been developed to help with engineers in suggestions of interventions based on real time traffic data. The information fusion has benefits for Decision Support System: the complementary sensors may fill the gaps of one another, the system is able to detect the changing of the percentage of different vehicle types in traffic. An example of detection and interventional suggestion about transient traffic on transport networks of a little town is presented at the end of the paper. The novelty of this paper is the gathering of information - triggered by the state changing from stationer to transient - from ad hoc channels and combining them with information from developed regular channels. --information gathering,information fusion,Kalman filter,transient traffic,Decision Support System

CONDITION MONITORING OF ROLLER BEARING USING ENHANCED DEMPSTER/SHAFER EVIDENCE THEORY

According to the generalized Jaccard coefficient and false degree, an improved approach is proposed by incorporating Dempster-Shafer proofs for determining the level of confidence in the evidence. It also determines the weight of proof in terms of trust and falsity. Then, the base probability of the original evidence is weighted and averaged, followed by the adoption of the combined Dempster's compositional rule. It is evident that the above combination can be applied in condition monitoring of bearings up to rupture. Firstly, the supporting vibration signal is decomposed by applying the empirical mode decomposition, empirical wavelet transformation and variational mode decomposition approaches. All the vectors of the fault characteristic are extracted by combining the sample entropy. Then, the fault probability is obtained by performing preliminary diagnosis using the relevance vector machine, where the obtained preliminary diagnostic result is considered as the primary probability of the Dempster-Shafer evidence theory. Finally, it is revealed that an accurate diagnosis could be achieved by performing fusion using the enhanced evidence combination method. Specifically, the accuracies of the initial condition monitoring based on the EMD, EWT and VMD sample entropies and RVM were found to be 97.5%, 98.75% and 95%, respectively. The closeness and high values of these accuracies show that the selected methods are valid. The obtained condition monitoring results show that the relevance vector machine combined with the Dempster-Shafer evidence could enhance the efficiency. This theory has the least error and better reliability in supporting failure diagnosis

Fusion Framework for Moving-Object Classification

International audiencePerceiving the environment is a fundamental task for Advance Driver Assistant Systems. While simultaneous localization and mapping represents the static part of the environment, detection and tracking of moving objects aims at identifying the dynamic part. Knowing the class of the moving objects surrounding the vehicle is a very useful information to correctly reason, decide and act according to each class of object, e.g. car, truck, pedestrian, bike, etc. Active and passive sensors provide useful information to classify certain kind of objects, but perform poorly for others. In this paper we present a generic fusion framework based on Dempster-Shafer theory to represent and combine evidence from several sources. We apply the proposed method to the problem of moving object classification. The method combines information from several lists of moving objects provided by different sensor-based object detectors. The fusion approach includes uncertainty from the reliability of the sensors and their precision to classify specific types of objects. The proposed approach takes into account the instantaneous information at current time and combines it with fused information from previous times. Several experiments were conducted in highway and urban scenarios using a vehicle demonstrator from the interactIVe European project. The obtained results show improvements in the combined classification compared with individual class hypothesis from the individual detector modules