Generic Sensor Failure Modeling for Cooperative Systems

Jäger, Georg; Zug, Sebastian; Casimiro, António

doi:10.3390/s18030925

Cited by 12 publications

(5 citation statements)

References 44 publications

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“…Thus, distributed sensor failures, such as breaking of the optic fiber, were not modeled, but could be easily inferred. The sensor failures that were considered in this work were partial failures [ 55 , 56 , 57 , 58 , 59 ]: Bias. Drifting.…”

Section: Methodsmentioning

confidence: 99%

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Hembert,

Ghnatios,

Cotton

et al. 2024

Sensors

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A deep geological repository for radioactive waste, such as Andra’s Cigéo project, requires long-term (persistent) monitoring. To achieve this goal, data from a network of sensors are acquired. This network is subject to deterioration over time due to environmental effects (radioactivity, mechanical deterioration of the cell, etc.), and it is paramount to assess each sensor’s integrity and ensure data consistency to enable the precise monitoring of the facilities. Graph neural networks (GNNs) are suitable for detecting faulty sensors in complex networks because they accurately depict physical phenomena that occur in a system and take the sensor network’s local structure into consideration in the predictions. In this work, we leveraged the availability of the experimental data acquired in Andra’s Underground Research Laboratory (URL) to train a graph neural network for the assessment of data integrity. The experiment considered in this work emulated the thermal loading of a high-level waste (HLW) demonstrator cell (i.e., the heating of the containment cell by nuclear waste). Using real experiment data acquired in Andra’s URL in a deep geological layer was one of the novelties of this work. The used model was a GNN that inputted the temperature field from the sensors (at the current and past steps) and returned the state of each individual sensor, i.e., faulty or not. The other novelty of this work lay in the application of the GraphSAGE model which was modified with elements of the Graph Net framework to detect faulty sensors, with up to half of the sensors in the network being faulty at once. This proportion of faulty sensors was explained by the use of distributed sensors (optic fiber) and the environmental effects on the cell. The GNNs trained on the experimental data were ultimately compared against other standard classification methods (thresholding, artificial neural networks, etc.), which demonstrated their effectiveness in the assessment of data integrity.

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Section: Methodsmentioning

confidence: 99%

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Hembert,

Ghnatios,

Cotton

et al. 2024

Sensors

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“…Therefore, Sc ST includes two parts: the normal operation part and the failure part. Based on equation (4), the failure data subcube is a quadruple shown in (6).…”

Section: A Failure Distribution Characteristics By System Identification and Timementioning

confidence: 99%

“…Zheng et al [5] collected the fault recording from one converter station when the communication failed due to a disturbance in the AC system and calculated and statistically analyzed the turn-off angle of valves, zero-crossing offset, and landing amplitude of bus voltage. Jager et al [6] proposed an integration step that evaluates the failure model of shared information in relation to an application's fault tolerance and presented a mathematically defined generic failure model as well as a processing chain for automatically extracting failure models from empirical data. The work in [7] proposed a risk index system for the catenary lines of highspeed railways considering the characteristics of time-space differences to represent and quantify the characteristics of risk.…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Failure Characteristics and Reliability Analysis for Train Control On-Board Subsystem

et al. 2019

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The train control on-board subsystem (TC-OBS) plays an important role in the safety and efficiency of the high-speed train's operation. Therefore, there is an urgent demand for the analysis of failure characteristics and the reliability of TC-OBS. In this paper, a specific data model is built for the TC-OBS operational and failure data based on data cubes. This model analyzed the failure distribution characteristics of TC-OBS from the combined angles of System Identification, Time and Operation Attribute through the operations of data cubes. Thus, the representative units and systems can be the research objects of the reliability evaluation. With these representative units and systems, this paper uses Bayesian estimation combined with Markov Chain Monte Carlo (MCMC) to estimate the parameters of the time between failures (TBF) distribution model and the reliability is analyzed. Simulation results show that the data model based on data cubes can offer an efficient and convenient method to analyze the failure characteristics and reliability of TC-OBS.INDEX TERMS Train control system, reliability, failure distribution characteristics, data modeling.

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“…While not considered in this early phase, failure modeling [20] and detection will be applied to enable failure-aware applications and to further mitigate the effects of sensor failures. Run-time approaches, such as the validity concept, were successfully applied to depth measurements of RGB-D sensors as well as to low-level distance sensors [21] and are specifically design to track sensor failures while propagating through a processing chain.…”

Section: Current State and Future Directionmentioning

confidence: 99%

Towards Robot-Centric Conceptual Knowledge Acquisition

Jäger¹,

Mueller²,

Thosar³

et al. 2018

Preprint

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Robots require knowledge about objects in order to efficiently perform various household tasks involving objects. The existing knowledge bases for robots acquire symbolic knowledge about objects from manuallycoded external common sense knowledge bases such as ConceptNet, Word-Net etc. The problem with such approaches is the discrepancy between human-centric symbolic knowledge and robot-centric object perception due to its limited perception capabilities. Ultimately, significant portion of knowledge in the knowledge base remains ungrounded into robot's perception. To overcome this discrepancy, we propose an approach to enable robots to generate robot-centric symbolic knowledge about objects from their own sensory data, thus, allowing them to assemble their own conceptual understanding of objects. With this goal in mind, the presented paper elaborates on the work-in-progress of the proposed approach followed by the preliminary results. MotivationBaber [1] postulated that a deliberation for tool selection in humans or animals alike is facilitated by conceptual knowledge about objects, especially, knowledge about their physical and functional properties and relationship between them. The conceptual knowledge about household objects is desired in a service robot too when performing various household tasks, for instance, selecting an appropriate tool for a given task, selecting a substitute for a missing tool required in some task or action selection for using objects. Since the demand for such conceptual knowledge about objects has been increasing, the development of such knowledge bases has been undertaken by the researchers around the world.

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Generic Sensor Failure Modeling for Cooperative Systems

Cited by 12 publications

References 44 publications

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Assessing Sensor Integrity for Nuclear Waste Monitoring Using Graph Neural Networks

Data-Driven Failure Characteristics and Reliability Analysis for Train Control On-Board Subsystem

Towards Robot-Centric Conceptual Knowledge Acquisition

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