WSN Data Confidence Attribution Using Predictors

Scheffel, Roberto Milton; Frhlich, Antnio A.

doi:10.1109/ladc.2018.00025

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…A confidence level is assigned to each piece of sensed data, before it is used for fault detection. Data confidence attribution is done using ANN predictors introduced by the authors in a previous work (Scheffel and Fröhlich (2018b)). Sensed data are encapsulated as SmartData, transmitted using the FT-TSTP protocol and stored and processed by LISHA IoT Platform.…”

Section: Design Of the Proposed Fault Detectormentioning

confidence: 99%

“…Incorrect readings can lead to wrong decisions if used as input on a control system. In the proposed architecture, each sensor node verifies its own readings through a confidence attribution process (Scheffel and Fröhlich 2018b). An ANN predictor computes the actual expected value using as input its last reading and the readings and confidence levels from a set of correlated sensors.…”

Section: Confidence Attributionmentioning

confidence: 99%

“…Finally, the third parameter δ defines a confidence limit, below which a failure detection algorithm begins to try identifying a faulty sensor. The process also allows the isolation of a sensor failure, causing no interference in other sensors' confidence calculations, as described in Scheffel and Fröhlich (2018b).…”

Section: Confidence Attributionmentioning

confidence: 99%

“…As the confidence attribution was already described in Scheffel and Fröhlich (2018b), this work is concentrated on evaluating the fault detection mechanism to run on the Digital Twin. The dataset obtained by the printings was used to train a Convolutional Neural Network.…”

Section: Implementation Of the Proposed Fault Detectormentioning

confidence: 99%

See 3 more Smart Citations

Automated fault detection for additive manufacturing using vibration sensors

Scheffel

Fröhlich

Silvestri

2021

International Journal of Computer Integrated Manufacturing

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Online process control is a crucial task in modern production systems that use digital twin technology. The data acquisition from machines must provide reliable and on-the-fly data, reflecting the exact status of the ongoing process. This work presents an architecture to acquire data for an Additive Manufacturing (3D printer) process, using a set of consolidated Internet of Things (IoT) technologies to collect, verify and store these data in a trustful and secure way. The need for online monitoring and fault detection is addressed by the development of a classifier using Convolutional Neural Networks. This deep learning approach, using temporally aligned vibration data provided by the underlying architecture, allows raw data processing to detect patterns without signal preprocessing and without domain-specific knowledge for model building.

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Section: Design Of the Proposed Fault Detectormentioning

confidence: 99%

Section: Confidence Attributionmentioning

confidence: 99%

Section: Confidence Attributionmentioning

confidence: 99%

Section: Implementation Of the Proposed Fault Detectormentioning

confidence: 99%

See 2 more Smart Citations

Automated fault detection for additive manufacturing using vibration sensors

Scheffel

Fröhlich

Silvestri

2021

International Journal of Computer Integrated Manufacturing

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“…Every node transmits its sensed value, the predicted value, and the confidence level, to provide information about its current state to the application and to the other correlated nodes. This work is an extension of [1], describing the whole architecture, but only the confidence attribution scheme is completely implemented and evaluated. It extends the study of the parameters' effect on the algorithm's efficiency and makes a verification of how the proposed solution handles different types of errors.…”

Section: Introductionmentioning

confidence: 99%

Increasing sensor reliability through confidence attribution

Scheffel

Fröhlich

2019

J Braz Comput Soc

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The reliability of wireless sensor networks (WSN) is getting increasing importance as this kind of networks are becoming the communication base for many cyber-physical systems (CPS). Such systems rely on sensor data correctness to make decisions; therefore, faulty data can lead such systems to take wrong actions. Errors can be originated by sensor's hardware failures or software bugs and also from the intentional interference of intruders. The gateways that connect such WSN to the Internet are natural intruders' targets as they usually run conventional operating systems and communication protocols. This work proposes a confidence attribution scheme, based on lightweight predictors running on the sensors. The solution also proposes a parameterizable formula, in order to stamp every value sent by a sensor with a confidence level, calculated upon the values of a subset of correlated sensors. This work also presents an algorithm that can identify a defective sensor into its subset. The use of predictors and confidence attribution are proposed as the basis of a mechanism that increases the WSN resilience against sensor failure or bad data injection by intruders. Several simulations were performed to evaluate the detection efficiency against different types of sensor errors. This work also analyses mechanisms to deal with concept drifts in the WSN lifetime.

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SmartData Safety: Online Safety Models for Data-Driven Cyber-Physical Systems

Hoffmann

Fröhlich

2022

IECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society

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WSN Data Confidence Attribution Using Predictors

Cited by 7 publications

References 20 publications

Automated fault detection for additive manufacturing using vibration sensors

Automated fault detection for additive manufacturing using vibration sensors

Increasing sensor reliability through confidence attribution

SmartData Safety: Online Safety Models for Data-Driven Cyber-Physical Systems

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