Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27230%2F23%3A10252904" target="_blank" >RIV/61989100:27230/23:10252904 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/1424-8220/23/14/6266" target="_blank" >https://www.mdpi.com/1424-8220/23/14/6266</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/s23146266" target="_blank" >10.3390/s23146266</a>

Result language

angličtina

Original language name

Integrated Edge Deployable Fault Diagnostic Algorithm for the Internet of Things (IoT): A Methane Sensing Application

Original language description

The Internet of Things (IoT) is seen as the most viable solution for real-time monitoring applications. But the faults occurring at the perception layer are prone to misleading the data driven system and consume higher bandwidth and power. Thus, the goal of this effort is to provide an edge deployable sensor-fault detection and identification algorithm to reduce the detection, identification, and repair time, save network bandwidth and decrease the computational stress over the Cloud. Towards this, an integrated algorithm is formulated to detect fault at source and to identify the root cause element(s), based on Random Forest (RF) and Fault Tree Analysis (FTA). The RF classifier is employed to detect the fault, while the FTA is utilized to identify the source. A Methane (CH4) sensing application is used as a case-study to test the proposed system in practice. We used data from a healthy CH4 sensing node, which was injected with different forms of faults, such as sensor module faults, processor module faults and communication module faults, to assess the proposed model&apos;s performance. The proposed integrated algorithm provides better algorithm-complexity, execution time and accuracy when compared to FTA or standalone classifiers such as RF, Support Vector Machine (SVM) or K-nearest Neighbor (KNN). Metrics such as Accuracy, True Positive Rate (TPR), Matthews Correlation Coefficient (MCC), False Negative Rate (FNR), Precision and F1-score are used to rank the proposed methodology. From the field experiment, RF produced 97.27% accuracy and outperformed both SVM and KNN. Also, the suggested integrated methodology&apos;s experimental findings demonstrated a 27.73% reduced execution time with correct fault-source and less computational resource, compared to traditional FTA-detection methodology.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

<a href="/en/project/EF16_019%2F0000867" target="_blank" >EF16_019/0000867: Research Centre of Advanced Mechatronic Systems</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Sensors

ISSN

1424-3210

e-ISSN

1424-8220

Volume of the periodical

23

Issue of the periodical within the volume

14

Country of publishing house

CH - SWITZERLAND

Number of pages

20

Pages from-to

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UT code for WoS article

001036707500001

EID of the result in the Scopus database

2-s2.0-85165970763