Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment

Hybrid Machine Learning-Based Fault-Tolerant Sensor Data Fusion and Anomaly Detection for Fire Risk Mitigation in IIoT Environment

In the oil and gas IIoT environment, fire detection systems heavily depend on fire sensor data, which can be prone to inaccuracies due to faulty or unreliable sensors. These sensor issues, such as noise, missing values, outliers, sensor drift, and faulty readings, can lead to delayed or missed fire...

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Bibliographic Details
Main Authors: Jayameena Desikan, Sushil Kumar Singh, A. Jayanthiladevi, Shashi Bhushan, Vinay Rishiwal, Manish Kumar
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Sensors
Subjects:
IIoT
faulty sensors
anomaly detection
Dempster–Shafer
machine learning
sensor fusion
Online Access:https://www.mdpi.com/1424-8220/25/7/2146
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