Research on Online Fault Diagnosis Model Based on IoT in Process Industry

Deng, Ju Li; Chen, Guo Rong

doi:10.4028/www.scientific.net/amr.765-767.2089

Cited by 2 publications

(1 citation statement)

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“…Du et al, proposed an IoT-based real-time monitoring solution for the presence of toxic gas in an oil depository via a GSM platform [12]. An online fault diagnosis model for the process industry is presented by the authors of reference [13] that includes multiple information collection and knowledge sharing points using IoT. Nivedhitha et al, presented a smart smoke and LPG detection system using the Texas EZ430-RF2500 (http://www.ti.com/lit/ug/slau227e/slau227e.pdf, accessed on 12 September 2012) wireless module [14].…”

Section: Introductionmentioning

confidence: 99%

AI-Enabled IoT Framework for Leakage Detection and Its Consequence Prediction during External Transportation of LPG

Dash,

Bandopadhay,

Samal

et al. 2023

Sensors

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An accident during the transport of liquefied petroleum gas (LPG) via a tanker vehicle leads to the leakage of a flammable substance, causing devastation. In such a situation, the appropriate action with the shortest possible delay can minimize subsequent losses. However, the decision-making mechanism remains unable to detect the occurrence of an accident and evaluate its extent within the critical time. This paper proposes an automatic framework for leakage detection and its consequence prediction during the external transportation of LPG using artificial intelligence (AI) and the internet of things (IoT). An AI model is developed to predict the probable consequences of the accident in terms of the diameter of risk contours. An IoT framework is proposed in which the developed AI model is deployed in the edge device to detect any leakage of gas during transportation, to predict its probable consequences, and to report it to the remotely located disaster management team for initiating appropriate action. A prototype of the proposed model is built and its performance is successfully tested. The proposed solution would significantly help to identify efficient disaster management techniques by allowing for quick leakage detection and the prediction of its probable consequences.

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Section: Introductionmentioning

confidence: 99%