Development and Analysis of a Distributed Leak Detection and Localisation System for Crude Oil Pipelines

Ahmed, Safuriyawu; Mouël, Frédéric Le; Stouls, Nicolas; Kouyi, Gislain Lipeme

doi:10.3390/s23094298

Cited by 5 publications

(1 citation statement)

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“…IIoT is used for tracking the oil and gas industry operations flow [31] Leak detection in oil pipelines utilizing IIoT -2D CNN -LSTM AE Efficient wireless sensor system for leakage detection in the metallic pipeline [36] Leak In conclusion, the oil and gas industry-based IIoT research papers mainly collect, analyze, and manage data and have not been studied to incorporate a smart alarm. Therefore, creating an extended intelligent IIoT system that uses a TTS model-based neural network to notify staff in oil refineries' control rooms during emergencies is essential.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

A Smart Control System for the Oil Industry Using Text-to-Speech Synthesis Based on IIoT

et al. 2023

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Oil refineries have high operating expenses and are often exposed to increased asset integrity risks and functional failure. Real-time monitoring of their operations has always been critical to ensuring safety and efficiency. We proposed a novel Industrial Internet of Things (IIoT) design that employs a text-to-speech synthesizer (TTS) based on neural networks to build an intelligent extension control system. We enhanced a TTS model to achieve high inference speed by employing HiFi-GAN V3 vocoder in the acoustic model FastSpeech 2. We experimented with our system on a low resources-embedded system in a real-time environment. Moreover, we customized the TTS model to generate two target speakers (female and male) using a small dataset. We performed an ablation analysis by conducting experiments to evaluate the performance of our design (IoT connectivity, memory usage, inference speed, and output speech quality). The results demonstrated that our system Real-Time Factor (RTF) is 6.4 (without deploying the cache mechanism, which is a technique to call the previously synthesized speech sentences in our system memory). Using the cache mechanism, our proposed model successfully runs on a low-resource computational device with real-time speed (RTF equals 0.16, 0.19, and 0.29 when the memory has 250, 500, and 1000 WAV files, respectively). Additionally, applying the cache mechanism has reduced memory usage percentage from 16.3% (for synthesizing a sentence of ten seconds) to 6.3%. Furthermore, according to the objective speech quality evaluation, our TTS model is superior to the baseline TTS model.

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Section: Machine Learning Techniquesmentioning

confidence: 99%