Integrating HSE Governance with Intelligent Leakage Detection Systems for Sustainable Industrial Development in Saudi Arabia
Year: 2026
Type: article
Abstract: Saudi Arabia’s industrial expansion under Vision 2030 increases the strategic importance of preventing loss of containment across oil and gas, petrochemical, water, utilities, and infrastructure assets. This review examines how health, safety, and environment (HSE) governance can be integrated with intelligent leakage detection so that sensing technologies become part of accountable risk management rather than isolated technical tools. An integrative review of peer-reviewed literature published during 2020–2025 synthesizes evidence on acoustic sensing, pressure and flow analytics, Industrial Internet of Things (IIoT), machine and deep learning, digital twins, risk-based inspection, dynamic risk analysis, human factors, and safety governance. The synthesis shows that intelligent systems can improve detection speed, localization, and predictive integrity assessment, but technical accuracy alone does not ensure safer outcomes. Governance quality determines whether alerts are validated, escalated, translated into barrier actions, documented, and learned from. The paper proposes an HSE-integrated architecture linking sensor assurance, contextual analytics, dynamic risk ranking, human authorization, emergency response, maintenance, and sustainability metrics. It argues that Saudi industrial operators should treat leakage intelligence as a safety-critical decision system with defined ownership, cybersecurity controls, model assurance, and lifecycle verification. Research priorities include Saudi field datasets, explainable and uncertainty-aware models, cross-asset interoperability, human–AI decision protocols, and evidence connecting intelligent detection to measurable reductions in environmental releases and major-accident risk.
Author: Muhammad Ashfaq
Language: English
Topic: Industrial and Mining Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7222465807
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