Intelligent Sensor Fusion for Enhanced Industrial Process Monitoring
Keywords:
sensor fusion, industrial process monitoring, artificial intelligence, cyber-physical systems, predictive maintenance, data governance, robustness, sustainabilityAbstract
The increasing complexity and digitalisation of industrial processes demand more sophisticated monitoring techniques that can synthesise heterogeneous data streams into coherent, actionable insights. Intelligent sensor fusion, which integrates data from multiple sensing modalities through artificial intelligence and machine learning algorithms, offers a transformative approach to process monitoring in domains such as manufacturing, energy production, and chemical processing. This paper presents a comprehensive systems-level examination of intelligent sensor fusion for industrial applications, focusing on architectural trade-offs, governance considerations, robustness requirements, and sustainability implications. We begin by establishing the conceptual foundations of sensor fusion, distinguishing between data-level, feature-level, and decision-level paradigms. Subsequently, we analyse the architectural layers that underpin modern fusion systems, including edge computing, cloud infrastructure, and communication protocols, highlighting the tension between latency, bandwidth, and centralisation. The role of artificial intelligence is scrutinised, with particular attention to deep learning models, probabilistic frameworks, and the challenges of explainability and domain adaptation in safety-critical environments. We then explore the governance landscape, addressing data provenance, algorithmic bias, security vulnerabilities, and the regulatory frameworks that must evolve to govern autonomous decision-making derived from fused sensor data. Deployment and sustainability are examined through the lens of energy consumption, lifecycle management, and retrofitting legacy systems. Comparative case illustrations from discrete manufacturing, continuous processes, and energy grids ground the discussion in practical realities. Finally, we outline future directions including federated learning, digital twins, and standardisation efforts. The paper argues that while intelligent sensor fusion significantly enhances monitoring capabilities, its successful deployment requires careful orchestration of technical, organisational, and policy dimensions to ensure reliable, fair, and sustainable industrial intelligence.
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