Taxonomy, challenges, and future directions for AI-driven industrial cooling systems

Abstract

The efficiency and reliability of industrial cooling systems are critical for sectors such as energy systems, electronics manufacturing, and data centers. Traditional cooling systems rely on reactive maintenance, leading to increased downtime, energy consumption, and operating costs. Recent advances in artificial intelligence (AI), including machine learning (ML), deep learning (DL), and physics-informed neural networks (PINNs), have enabled proactive fault diagnosis and predictive maintenance in industrial cooling systems, significantly reducing energy use and improving operational reliability. However, current AI applications face challenges, such as limited access to quality datasets, computational complexity, integration with legacy systems, and model scalability. This paper systematically addresses these gaps by providing a detailed taxonomy of AI-driven cooling system diagnostics, categorizing state-of-the-art methods, and identifying critical research challenges. Our main contribution is a structured taxonomy that integrates ML, DL, and PINNs, offering a clear framework for analyzing current practices and potential improvements. The paper highlights critical insights across 138 reviewed studies, emphasizing the transformative role of hybrid AI frameworks in diagnostics, including use cases in HVAC, data centers, and thermal imaging. Notably, the integration of ML, DL, and PINNs has been shown to improve fault detection accuracy, energy efficiency, and model interpretability, paving the way for scalable, real-time deployments.