article · Signals
Industrial fault diagnosis ensures system reliability and reduces maintenance costs. Traditional approaches depend on human intelligence, using expertise and contextual reasoning, but modern machinery produces volumes of data that limit the scalability of human inspection. Artificial intelligence processes large datasets effectively and improves diagnostic accuracy. However, artificial intelligence models struggle with high deployment costs, data dependency, and a lack of explainability. Human experts remain vital for interpreting novel, rare, or ambiguous fault states requiring contextual judgment. Combining both capabilities through augmented intelligence offers a collaborative pathway that unites the computational power of algorithms with human flexibility. Future intelligent diagnostic architectures will likely incorporate emerging methods such as explainable systems, foundation models, physics-informed networks, and digital twins to build reliable, human-centred diagnostic processes for industrial machinery.
Industrial equipment breakdowns create expensive downtime and maintenance challenges. As modern machinery becomes faster and more complex, automated systems cannot fully replace experienced technicians, nor can technicians track massive operational data streams unassisted. Moving toward augmented intelligence ensures industrial operations maintain high diagnostic accuracy while retaining human oversight, helping organisations identify machinery faults earlier, prevent catastrophic failures, and operate more dependable manufacturing facilities.
The findings highlight an application pathway for industrial plant operators and maintenance software developers seeking combined human-machine monitoring tools. By pairing machine learning algorithms with human supervisory interfaces, diagnostic tools can better address complex machinery faults. However, as this study is a literature-based review identifying emerging directions like digital twins and foundation models, the concepts remain at an early, conceptual stage of development rather than representing an applied or market-ready product.
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Fault diagnosis in industrial equipment plays a crucial role in ensuring reliable system functionality and minimizing the costs associated with repair and maintenance. Traditionally, fault diagnosis has relied on human intelligence (HI), with skilled personnel applying knowledge and expertise based on reasoning and contextual understanding. Nevertheless, as modern industrial systems have grown more complex, operated at higher speeds, and generated massive amounts of data, relying solely on HI has become increasingly challenging and less scalable. Consequently, modern fault diagnosis has turned toward artificial intelligence (AI). This paper presents a comparative study of HI and AI in industrial fault diagnosis, based on a literature-driven analysis. The results confirm that while AI-based fault diagnosis systems perform well in processing large datasets and achieve improved diagnostic accuracy, these practices also face limitations related to data dependency, explainability, and deployment cost. By contrast, human intelligence remains indispensable in handling uncertain, rare, or new fault conditions that require contextual judgment and flexibility. The review further indicates that augmented intelligence (AuI) provides a collaborative framework that combines the complementary strengths of HI and AI for industrial fault diagnosis. Furthermore, emerging research directions, such as explainable and trustworthy AI, foundation models, large language models, physics-informed AI, digital twins, and human-centered AI, are identified as promising developments for next-generation intelligent diagnostic systems. The findings suggest that augmented intelligence is the most promising approach for advancing the performance and reliability of diagnostic systems in industrial machines.
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DOI: 10.3390/signals7040082
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