| Summary: | Fault diagnosis is integral to maintenance practices, ensuring optimal machinery functionality. While traditional methods relied on human expertise, intelligent fault diagnosis techniques, propelled by machine learning (ML) advancements, now offer automated fault identification. Despite their efficiency, a research gap exists, emphasizing the need for methods providing not just reliable fault identification but also in-depth causal factor analysis. This research introduces a novel approach using an extra tree classification algorithm and feature selection to identify fault importance in manufacturing processes. Compared with SVM, neural networks, and tree-based ML, the method enhances training and computational efficiency, achieving over 99% classification accuracy on prognostics and health management 2021 dataset. Importantly, the algorithm enables researchers to analyze individual fault causes, addressing a critical research gap. The study provides guidelines for further research, aiming to refine the proposed strategy. This work contributes to advancing fault diagnosis methodologies, combining automation with comprehensive causal analysis, crucial for both academic and industrial applications.
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