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.

中文翻译：

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使用额外树分类算法和特征选择策略的制造过程智能故障诊断

故障诊断是维护实践中不可或缺的一部分，可确保最佳的机械功能。虽然传统方法依赖于人类专业知识，但在机器学习 (ML) 进步的推动下，智能故障诊断技术现在可以提供自动故障识别。尽管其效率很高，但仍存在研究空白，强调需要不仅提供可靠的故障识别，而且还提供深入的因果因素分析的方法。这项研究引入了一种新颖的方法，使用额外的树分类算法和特征选择来识别制造过程中的故障重要性。与 SVM、神经网络和基于树的 ML 相比，该方法提高了训练和计算效率，在预测和健康管理 2021 数据集上实现了 99% 以上的分类准确率。重要的是，该算法使研究人员能够分析单个故障原因，解决关键的研究空白。该研究为进一步研究提供了指导，旨在完善所提出的策略。这项工作有助于推进故障诊断方法，将自动化与全面的因果分析相结合，这对于学术和工业应用都至关重要。