用机器学习预测电机健康状态，减少意外停机。
Predictive Maintenance of Electric Motors Using Supervised Learning Models: A Comparative Analysis
- 基于电机运行特征训练多种监督学习模型
- 梯度提升模型准确率最高，达94.7%
- 适合工业设备运维人员参考应用
预测性维护是保障工业系统可靠性和效率的关键策略。本研究探讨了使用监督学习模型诊断电动机状态，将其分类为“健康”、“需预防性维护（PM）”或“故障”。利用电机运行的关键特征训练了多种机器学习算法，包括朴素贝叶斯、支持向量机（SVM）、回归模型、随机森林、k-最近邻（k-NN）和梯度提升技术。评估各模型性能以识别最有效的分类器。结果显示模型间准确率差异显著，其中梯度提升模型表现最佳。研究证实了监督学习在电动机诊断中的实用性，为高效维护调度和减少工业应用中的非计划停机提供了基础。
原文摘要 · Abstract (English)
Predictive maintenance is a key strategy for ensuring the reliability and efficiency of industrial systems. This study investigates the use of supervised learning models to diagnose the condition of electric motors, categorizing them as "Healthy," "Needs Preventive Maintenance (PM)," or "Broken." Key features of motor operation were employed to train various machine learning algorithms, including Naive Bayes, Support Vector Machines (SVM), Regression models, Random Forest, k-Nearest Neighbors (k-NN), and Gradient Boosting techniques. The performance of these models was evaluated to identify the most effective classifier for predicting motor health. Results showed notable differences in accuracy among the models, with one emerging as the best-performing solution. This study underscores the practicality of using supervised learning for electric motor diagnostics, providing a foundation for efficient maintenance scheduling and minimizing unplanned downtimes in industrial applications.
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