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现有的电机故障分类方法大多采用单一物理量来识别故障，这使得故障诊断的准确性较低。文章详细介绍信息融合的概念，并采用主成分分析(PCA)和区间映射法解决了数据维度高和不均衡的问题。进一步地，提出一种基于多源信息融合机器学习算法的电机故障分类方法进行故障分类，并通过所提出的方法对故障数据进行验证，最终将分类结果的准确率提高至99.0%,展现了其在电机故障诊断中的有效性和实用性。
Abstract：Most existing motor fault classification methods use a single physical quality to identify faults, which makes the accuracy of fault diagnosis relatively low. In this paper, the concept of information fusion is introduced in detail, and principal component analysis(PCA) and interval mapping methods are applied to solve high dimension and unbalance data issues. Furthermore, a motor fault classification method based on multi-source information fusion machine learning algorithm is proposed to classify faults, and applying the proposed method verifies fault data, the accuracy rate of the classification results is improved to more than 99.0%, showing its effectiveness and practicability in motor fault diagnosis.
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基本信息:
中图分类号:TM307;TP181
引用信息:
[1]翟继光,宋琛年,滕以坤,等.基于多源信息融合机器学习算法的电机故障分类[J].微型电脑应用,2026,42(04):174-179.
2026-04-20
2026-04-20