一种混合特征选择和多标签驱动的智能故障诊断方法,用于变速箱
Di Liu1, Xiangfeng Zhang1, Zhiyu Zhang1
1College of Intelligent Manufacturing and Industrial Modernization, Xinjiang University, Urumchi 830017, China.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
一个新的特征选择框架通过识别最佳数据子集来改善变速箱故障诊断. 这种方法提高了复杂机械故障的分类准确性,减少了财务损失.
科学领域:
- 机械工程 机械工程
- 数据科学数据科学数据科学
- 人工智能的人工智能
背景情况:
- 变速箱故障会造成巨大的财务损失,因为它们在机械中起着至关重要的作用.
- 在高维数据中诊断复合故障仍然具有挑战性,尽管数据驱动方法的进步.
研究的目的:
- 开发一个特征选择和故障脱框架,用于增强变速箱复合故障诊断.
- 自动从高维数据中确定最佳特征子集,以提高诊断性能.
主要方法:
- 一个混合的,三阶段的特征选择框架,结合了过器模型 (费舍尔分数,信息获取,皮尔森相关性),一个加权平均方案与遗传算法优化,和启发式搜索策略 (二进制,顺序前向/后向搜索).
- 使用多标签的K-最近邻居 (ML-kNN) 作为选择最佳特征子集的分类器.
- 该框架考虑了特征不相关性,冗余性和特征间的相互作用.
主要成果:
- 拟议的方法实现了高诊断准确性,ML-kNN在两个变速箱复合故障数据集上达到96.22%和100%的准确性.
- 选择的最佳特征子集与现有方法相比,表现出优越的性能.
- 该框架有效地预测了复合故障样本的多个标签,使故障识别和分离成为可能.
结论:
- 开发的特征选择框架显著提高了变速箱复合故障诊断的准确性,并减少了特征维度.
- 该方法为识别和解高维数据集中的复杂故障提供了有效的解决方案.
- 这种方法有望提高关键机械的可靠性并降低维护成本.
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