基于改进的LTSS-BoW模型和囊网络的行星变速箱的复合故障诊断
Guoyan Li1,2, Liyu He1, Yulin Ren1
1Key Laboratory of Advance Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了LTSS-BoW-CapsNet用于行星变速箱复合故障诊断. 这种新的方法有效地识别了多个故障组件,准确度超过97%,提高了机械设备的安全性.
科学领域:
- 机械工程 机械工程
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 行星变速箱对于机械设备的安全至关重要.
- 现有的深度学习方法在缺乏复合故障数据和单标签分类方面扎.
- 准确的复合故障识别对于预测性维护至关重要.
研究的目的:
- 为行星变速箱的智能复合故障识别提出一种新的深度学习模型.
- 为了解决当前方法中缺陷样本和单标签分类不足的局限性.
- 通过改进故障诊断,提高机械设备的安全性和可靠性.
主要方法:
- 开发了一个特征提取器,结合了局部时间自我相似性 (LTSS) 和袋词 (BoW) 模型 (LTSS-BoW).
- 设计了一个基于囊网络 (CapsNet) 的多标签分类器,包含动态路由和平均值.
- 拟议的LTSS-BoW-CapsNet模型在三个复合故障诊断任务上得到了验证.
主要成果:
- 通过LTSS-BoW-CapsNet方法,在各种复合故障模式中有效地解和识别多故障组件.
- 在复合故障诊断任务上实现了超过97%的测试准确性.
- 在准确性和有效性方面表现优于四种传统分类模型.
结论:
- 拟议的LTSS-BoW-CapsNet为行星变速箱的智能复合故障识别提供了一个强大的解决方案.
- 该方法成功地克服了有限数据和单一标签分类的挑战.
- 这一进步有助于提高机械设备的安全性和运营效率.
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