滚动轴承的多断层分类和诊断基于改进的卷积神经网络
Xiong Zhang1,2, Jialu Li2, Wenbo Wu2
1Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention, Baoding 071003, China.
Entropy (Basel, Switzerland)
|May 27, 2023
概括
这项研究引入了改进的卷积神经网络,用于诊断多个滚动轴承故障,提高了复杂工业条件下的准确性. 该方法有效地使用多类信号对各种轴承故障类型进行分类.
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:
- 现有的滚动轴承故障诊断方法往往忽略了多个同时存在的故障.
- 多重故障和操作条件的存在使诊断复杂化,并降低准确性.
研究的目的:
- 开发一个改进的卷积神经网络 (CNN),用于在滚动轴承中准确的多故障诊断.
- 解决当前处理复杂故障场景的方法的局限性.
主要方法:
- 一个新的CNN架构,采用三层卷积结构.
- 使用了平均聚合和全球平均聚合层,分别取代了共同最大聚合和完全连接层.
- 整合了一个批量规范化 (BN) 层,用于模型优化.
主要成果:
- 拟议的方法证明了轴承故障的有效多重分类.
- 使用XJTU-SY和Paderborn大学实验数据的验证证实了该方法的有效性.
结论:
- 改进的CNN为诊断多个滚动轴承故障提供了强大的解决方案.
- 这种方法提高了在复杂故障条件的实际应用中诊断的准确性.
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