一种新型智能故障诊断方法,用于采用多源数据和改进GASA的轴承
Qingming Hu1,2,3, Xinjie Fu1, Yanqi Guan1
1School of Mechanical and Electrical Engineering, Qiqihar University, Qiqihar 161006, China.
Sensors (Basel, Switzerland)
|August 29, 2024
概括
这项研究引入了一种新的基因模拟回火优化 (GASA) 技术,使用多源数据卷积神经网络 (MSCNN) 进行增强的滚动轴承故障诊断. 该方法的准确性超过98%,即使在杂的环境中,也能显著改善故障检测.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 单一来源的深度学习在故障诊断方面显示出前景.
- 从多来源数据中提取用于故障诊断的信息仍然具有挑战性.
- 滚动轴承故障诊断需要准确和强大的方法.
研究的目的:
- 提出一种利用多来源数据的新型故障诊断方法.
- 为了提高滚动轴承故障诊断的准确性和稳定性.
- 通过多源数据卷积神经网络 (MSCNN) 利用基因模拟化优化 (GASA).
主要方法:
- 集成的多源数据 (振动和电机电流信号).
- 使用连续波形变换 (CWT) 将振动信号转换为时间频率图.
- 开发了一个GASA-MSCNN模型用于特征提取和故障分类.
主要成果:
- 该GASA-MSCNN模型在识别各种滚动轴承故障方面取得了高准确性 (≥98%).
- 拟议的方法在可变噪声环境中显示出强度.
- 与现有方法相比,观察到故障检测准确度的显著改善.
结论:
- 该GASA-MSCNN方法有效地利用多源数据进行准确的滚动轴承故障诊断.
- 该方法为故障检测提供了强大的解决方案,即使在噪音条件下也是如此.
- 这种方法代表了智能故障诊断系统的重大进步.
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