自然模态素描网络:用于轴承冲动特征提取的可解释方法
IEEE transactions on cybernetics
|March 3, 2025
概括
一个新的自然模态素描网络 (NMSNet) 从杂的滚动轴承数据中稳定地提取冲动特征 (IF). 这种可解释的深度学习方法提高了故障诊断的可信度.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 人工智能的人工智能
背景情况:
- 冲动特征 (IF) 响应对于滚动轴承故障诊断至关重要.
- 在现实场景中,由于强烈的噪音,提取IF具有挑战性.
- 目前的深度学习方法缺乏可解释性和可信性.
研究的目的:
- 开发一种可靠和可信的方法来进行轴承IF提取.
- 引入自然模态素描网络 (NMSNet) 以加强故障诊断.
- 提高特征提取中的深度学习的可解释性和可靠性.
主要方法:
- 构建了NMSNet,使用模态响应作为卷积内核.
- 将向前传播解释为自然的模态素描 (恢复和加权叠加).
- 开发了一种用于理论解释和消除噪音的新校正算法.
- 使用加权的融合和训练约束实现了自适应的模态素描.
主要成果:
- 在模拟和实验中,NMSNet证明了有效和耐噪声的IF提取.
- 该方法实现了自适应模态素描.
- 与错误相关的解释性分析证实了知识的获取.
结论:
- NMSNet为轴承IF提取提供了一个可信和强大的解决方案.
- 网络的可解释逻辑,来自故障机制,提高了诊断可靠性.
- 在故障诊断方面,NMSNet克服了传统深度学习的局限性.
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