多维快速非线性盲解卷网络用于轴承复合材料的特征提取
Hao Ma1, Baokun Han1, Qingyao Zhang1
1College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266000, China.
ISA transactions
|October 8, 2025
概括
一种新的方法,多维快速非线性盲解卷网络 (MFNBD-net),有效地分离和提取复合故障特征. 这种技术克服了弱故障信号和干扰带来的挑战,改善了机器诊断.
科学领域:
- 机械工程 机械工程
- 信号处理 信号处理
- 状态监控 状态监控
背景情况:
- 单个轴承故障可能会因为不均的应力和异常负荷而导致新的故障.
- 复合故障中的弱故障特征通常被现有的故障标志或干扰掩盖.
- 准确识别复合故障对于防止机器发生灾难性故障至关重要.
研究的目的:
- 开发一种用于分离和提取复合故障特征的新方法.
- 在复杂的故障场景中解决弱和别名故障签名的挑战.
- 提高旋转机械故障诊断的稳定性和准确性.
主要方法:
- 将快速非线性盲解卷 (FNBD) 扩展到多维FNBD (MFNBD),用于复合特征解.
- 引入统一的多维初始化以指导多特征提取趋同.
- 基于和分布的剪切包膜光谱曲解的开发,以消除无关的组件.
- 将自适应非线性转换和过波形惩罚纳入MFNBD-net框架.
主要成果:
- 与现有方法相比,MFNBD-net在多维特征脱方面表现优异.
- 提出的方法有效地从复杂干扰中分离和提取弱故障特征.
- 模拟和实验结果验证了MFNBD-net.net的稳定性和准确性.
结论:
- 在条件监测中,MFNBD-net是提取复合故障特征的有希望的工具.
- 该方法在处理具有多个同时问题的复杂故障场景时提供了显著的优势.
- 这一进步有助于更可靠,更有效的机械故障诊断.
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