旋转机械的故障诊断:基于时间卷积网络和广泛学习系统的高效和轻量级框架
Hao Wei1,2, Qinghua Zhang2, Yu Gu1,2,3
1College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
一个新的轻量级框架,LTCN-IBLS,为旋转机械提供高效的故障诊断. 它实现了高精度和强度,非常适合工业应用.
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:
- 旋转机械在制造业中至关重要,需要可靠的故障诊断以确保运行安全.
- 在复杂的工业环境中,现有的方法可能缺乏效率或稳定性.
研究的目的:
- 提出一种新的,轻量级的框架 (LTCN-IBLS) 进行旋转机械的高效和强大的故障诊断.
- 使用双重轻质时卷积网络 (LTCN) 提取全面的断层特征,并使用增量广泛学习系统 (IBLS) 将其分类.
主要方法:
- 在严格的时间限制下开发了一个整合两个LTCN骨干的框架,用于在严格的时间限制下提取时间频率和时间特征.
- 融合了提取的特征,并使用增量广泛学习系统 (IBLS) 分类器来识别故障.
- 进行了废弃实验,以分析组件贡献,并对多个数据集的最新模型进行性能评估.
主要成果:
- 通过LTCN-IBLS框架,在高平均评估指标 (准确度≥0.9158,MP≥0.9235,MR≥0.9158,MF≥0.9148) 的情况下,表现优越.
- 实现了最低数量的可训练参数 (≤0.0165 Mage),突出了其轻量级的性质.
- 对高斯白噪声表现出强大的稳定性,证实了其在具有挑战性的条件下可靠性.
结论:
- 拟议的LTCN-IBLS框架对于旋转机械故障诊断非常有效和强大.
- 它的轻量级设计和高精度使其适合在工业环境中实际实施.
- 该研究验证了LTCN特征提取和IBLS分类之间的协同作用,用于先进的故障诊断.
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