基于对角双矩形阵列的MVDR-LSTM距离估计模型
Xiong Zhang1,2,3, Wenbo Wu3, Jialu Li3
1Hebei Key Laboratory of Electric Machinery Health Maintenance & Failure Prevention, North China Electric Power University, Baoding 071003, China.
Sensors (Basel, Switzerland)
|June 10, 2023
概括
这项研究引入了一种新的方法,用于通过先进的深度学习来估计带式输送机中的滚筒断裂距离. 该技术能够准确地识别出故障位置,即使在杂的工业环境中也是如此.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 带式输送机在工业环境中至关重要,但滚子故障会导致大量停机时间.
- 准确和实时的故障检测对于高效的工业运作至关重要.
- 现有的方法可能会在复杂的结构和长的输送距离方面扎.
研究的目的:
- 开发一种新且准确的方法,用于估计带式输送机中的滚筒断裂距离.
- 解决当前故障检测技术在具有挑战性的工业环境中的局限性.
- 利用深度学习来提高输送系统的预测性维护.
主要方法:
- 采用了对角形双矩形麦克风阵列用于声学数据采集.
- 在信号处理中采用最小变量无扭曲响应 (MVDR) 算法.
- 应用长短期记忆 (LSTM) 网络进行故障距离分类和估计.
主要成果:
- 拟议的MVDR-LSTM方法实现了高精度的故障距离识别.
- 与传统光束成形算法 (CBF) -LSTM和功能光束成形算法 (FBF) -LSTM相比,已经证明了卓越的性能.
- 成功估计了机故障距离,即使在杂的环境中.
结论:
- 开发的方法为带式输送机中滚筒断裂距离估计提供了强大的解决方案.
- 该方法在各种工业测试和监测领域具有很大的应用潜力.
- 深度学习与先进的信号处理相结合,为工业故障诊断提供了一个有前途的途径.
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