在复杂的噪音环境中驱动火炮装载系统发动机的故障诊断方法
Wenkuan Huang1, Yong Li1, Jinsong Tang1
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
这项研究引入了对大炮驱动电机的先进故障诊断模型,提高了噪音环境中的信号质量. 新方法显著提高了检测电机故障的准确性,这对于军事设备的可靠性至关重要.
科学领域:
- 电气工程 电气工程
- 机械工程 机械工程
- 信号处理 信号处理
背景情况:
- 现代火炮依赖于电动驱动技术,使得电机故障监测至关重要.
- 在复杂的火炮环境中,传感器信号容易受到严重的噪音干扰.
- 现有的方法很难获得高质量的电机信号来准确诊断故障.
研究的目的:
- 开发一个强大的故障诊断模型,用于大炮驱动电机在噪音条件下.
- 为了应对由于复杂的电磁干扰而导致的信号退化的挑战.
- 提高关键军事系统故障检测的可靠性和准确性.
主要方法:
- 利用波浪噪声降低网络,并优化了聚变波浪基和软值.
- 采用卷积神经网络 (CNN) 来进行端到端的故障分类.
- 将CNN集成到AdaBoost框架中,并使用注意力机制来进行增强的诊断.
主要成果:
- 拟议的模型在不同的信号与噪声比率 (SNRs) 中实现了92%的平均准确性.
- 与传统故障诊断方法相比,表现出超过8.5%的性能改善.
- 在严重的噪音干扰条件下成功恢复和诊断了电机信号.
结论:
- 开发的故障诊断模型在复杂的火炮环境中有效处理噪音信号.
- 波形无声化,CNN,AdaBoost和注意力机制的组合提供了卓越的性能.
- 这种方法提高了电动驱动系统在现代军事应用中的可靠性.
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