基于CNN和变压器编码器特征融合的反转活塞的新故障诊断方法
Yuehua Lai1,2,3, Ran Li1,2,3,4, Zhuang Ye1,2
1State Recognized Technology Center, Beijing Tianma Intelligent Control Technology Co., Ltd, Beijing, People's Republic of China.
Science progress
|April 17, 2025
概括
一种新方法提高了使用组合卷积神经网络 (CNN) 和变压器编码器的反转活塞的故障诊断. 这种方法可以达到99.145%的准确性,大大改善了现有的在噪音环境中监测条件的技术.
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:
- 反复活塞在煤矿开采中至关重要,但由于噪音较大的地下环境,它们的状态监测和故障诊断具有挑战性.
- 现有的方法遭受不敏感的特征提取和低诊断准确性,需要改进的方法.
研究的目的:
- 通过融合CNN和变压器编码器的特征,提出一种用于互换活塞的新型故障诊断方法.
- 在复杂,杂的操作条件下提高故障诊断的准确性和可靠性.
主要方法:
- 一个多尺度CNN编码器和变压器编码器并行使用,以提取本地和全球信号特征.
- 多尺度卷积模块增强了本地特征多样性,而与曲轴阶段对齐的补丁细分提高了全球特征解释性.
- 功能融合模块集成了本地和全球功能,以进行全面的设备状态表征.
主要成果:
- 拟议的方法在反转活塞的故障诊断任务中实现了99.145%±0.1576%的诊断准确度.
- 这种准确性明显超过现有方法的准确性,证明了卓越的性能.
结论:
- 开发的特征融合方法有效地诊断了反转活塞的故障.
- 该方法提供了一个强大的,高度准确的解决方案,用于在苛刻的工业环境中监测条件.
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