使用Mel光谱图和卷积神经网络对电机进行基于声学的故障诊断
Hasan Uzel1, Yıldırım Özüpak2, Feyyaz Alpsalaz1
1Akdağmadeni Vocational School, Yozgat Bozok University, Yozgat, Turkey.
Scientific reports
|December 23, 2025
概括
这项研究引入了使用Mel光谱图和轻型卷积神经网络 (CNN) 进行电机故障诊断的深度学习框架. 该模型在分类电机状态时达到99.7%的准确性,使实时预测性维护成为可能.
科学领域:
- 工程 工程师 工程师 工程师
- 人工智能的人工智能
- 声学 声学 在声学方面
背景情况:
- 电机故障会导致大量的工业停机时间和成本.
- 早期检测声学故障对于预测性维护至关重要.
- 现有的诊断方法可能缺乏准确性,稳定性或实时功能.
研究的目的:
- 开发一个深度学习框架,用于准确和高效的电机声学故障诊断.
- 使用音频录音来分类电机状态 (好,坏,重负荷).
- 确保对拟议的诊断模型进行可靠的评估.
主要方法:
- 使用来自音频录音的Mel光谱图.
- 采用轻量级卷积神经网络 (CNN) 架构.
- 实施严格的评估技术,包括文件级分割和5倍交叉验证.
主要成果:
- 达到99.7%的测试准确度,超过了最先进的方法.
- 在不同的信号噪声比 (SNR) 条件下,已证明噪声稳定性和稳定的性能.
- 确定低频声学特征对于故障检测至关重要.
结论:
- 拟议的深度学习框架为实时电机故障诊断提供了高度准确,可解释和高效的解决方案.
- 该模型显示了嵌入式系统在工业预测维护中的强大的适应性和可行性.
- 未来的研究可以探索转移学习和多式联络融合,以改善诊断.
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