来自无刷直流电机的音频信号数据集,用于预测性维护
Rommel Stiward Prieto Estacio1, Diego Alberto Bravo Montenegro1, Carlos Felipe Rengifo Rodas1
1Universidad del Cauca, Calle 5 Nro. 4-70, Popayán (Cauca), Colombia.
Data in brief
|October 2, 2023
概括
这项研究表明,音频分析如何检测用于预测性维护 (PdM) 的无刷直流 (BLDC) 电机的故障. 分析运动声音有助于开发机器学习模型,以防止故障并降低工业成本.
科学领域:
- 工程 工程师 工程师 工程师
- 工业物联网工业物联网工业物联网
- 声学 声学 在声学方面
背景情况:
- 预测性维护 (PdM) 对第四次工业革命至关重要,旨在防止系统故障并降低成本.
- 无刷直流电机 (BLDC) 在工业应用中越来越多地使用,需要有效的PdM策略.
- 音频分析为监测BLDC运动健康和检测异常提供了一个务实的方法.
研究的目的:
- 在各种故障模式下描述BLDC电机的基于声音的行为.
- 为研究人员提供数据集,为PdM开发信号处理和机器学习应用程序.
- 通过声学分析增强预测性维护和工业4.0的能力.
主要方法:
- 对BLDC电机进行实验,以诱导和记录不同的故障模式.
- 使用音频分析技术来捕获和处理电机声音数据.
- 收集来自哥伦比亚考卡大学 (Universidad del Cauca) 研究人员进行的一项实验的数据.
主要成果:
- 在BLDC电机中的特定声音模式和不同的故障模式之间建立了相关性.
- 为BLDC电机故障生成了有价值的声学特征数据集.
- 展示了声音分析对实时故障检测的潜力.
结论:
- 音频分析是一种可行的和有效的方法,用于BLDC电机的预测性维护.
- 收集的数据集可以为工业应用的机器学习模型的进步做出重大贡献.
- 这项研究支持将声监测集成到工业4.0框架中,以提高运营效率和降低成本.
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