Prieto Estacio Rommel Stiward, Bravo Montenegro Diego Alberto, Rengifo Rodas Carlos Felipe
Universidad del Cauca, Calle 5 Nro. 4-70, Popayán (Cauca), Colombia.
Data Brief. 2023 Sep 13;50:109569. doi: 10.1016/j.dib.2023.109569. eCollection 2023 Oct.
Predictive Maintenance () has a main role in the Fourth Industrial Revolution; its goal is to design models that can safely detect failure in systems before they fail, aiming to reduce financial, environmental, and operational costs. A brushless DC (BLDC) electric motors have increasingly become more popular and been gaining popularity in industrial applications, so their analysis for applications is only a natural progression; audio analysis proves to be a useful method to achieve this and rises as a very pragmatic case of study of the characteristics of the motors. The main goal of this paper is to showcase sound-based behavior of BLDC motors in different failure modes as result of an experiment led by researchers at Universidad del Cauca in Colombia. This dataset may provide researchers with useful information regarding signal processing and the development of Machine Learning applications that would achieve an improvement within Predictive Maintenance and I4.0.Predictive Maintenance () has a main role in the Fourth Industrial Revolution; its goal is to design models that can safely detect failure in systems before they fail, aiming to reduce financial, environmental, and operational costs. A brushless DC (BLDC) electric motors have increasingly become more popular and been gaining popularity in industrial applications, so their analysis for applications is only a natural progression; audio analysis proves to be a useful method to achieve this and rises as a very pragmatic case of study of the characteristics of the motors. The main goal of this paper is to showcase sound-based behavior of BLDC motors in different failure modes as result of an experiment led by researchers at Universidad del Cauca in Colombia. This dataset may provide researchers with useful information regarding signal processing and the development of Machine Learning applications that would achieve an improvement within Predictive Maintenance and I4.0.
预测性维护在第四次工业革命中发挥着重要作用；其目标是设计能够在系统发生故障之前安全检测到故障的模型，旨在降低财务、环境和运营成本。无刷直流（BLDC）电动机在工业应用中越来越受欢迎且日益普及，因此对其进行应用分析是自然而然的发展；音频分析被证明是实现这一目标的有用方法，并且作为对电动机特性进行研究的一个非常实用的案例而兴起。本文的主要目标是展示哥伦比亚考卡大学的研究人员所进行的一项实验结果，即无刷直流电动机在不同故障模式下基于声音的行为。该数据集可为研究人员提供有关信号处理以及机器学习应用开发的有用信息，这些应用将在预测性维护和工业4.0领域实现改进。预测性维护在第四次工业革命中发挥着重要作用；其目标是设计能够在系统发生故障之前安全检测到故障的模型，旨在降低财务、环境和运营成本。无刷直流（BLDC）电动机在工业应用中越来越受欢迎且日益普及，因此对其进行应用分析是自然而然的发展；音频分析被证明是实现这一目标的有用方法，并且作为对电动机特性进行研究的一个非常实用的案例而兴起。本文的主要目标是展示哥伦比亚考卡大学的研究人员所进行的一项实验结果，即无刷直流电动机在不同故障模式下基于声音的行为。该数据集可为研究人员提供有关信号处理以及机器学习应用开发的有用信息，这些应用将在预测性维护和工业4.0领域实现改进。
