Abstract

Competition in the industrial environment is increasingly intense, so it is of utmost importance that organizations keep their assets in operation as much as possible (in order to produce more). In this context, there is a need for predictive maintenance, a technique that detects the health of assets in real time, allowing failures to be diagnosed before they can interrupt the operation of the assets, avoiding high financial losses. This study uses a sixteen-motor experimental setup with four different known operating conditions. The vibration signal of these motors, through signal analysis, both in time and frequency domains, is performed to evaluate the types and severities of the defects. An artificial neural network (ANN) is used to classify these defects. Considering the vibration analysis, mechanical faults can be identified quickly and conveniently. For the development of the ANN, it was necessary to perform a preprocessing of the vibration signal (response in time) due to the data size, which overwhelms the network. Thus, statistical data were used to extract key information from the vibration signal. Finally, the neural network created based on this study’s methodology presents extremely reliable results, allowing a quick and robust diagnosis of the motor operating condition.

中文翻译：

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基于振动信号分析和人工神经网络的三相电动机故障分类

摘要

工业环境中的竞争日益激烈，因此组织尽可能多地保持其资产运营（以生产更多资产）至关重要。在这种情况下，需要进行预测性维护，该技术可实时检测资产的运行状况，从而在故障能够中断资产运行之前进行诊断，从而避免了高额的财务损失。这项研究使用了16种电机的实验装置，并具有四种不同的已知工作条件。通过信号分析在时域和频域上对这些电动机的振动信号进行评估，以评估缺陷的类型和严重程度。人工神经网络（ANN）用于对这些缺陷进行分类。考虑到振动分析，机械故障可以快速，方便地识别。对于ANN的开发，由于数据大小，有必要对振动信号进行预处理（及时响应），这会使网络不堪重负。因此，使用统计数据从振动信号中提取关键信息。最后，基于本研究方法创建的神经网络可提供极其可靠的结果，从而可以快速，可靠地诊断电动机的运行状况。