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基于时域和基于谱成像的传递学习方法在振动数据上对感应电动机的故障检测。

Fault Detection in Induction Motor Using Time Domain and Spectral Imaging-Based Transfer Learning Approach on Vibration Data.

机构信息

Mechanical Engineering, Galgotias College of Engineering and Technology, Dr. A.P.J. Abdul Kalam Technical University, Greater Noida 201306, India.

Symbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University), Pune 412115, India.

出版信息

Sensors (Basel). 2022 Oct 26;22(21):8210. doi: 10.3390/s22218210.

DOI:10.3390/s22218210
PMID:36365909
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9655596/
Abstract

The induction motor plays a vital role in industrial drive systems due to its robustness and easy maintenance but at the same time, it suffers electrical faults, mainly rotor faults such as broken rotor bars. Early shortcoming identification is needed to lessen support expenses and hinder high costs by using failure detection frameworks that give features extraction and pattern grouping of the issue to distinguish the failure in an induction motor using classification models. In this paper, the open-source dataset of the rotor with the broken bars in a three-phase induction motor available on the IEEE data port is used for fault classification. The study aims at fault identification under various loading conditions on the rotor of an induction motor by performing time, frequency, and time-frequency domain feature extraction. The extracted features are provided to the models to classify between the healthy and faulty rotors. The extracted features from the time and frequency domain give an accuracy of up to 87.52% and 88.58%, respectively, using the Random-Forest (RF) model. Whereas, in time-frequency, the Short Time Fourier Transform (STFT) based spectrograms provide reasonably high accuracy, around 97.67%, using a Convolutional Neural Network (CNN) based fine-tuned transfer learning framework for diagnosing induction motor rotor bar severity under various loading conditions.

摘要

感应电动机因其坚固耐用和易于维护而在工业驱动系统中发挥着重要作用，但同时它也会遭受电气故障，主要是转子故障，例如断条转子。需要早期识别缺陷，以减少支持费用，并通过使用故障检测框架来阻止高成本，该框架对问题进行特征提取和模式分组，以使用分类模型来区分感应电动机中的故障。本文使用 IEEE 数据端口上提供的三相感应电动机带断条转子的开源数据集进行故障分类。该研究旨在通过在感应电动机的转子上进行时频域和时频域特征提取，在各种负载条件下识别故障。提取的特征提供给模型，以在健康和故障转子之间进行分类。使用随机森林 (RF) 模型，从时域和频域提取的特征的准确性分别高达 87.52%和 88.58%。而在时频域中，基于短时傅里叶变换 (STFT) 的频谱图使用基于卷积神经网络 (CNN) 的微调迁移学习框架，在各种负载条件下诊断感应电动机转子条严重程度时，提供了相当高的准确性，约为 97.67%。

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