Hybrid Modeling of an Induction Machine to Support Bearing Diagnostics

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Praneet Amitabh;Dimitar Bozalakov;Frederik De Belie
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Abstract

In this article, a novel hybrid model of an induction machine is proposed that can emulate the response of a machine with a faulty bearing. The idea behind developing such a topology is to have the response quite close to that from a real asset while keeping it computationally efficient. The aim is to develop an accurate and efficient model, akin to digital twins, which have the potential for real-time operation. Therefore, the model is divided into two parts. One is a physics-based model that takes fundamental equations and motor construction parameters to yield an intermediate response. All the major parameters are taken into account such that the fundamental component comes quite close to that of the real asset and the bearing fault signature comes in the same order. These signatures are quite small and some small parasitic effects or the assumptions taken while simplifying the model might not impact the fundamental component that much but they alter the signature's amplitude quite significantly. One way is to model all the parasitic effects, which might increase the computation effort significantly. Another way is to take all the parasitic effects altogether and bridge the difference using a statistical approach which is developed using experimental data. Therefore, the current measurements were taken for several bearings with different fault severity. These measurements are processed and quantified such that the net outcome can express the evolution of the signature with increasing fault severity. The same is done for the data generated using the physics-based model. Finally, the difference in the responses is reduced using the neural network such that it can mimic real-world machine behavior closely. The analytical model followed by statistical adjustment overall is considered a hybrid model. The ultimate goal of this methodology is to generate extensive datasets encompassing diverse operating conditions that can be used further to estimate the health of the bearing and possibly be used for training predictive algorithms to estimate bearing RUL in motors. The proposed methodology is developed for the machine operating at 1000 and 1500 RPM and is validated for three different operating speeds.
支持轴承诊断的感应机混合建模
本文提出了一种新颖的感应机混合模型,它可以模拟轴承故障机器的响应。开发这种拓扑结构的理念是在保持计算效率的同时,使其响应非常接近真实资产的响应。这样做的目的是开发一个精确高效的模型,类似于数字双胞胎,具有实时运行的潜力。因此,模型分为两部分。一个是基于物理的模型,它利用基本方程和电机结构参数来产生中间响应。所有主要参数都被考虑在内,因此基本分量与实际资产的基本分量非常接近,轴承故障特征也与实际资产的特征相同。这些特征非常小,一些微小的寄生效应或简化模型时的假设可能不会对基本分量产生太大影响,但却会显著改变特征的振幅。一种方法是对所有寄生效应建模,这可能会大大增加计算量。另一种方法是将所有寄生效应一网打尽,然后使用利用实验数据开发的统计方法弥合差异。因此,我们对故障严重程度不同的多个轴承进行了电流测量。对这些测量结果进行处理和量化,使净值结果能够表达故障严重程度增加时特征的演变。使用物理模型生成的数据也是如此。最后,利用神经网络缩小响应的差异,使其能够紧密模拟真实世界中的机器行为。分析模型和统计调整总体上被视为一种混合模型。该方法的最终目标是生成广泛的数据集,涵盖各种运行条件,可进一步用于估算轴承的健康状况,也可用于训练预测算法,以估算电机轴承的 RUL。所建议的方法是针对机器在 1000 和 1500 RPM 转速下运行而开发的,并在三种不同的运行转速下进行了验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
IEEE Open Journal of the Industrial Electronics Society
IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
10.80
自引率
2.40%
发文量
33
审稿时长
12 weeks
期刊介绍: The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments. Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.
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