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Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis

机译：基于瞬态杂散磁通量分析的感应电动机机电故障自动检测智能传感器

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摘要

Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.
机译：感应电动机是许多工业过程中必不可少的且被广泛使用的组件。尽管这些机器非常坚固，但它们很容易发生故障。如今，获得可靠，准确的电动机健康诊断是最重要的任务，以便可以随后减少所需的时间并降低维修成本。完成此任务的最常见方法是基于电流分析，该分析具有一些众所周知的缺点，可能导致错误的诊断。随着传感器和信号处理领域技术的新发展，应该探索将不同幅度的分析信息相结合的可能性，以便在故障发展成不可逆的损害之前获得更可靠的诊断结论。 。本文提出了一种智能传感器，该传感器探索了在启动瞬态过程中由低成本，易于设置，无创且紧凑的三轴杂散磁通传感器捕获的轴向杂散磁通，轴向杂散磁通和组合的加权分析。通过短时傅立叶变换（STFT）并使用统计参数表征出现在其上的特定模式，这些统计参数将特征归约线性判别分析（LDA），然后将前馈神经网络（FFNN）馈入分类目的，从而提供了提供现场自动故障诊断方案。获得的结果表明，所提出的智能传感器对于监测和诊断早期感应电动机机电故障是有效的。实验室感应电动机测试台可对单个的和组合的损坏的转子棒和未对准故障进行验证。

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