Engineering Faculty, San Juan del Río Campus, Universidad Autónoma de Querétaro, Av. Río Moctezuma 249, San Juan del Río, Querétaro 76807, Mexico.
Instituto Tecnológico de la Energía, Universitat Politècnica de València (UPV), Camino de Vera s/n, 46022 Valencia, Spain.
Sensors (Basel). 2020 Mar 8;20(5):1477. doi: 10.3390/s20051477.
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.
感应电动机是许多工业过程中必不可少且广泛使用的组件。尽管这些机器非常坚固，但它们也容易出现故障。如今，获得电机健康状况的可靠和准确诊断是一项至关重要的任务，以便随后可以减少所需的时间和维修成本。完成此任务最常见的方法是基于电流分析，但是这种方法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新技术的发展，应该探索结合从不同量值的分析中获得的信息的可能性，以便在故障发展为不可逆损坏之前，得出更可靠的诊断结论。本文提出了一种智能传感器，该传感器通过短时间傅里叶变换（STFT）探索了在启动瞬态期间通过低成本、易于设置、非侵入性和紧凑的三轴 stray 通量传感器捕获的轴向、径向和两者组合 stray 通量的加权分析，并使用特征减少线性判别分析（LDA）和前馈神经网络（FFNN）的统计参数来对其进行特征描述，从而对特定模式进行特征描述，用于分类目的，为提供现场自动故障诊断方案提供了可能性。所得结果表明，所提出的智能传感器在监测和诊断感应电动机早期机电故障方面非常有效。通过针对单个和组合的断条转子和不对中故障的实验室感应电动机测试台进行了验证。
