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使用具有优化斯莱皮恩窗的电流传感器对瞬态运行中的感应电机进行故障诊断

Fault Diagnosis of Induction Machines in a Transient Regime Using Current Sensors with an Optimized Slepian Window.

作者信息

Burriel-Valencia Jordi, Puche-Panadero Ruben, Martinez-Roman Javier, Sapena-Bano Angel, Pineda-Sanchez Manuel

机构信息

Institute for Energy Engineering, Universitat Politècnica de València, Cmno. de Vera s/n, 46022 Valencia, Spain.

出版信息

Sensors (Basel). 2018 Jan 6;18(1):146. doi: 10.3390/s18010146.

DOI:10.3390/s18010146
PMID:29316650
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5795768/
Abstract

The aim of this paper is to introduce a new methodology for the fault diagnosis of induction machines working in the transient regime, when time-frequency analysis tools are used. The proposed method relies on the use of the optimized Slepian window for performing the short time Fourier transform (STFT) of the stator current signal. It is shown that for a given sequence length of finite duration, the Slepian window has the maximum concentration of energy, greater than can be reached with a gated Gaussian window, which is usually used as the analysis window. In this paper, the use and optimization of the Slepian window for fault diagnosis of induction machines is theoretically introduced and experimentally validated through the test of a 3.15-MW induction motor with broken bars during the start-up transient. The theoretical analysis and the experimental results show that the use of the Slepian window can highlight the fault components in the current's spectrogram with a significant reduction of the required computational resources.

摘要

本文的目的是介绍一种用于在暂态工况下运行的感应电机故障诊断的新方法，该方法使用时频分析工具。所提出的方法依赖于使用优化的斯莱皮恩窗来对定子电流信号进行短时傅里叶变换（STFT）。结果表明，对于给定的有限持续时间的序列长度，斯莱皮恩窗具有最大的能量集中度，比通常用作分析窗的门控高斯窗所能达到的能量集中度更高。本文从理论上介绍了斯莱皮恩窗在感应电机故障诊断中的应用和优化，并通过对一台在启动暂态期间出现断条故障的3.15兆瓦感应电动机进行测试进行了实验验证。理论分析和实验结果表明，使用斯莱皮恩窗可以在显著减少所需计算资源的情况下突出电流频谱图中的故障分量。

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