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Time-frequency space vector modulus analysis of motor current for planetary gearbox fault diagnosis under variable speed conditions

机译：变速箱条件下行星齿轮箱故障诊断的电动机电流时频矢量模量分析

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摘要

Motor current signature analysis is a promising technique for electromechanical system fault detection, and has been studied under steady states. In this paper, a planetary gearbox fault diagnosis method under variable speed conditions using stator current signal is proposed. In order to thoroughly understand the frequency characteristics of current signals, an analytical amplitude modulation and phase modulation (AM-PM) current model considering gear fault modulation effects is presented. Two more aspects of endeavor are made to highlight gear fault signatures in stator current signals in context of inconspicuousness and time variability. Firstly, to address the sideband complexity and power supply dominance issues inherent with stator current signals, squared space vector modulus (SVM) together with its time-varying spectral characteristics in planetary gearbox fault cases under variable speed conditions are mathematically derived. Secondly, to reveal time-varying fault features in details, polynomial chirplet transform (PCT) is improved by iterative algorithm, and merits of fine time-frequency resolution and cross term free nature are achieved. The effectiveness of the proposed method is illustrated by numerical simulation, and is further validated by lab experiments on a real world 4 kW induction motor driven planetary gearbox test rig. (C) 2018 Elsevier Ltd. All rights reserved.
机译：电动机电流信号分析是一种用于机电系统故障检测的有前途的技术，并且已经在稳态下进行了研究。提出了一种基于定子电流信号的变速箱行星齿轮箱故障诊断方法。为了全面了解电流信号的频率特性，提出了一种考虑齿轮故障调制效应的解析幅度调制和相位调制（AM-PM）电流模型。在不显眼和时间可变的情况下，还进行了另外两个方面的工作以突出定子电流信号中的齿轮故障信号。首先，为了解决定子电流信号固有的边带复杂性和电源优势问题，在变速箱行星齿轮箱故障情况下，数学推导了平方空间矢量模量（SVM）及其时变频谱特征。其次，为详细揭示随时间变化的故障特征，通过迭代算法对多项式Chirplet变换（PCT）进行了改进，实现了时频分辨率和交叉项自由度优良的优点。通过数值模拟说明了该方法的有效性，并通过在现实世界中的4 kW感应电动机驱动的行星齿轮箱测试台上进行的实验验证了该方法的有效性。 （C）2018 Elsevier Ltd.保留所有权利。

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