An effective Hilbert-Huang transform-based approach for dynamic eccentricity fault diagnosis in double-rotor double-sided stator structure axial flux permanent magnet generator under various load and speed conditions

Makan Torabi, Y. Alinejad‐Beromi
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引用次数: 3

Abstract

: Eccentricity fault in double-sided axial flux permanent magnet generator is very difficult to be detected as the fault generated variations in terminal electrical parameters are very weak and chaotic, especially at the initial stages of the fault occurrence. In addition, one of the most important problems in any fault diagnosis approach is the investigation of load and speed variation on the proposed indices. To overcome the aforementioned difficulty and problems, this paper adopts a novelty detection algorithm based on Hilbert–Huang transform (HHT) which is a time-frequency signal analysis approach based on empirical mode decomposition and the Hilbert transform. It is well suited for reliable fault diagnosis since it is unaffected by transient conditions which make the diagnosis process incur into false alarms. The HHT-based methods has been demonstrated in recent years for rotor and bearing faults detection of induction machine and also for stator faults identification in PM synchronous machines with radial flux structure. This study explores the possibility of applying the technique to the detection of dynamic eccentricity faults in double-rotor double-sided stator structure axial flux permanent magnet generator under variable load and speed conditions. This approach relies on two steps: estimating the intrinsic mode functions (IMFs) by the empirical mode decomposition (EMD) and computing the instantaneous amplitude (IA) and instantaneous frequency (IF) of IMFs using the Hilbert transform. The more significant IMFs are determined by means of Hilbert spectrum, which is applied for accurate eccentricity fault diagnosis. The eccentricity severity can be evaluated based on the IMFs energy value. The theoretical basis of the proposed method is presented. The effectiveness of the proposed method is verified by a series of simulation and experimental tests under different conditions. The results show that the presented approach in this paper is robust against
基于Hilbert-Huang变换的双转子双侧定子结构轴向磁通永磁发电机动态偏心故障诊断方法
双向轴向磁通永磁发电机偏心故障由于故障产生的终端电气参数变化非常微弱和混乱,特别是在故障发生的初始阶段,因此很难检测到偏心故障。此外,在任何故障诊断方法中,最重要的问题之一是研究所提出的指标上的负载和速度变化。为了克服上述困难和问题,本文采用了基于Hilbert - huang变换(Hilbert - huang transform, HHT)的新颖性检测算法,这是一种基于经验模态分解和Hilbert变换的时频信号分析方法。该方法不受故障诊断过程中产生虚警的瞬态条件的影响,适合于可靠的故障诊断。近年来,基于hht的方法已在感应电机转子和轴承故障检测以及径向磁链结构的永磁同步电机定子故障识别中得到验证。本研究探讨了将该技术应用于双转子、双面定子结构轴向磁通永磁发电机变负载变转速工况下动态偏心故障检测的可能性。该方法依赖于两个步骤:通过经验模态分解(EMD)估计内禀模态函数(IMFs),并使用希尔伯特变换计算IMFs的瞬时振幅(IA)和瞬时频率(IF)。利用希尔伯特谱确定了较为显著的分量,并将其应用于偏心故障的准确诊断。偏心的严重程度可以根据imf的能量值来评估。给出了该方法的理论基础。通过一系列不同条件下的仿真和实验验证了该方法的有效性。结果表明,本文提出的方法对
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