An Intelligent Harmonic Synthesis Technique for Air-Gap Eccentricity Fault Diagnosis in Induction Motors
De Z.Li; Wil Son Wang; FathyI smail
Abstract:Induction motors (IMs) are commonly used in various industrial applications. To improve energy consumption efficiency, a reliable IM health condition monitoring system is very useful to detect IM fault at its earliest stage to prevent operation degradation, and malfunction of IMs. An intelligent harmonic synthesis technique is proposed in this work to conduct incipient air-gap eccentricity fault detection in IMs. The fault harmonic series are synthesized to enhance fault features. Fault related local spectra are processed to derive fault indicators for IM airgap eccentricity diagnosis. The effectiveness of the proposed harmonic synthesis technique is examined experimentally by IMs with static air-gap eccentricity and dynamic air-gap eccentricity states under different load conditions. Test results show that the developed harmonic synthesis technique can extract fault features effectively for initial IM air-gap eccentricity fault detection.
Keywords: Air-gap eccentricity; Current signal; Fault detection; Induction motor
1. Introduction
Induction motors (IMs) are commonly used in various industrial applications. Furthermore, IMs consume about 50% of the generated electrical energy in the world [1]. IM defects will lead to low productivity and inefficient energy consumption. Endeavors have been put, for decades, to improve IM operation accuracy and IM driven industrial process efficiency. In industrial maintenance applications, for example, an efficient and reliable IM condition monitor is very useful to detect an IM defect at its earliest stage to prevent malfunction of IMs and reduce maintenance cost.
In general, air-gap eccentricity is classified as static eccentricity, dynamic eccentricity, as well as mixed eccentricity of these two types [2]. In static air-gap eccentricity, geometric axis of rotor rotation is not the geometric axis of the stator, and position of the minimal radial air-gap length is fixed in space. In dynamic air-gap eccentricity, the rotor rotates around the geometric axis of the stator, where the position of the minimum air-gap length rotates with the rotor. In a particular case of static air-gap eccentricity, rotor geometric axis is not parallel to stator geometric axis; the degree of eccentricity gradually changes along stator axis, which is inclined static eccentricity [3]. IM air-gap eccentricity defects could result in unbalanced magnetic pull, bearing damage, excessive vibration and noise, and even stator-rotor rub failure [4]. Correspondingly, this work will focus on initial IM fault detection of static eccentricity and dynamic eccentricity.
Recently, many research efforts have been undertaken to diagnose IM air-gap eccentricity fault using stator current signals due to their low cost and ease of implementation [5, 6]. For example, Blouml;dt et al. [7] presented a Wigner distribution method to analyze stator current signals and diagnose IM eccentricity fault. Akin et al. [8] conducted real-time eccentricity fault detection using reference frame theory. Bossio et al. [9] employed additional excitation to reveal information about air-gap eccentricity fault. Alarcon et al. [10] applied notch finite-impulse response filter and Wigner-Ville Distribution to study rotor asymmetries and mixed eccentricities. Faiz et al. [11] employed instantaneous power harmonics to detect mixed IM eccentricity defect. Huang et al. [12] applied an artificial neural network for the detection of rotor eccentricity faults. Esfahani et al. [13] utilized the Hilbert-Huang transform to detect IM eccentricity fault. Nandi et al. [14] studied the eccentricity fault related harmonics with different rotor cages. Riera-Guasp et al. [15] applied Gabor analysis for transient current signals to detect eccentricity fault. Park and Hur [16] analyzed specific frequency patterns of the stator current to detect dynamic eccentricity fault. Mirimani et al. [17] presented an online diagnostic method for static eccentricity fault detection. Some intelligent tools based on soft computing and pattern classification were also used for motor fault diagnosis in Refs. [18-感应电动机气隙偏心故障诊断的智能谐波综合技术
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