Mohammad Rezazadeh Mehrjou

Static Eccentricity Fault Recognition in Three-Phase Line Start Permanent Magnet Synchronous Motor Using Finite Element Method

This paper is dedicated to investigating static eccentricity in a three-phase LSPMSM. The modeling of LSPMSM with static eccentricity between stator and rotor is developed using finite element method (FEM). The analytical expression for the permeance and flux components of nonuniform air-gap due to static eccentricity fault is discussed. Various indexes for static eccentricity detection using stator current signal of IM and permanent magnet synchronous motor (PMSM) are presented. Since LSPMSM is composed of a rotor which is a combination of these two motors, the ability of these features is evaluated for static eccentricity diagnosis in LSPMSM. The simulated stator current signal of LSPMSM in the presence of static eccentricity is analyzed in frequency domain using power spectral density (PSD). It is demonstrated that static eccentricity fault generates a series of low frequency harmonic components in the form of sidebands around the fundamental frequency. Moreover, the amplitudes of these components increase in proportion to the fault severity. According to the mentioned observations, an accurate frequency pattern is specified for static eccentricity detection in three-phase LSPMSM.

Performance analysis of line-start permanent magnet synchronous motor in presence of rotor fault

Electrical Motors are widely employed in both industrial and domestic fields. Line start-permanent magnet motor is one of the modern high efficiency motor introduced. During working of electrical motors, various faults, like stator faults, rotor faults, bearings faults, occur that lead to malfunction of the motor. Among these faults, rotor fault, broken bar, is important in the motors with squirrel cage rotor. This paper deal with the finite element method of the electromagnetic field associated with this motor to find the performance of it with presence of rotor faults.

Increase Performance of IPMSM by Combination of Maximum Torque per Ampere and Flux-Weakening Methods

Interior permanent magnet motor (IPMSM) was used as air conditioner compressor to reduce the power consumption and improve the performance of the system. Two control methods including maximum torque per ampere (MTPA) and flux-weakening methods were employed to increase the speed range of the air conditioner compressor. The present study adapted the flux weakening algorithm technique which can be used for constant torque and constant power regions. Results indicated that the operation speed range of the IPMSM may increase significantly by using the proposed flux weakening algorithm.

Evaluation of Fourier and wavelet analysis for efficient recognition of broken rotor bar in squirrel-cage induction machine

Incipient fault detection of the induction machines (IM) prevents the unscheduled downtime and hence reduces maintenance costs. The Motor Current Signature Analysis (MCSA) is considered as an effective fault detection method in any IM. However, a signal processing technique, which enhances the fault signature and suppress the dominant system dynamics and noise must be considered. Frequency analysis as well as time-frequency analysis is the most common signal processing methods. In this paper, the effectiveness of these two analysis methods were investigated for incipient broken rotor bar detection. Wavelet transform provides more accurate failure detection in different operational circumstances. However, there are different families in the wavelet analysis that affect the efficiency of encoding, denoising, compressing, decomposing and reconstructing the signal under observation. Accordingly, it is desirable to select the powerful wavelet family, which produces the best results for the signal being analyzed. This research also investigated the analysis of current signal using different families of wavelet for effective detection of broken rotor bars in IM.

Fault detection of broken rotor bar in LS-PMSM using random forests

Abstract This paper proposes a new approach to diagnose broken rotor bar failure in a line start-permanent magnet synchronous motor (LS-PMSM) using random forests. The transient current signal during the motor startup was acquired from a healthy motor and a faulty motor with a broken rotor bar fault. We extracted 13 statistical time domain features from the startup transient current signal, and used these features to train and test a random forest to determine whether the motor was operating under normal or faulty conditions. For feature selection, we used the feature importances from the random forest to reduce the number of features to two features. The results showed that the random forest classifies the motor condition as healthy or faulty with an accuracy of 98.8% using all features and with an accuracy of 98.4% by using only the mean-index and impulsion features. The performance of the random forest was compared with a decision tree, Naive Bayes classifier, logistic regression, linear ridge, and a support vector machine, with the random forest consistently having a higher accuracy than the other algorithms. The proposed approach can be used in industry for online monitoring and fault diagnostic of LS-PMSM motors and the results can be helpful for the establishment of preventive maintenance plans in factories.

Broken rotor bar detection in LS-PMSMs based on statistical features analysis of start-up current envelope

Early detection of asymmetry in electrical motors is important because of their diversity of use in different fields. A proper fault detection scheme helps to stop propagation of the failure or limit its escalation to severe degrees and thus prevents unscheduled downtimes that cause loss of production and financial income. Line start-permanent magnet motor (LS-PMSM) is one of the modern high efficiency motor introduced to the market recently. A new method for the fault diagnosis of a broken rotor bar in line-start permanent magnet synchronous motor is presented in this paper. The method is based on the analysis of the transient stator current envelopes of LS-PMSM using statistical features in time domain analysis. Four different level of load were investigated in this study to indicate the effect of load in broken rotor bar detection. Hilbert Transform is used to extract the envelope of the current signal during the transient reign.

A survey of broken rotor bar detection using PT and HT in squirrel cage electrical machine

Early detection of faults in electrical machines are imperative because of their diversity of use in different fields. A suitable fault monitoring scheme helps to stop propagation of the failure or limit its escalation to severe degrees and thus prevents unscheduled downtimes that cause loss of production and financial income. In this study, a survey of methods based on the Park transform and Hilbert transform for broken rotor bar fault monitoring in Squirrel cage electrical machine is presented.

Wavelet-Based Analysis of MCSA for Fault Detection in Electrical Machine

Early detection of irregularity in electrical machines is important because of their diversity of use in different fields. A proper fault detection scheme helps to stop the propagation of failure or limits its escalation to severe degrees, and thus it prevents unscheduled down‐ times that cause loss of production and financial income. Among different modes of fail‐ ures that may occur in the electrical machines, the rotor-related faults are around 20%. Successful detection of any failure in electrical machines is achieved by using a suitable condition monitoring followed by accurate signal processing techniques to extract the fault features. This article aims to present the extraction of features appearing in current signals using wavelet analysis when there is a rotor fault of eccentricity and broken rotor bar. In this respect, a brief explanation on rotor failures and different methods of condition moni‐ toring with the purpose of rotor fault detection is provided. Then, motor current signature analysis, the fault-related features appeared in the current spectrum and wavelet trans‐ form analyses of the signal to extract these features are explained. Finally, two case studies involving the wavelet analysis of the current signal for the detection of rotor eccentricity and broken rotor bar are presented.

Broken rotor bar detection of induction machine using wavelet packet coefficient-related features

Fault diagnosis of induction machine can be achieved through wavelet packet analysis to acquire information about its stability and mutability. This paper presents an experimental evaluation of applying wavelet packet transform based on the sideband components, (1 ± 2ks)fs, for broken rotor fault detection in induction machines. The wavelet-based method decomposes stator current signal into effective wavelet coefficients. It is shown that the root mean square (RMS) value of wavelet packet coefficients in special frequency bands collectively establishes a feature index. Once the broken rotor bar occurs, this index value increases to distinguish healthy and faulty mode of induction motor as well as fault severity. Additionally, we investigate the left sideband around the fundamental frequency (50Hz), (1 - 2s)fs, which specifically represents the stator current spectrum of the machine when a rotor bar breakage takes place. An induction motor with one and two bar breakage at 35%, 50% and 80% of full load are investigated. The experimental tests indicate good reliability of different frequency resolution for same frequency component.

Diagnosis of static eccentricity fault in line start permanent magnet synchronous motor

In this paper, finite element method is employed for diagnosis of static eccentricity in line start permanent magnet synchronous motor. The motor is modeled with different degrees of eccentricity. Stator current spectrum of healthy and faulty motor are analyzed using power spectral density technique. Amplitudes of harmonic components around fundamental frequency in stator current spectrum are proposed for static eccentricity detection in this type of motor.