As a result of increasing machines capabilities in modern manufacturing, machines run continuously for hours. Therefore, early fault detection is required to reduce the maintenance expenses and obviate high cost and unscheduled downtimes. Fault diagnosis systems that provide features extraction and patterns classification of the fault are able to detect and classify the failures in machines. The majority of the related works that reported a procedure for detection of rotor bar breakage so far have applied motor current signal analysis using discrete wavelet transform. In this paper, the most appropriate features are extracted from the coefficients of a wavelet packet transform after fast Fourier transform of current signal. The aim of this study is to develop an effective and sensitive method for fault detection under low load conditions. Through combining the strength of both time-scale and frequency domain analysis techniques, a unified wavelet packet signature analysis pinpoints the fault signature in the special fault-oriented frequency bands. The wavelet analysis combined with a feed-forward neural network classifier provides an intelligent methodology for the automatic diagnosis of the fault severity during runtime of the motor. The faults severity is considered as one, two, and three broken rotor bars. The results have confirmed that the proposed method is effective for diagnosing rotor bar breakage fault in an induction motor and classification of fault severity.

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.

High-efficiency motors are being gradually introduced in many industrial applications because of their positive impacts on the environment by reducing energy consumption and CO₂ emission. In this respect, line start permanent magnet synchronous motors (LS-PMSMs) have been introduced recently. Due to their unique configuration, LS-PMSMs allow the obtaining of super premium efficiency levels, accompanied with a high torque and power factor. However, since the use of LS-PMSMs in the industry is in its infancy, no efficient scheme has yet been reported for broken rotor bar (BRB) fault detection in this type of motor. Accordingly, the main aim of this research is to investigate the fault-related feature for BRB faults on LS-PMSMs. In this regard, a simulation model and experimental setup for the investigation of BRB in LS-PMSM are implemented. The detection strategy for BRB in LS-PMSM proposed here is based on the monitoring of the start-up current signal and discrete wavelet transform. The entropy features are used as fault-related features for BRB faults. Finally, the ability of these features is validated for the detection of BRB in LS-PMSM through statistical analysis. In this research, the importance of the starting load is also considered for BRB detection in LS-PMSMs.

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.