Self-Organizing Maps (SOM) is an excellent method of analyzing multidimensional data. The SOM based classification is attractive, due to its unsupervised learning and topology preserving properties. In this paper, the performance of the self-organizing methods is investigated in induction motor rotor fault detection and severity evaluation. The SOM is based on motor current signature analysis (...

This paper presents separation harmonics to discriminate rotor failure from low frequency load torque oscillations in three phase induction motors. The most common method for detecting broken rotor bar faults is to analyze the corresponding sidebands through motor current signature analysis (MCSA). If a motor is subjected to load fluctuation, then the oscillation related sidebands exhibit simil...

In this paper, condition monitoring of a geared rotor system using finite element (FE) model updating and particle swarm optimization (PSO) method is onsidered. For this purpose, employing experimental data from the geared rotor system, an updated FE model is obtained. The geared rotor system under study consists of two shafts, four bearings, and two gears. To get the experimental data,  iezoel...

Rotating electrical machines play an important role in the world’s industrial life. Hence there is a strong demand on their reliable and safe operation. Their faults and failures can lead to excessive downtimes and generate enormous costs in reduced output, emergency maintenance and lost revenues. Therefore the fault detection methods of electrical machines are of real interest. For the rotor f...

Induction motors are critical components in industrial processes since their failure usually lead to an unexpected interruption at the industrial plant. The condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault feat...

Different alternatives to detect and diagnose faults in induction machines have been proposed and implemented in the last years. The technology of artificial neural networks has been successfully used to solve the motor incipient fault detection problem. The characteristics, obtained by this technique, distinguish them from the traditional ones, which, in most cases, need that the machine which...

In order to have fault identification for the gate rotor shaft with multiple cracks effectively, the quantity identification method of the gate rotor shaft with multiple cracks based on wavelet finite element method is established and the frequency test of the gate rotor shaft based on blind source separation algorithm is analyzed. Firstly the wavelet finite element was established by combining...

This paper presents the performances evaluation of different wavelet transform under broken rotor bars fault diagnosis. In this report, stator phase current was used for wavelet analysis. Discrete wavelet transform (DWT) coefficients of stator current in a specific frequency band are derived and analyzed. Wavelets db8, db9, db10, sym7 and sym8 are employed to analyze broken bar distorted stator...

Abstract: Induction motors are the dominant components in industrial processes involving electromechanical energy conversion. Safety, reliability and efficiency are major concerns in modern induction motor applications. Since detecting faults on time could avoid costly unscheduled shutdowns, in recent years there has been an increased interest in induction motor fault detection and diagnosis. I...

Abstract: Multi-phase motors are gaining more attention due to the advantages of good fault tolerance capability and high power density, etc. By applying dual-rotor technology to multi-phase machines, a five-phase dual-rotor permanent magnet synchronous motor (DRPMSM) is researched in this paper to further promote their torque density and fault tolerance capability. It has two rotors and two se...