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Fault diagnosis techniques of electrical motors can prevent unplanned downtime and loss of money, production, and health. Various parts of the induction motor can be diagnosed: rotor, stator, rolling bearings, fan, insulation damage, and shaft. Acoustic analysis is non-invasive. Acoustic sensors are low-cost. Changes in the acoustic signal are often observed for faults in induction motors. In this paper, the authors present a fault diagnosis technique for three-phase induction motors (TPIM) using acoustic analysis. The authors analyzed acoustic signals for three conditions of the TPIM: healthy TPIM, TPIM with two broken bars, and TPIM with a faulty ring of the squirrel cage. Acoustic analysis was performed using fast Fourier transform (FFT), a new feature extraction method called MoD-7 (maxima of differences between the conditions), and deep neural networks: GoogLeNet, and ResNet-50. The results of the analysis of acoustic signals were equal to 100% for the three analyzed conditions. The proposed technique is excellent for acoustic signals. The described technique can be used for electric motor fault diagnosis applications. [ABSTRACT FROM AUTHOR]

Early detection and diagnosis of faults in synchronous machine (SM) are crucial since they drive critical applications. In this paper, the authors exploited the combined information from numerical simulation and experimental validation to evaluate the merits of certain fault indicators, for detecting machine faults. A specially designed SM was chosen for both simulation and measurement with the ability to physically introduce electro-mechanical failures. Shorted turn fault was made through accessible intermediate terminals of respective stator and rotor windings. Eccentricity faults of different intensity were created by suitable changeover of customized end-shield. Finite element method was used to develop healthy and faulty simulation models. The experimental setup facilitated measurement and subsequent calculation of key parameters such as stator branch currents, phase current, circulating current, rotor field current and shaft voltage. From the investigation of both simulation and experimental data, field current and shaft voltage were found to be two effective fault indicators from safety and reliability prospects. Both indicators have the ability to identify and discriminate between electro-mechanical and electrical faults. Further, it is demonstrated that with availability of physical dimensions and measured rotor parameters, a know-how can be created to detect failures, well before a catastrophic event occurs. [ABSTRACT FROM AUTHOR]