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This proposal provides good fault detectability results compared to other published works in addition to the identification of more frequencies associated with the faults. Subsequently, the frequency marginal of the Gabor representation is calculated to obtain the spectral content of the IMF in the frequency domain. This proposed approach uses the Complete Ensemble Empirical Mode Decomposition for decomposing the signal into several intrinsic mode functions. The contribution of this work is a methodology for detecting faults in induction motors in steady-state operation based on the analysis of acoustic sound and vibration signals. Fault identification may also be possible by analyzing acoustic sound and vibration signals, which is useful because sometimes this information is the only available. This method is known as motor current signature analysis, which identifies frequencies associated with faults in the frequency domain or by the time-frequency decomposition of the current signals. Usually, the diagnosis is performed by analyzing current signals at a steady-state motor operation or during a start-up transient. There are currently a variety of methodologies for fault diagnosis. Nowadays, timely maintenance of electric motors is vital to keep up the complex processes of industrial production. Methodology for fault detection in induction motors via sound and vibration signalsĭelgado-Arredondo, Paulo Antonio Morinigo-Sotelo, Daniel Osornio-Rios, Roque Alfredo Avina-Cervantes, Juan Gabriel Rostro-Gonzalez, Horacio Romero-Troncoso, Rene de Jesus The processor is then programmed to generate a fault index for the operating current data based on any isolated fault components. The processor is also programmed to acquire at least on additional set of real-time operating current data from the motor during operation, redefine the noise component present in each additional set of real-time operating current data, and remove the noise component from the operating current data in real-time to isolate any fault components present in the operating current data. The controller further includes a processor programmed to receive a baseline set of current data from an operating motor and define a noise component in the baseline set of current data. The system includes a controller configured to detect indicia of incipient mechanical motor faults. Dimino, Steven A.Ī system and method for detecting incipient mechanical motor faults by way of current noise cancellation is disclosed. System and method for motor fault detection using stator current noise cancellation An index is proposed to assess the severity of the fault present in the motor. The designed template for pseudo-coloring is in acquiescence with the InterNational Electrical Testing Association (NETA) thermographic standard. One method uses transient thermal monitoring during the start of motor and other applies pseudo coloring technique on infrared image of the motor, after it reaches a thermal steady state. Two methods have been proposed that detect the fault and estimate its severity. This paper proposes an online and non invasive technique that uses infrared thermography, in order to detect the presence of inter turn fault in induction motor drive.

It can lead to the failure of complete phase and can even cause accidents, if left undetected or untreated. Inter turn fault is the second most commonly observed faults in the motors and is considered the most severe. These are subjected to various environmental, thermal and load stresses that ultimately reduces the motor efficiency and later leads to failure. Induction motors are the most commonly used prime movers in industries. Induction motor inter turn fault detection using infrared thermographic analysis SVM is also found to be a very good fault classifier which is highly competitive with RBF.

The RBF neural network shows very encouraging results for multi-class classification problems and is hoped to set up a base for incipient fault detection of induction motor. The steady state motor current with Park's Transformation has been used for discrimination of inner race and outer race bearing defects. The paper investigates the effectiveness of different patter classifier like Feed Forward Back Propagation (FFBPN), Radial Basis Function (RBF) and Support Vector Machine (SVM) for detection of bearing faults in Induction Motor. Soft Computing Application in Fault Detection of Induction MotorĭOE Office of Scientific and Technical Information (OSTI.GOV)

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