Condition monitoring of induction motor using Artificial Neural Network

Bhavsar, Ravi C.; Patel, Rakeshkumar A.; Bhalja, Bhavesh R.

doi:10.1109/aicera.2014.6908207

Cited by 8 publications

(5 citation statements)

References 11 publications

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“…Different alternatives to detect and diagnose faults in induction machines have been proposed and implemented in recent years. In a study, Bhavsar et al, [5] worked on stator fault detection in IM using ANN. They stated that when there is a fault in a motor the stator current becomes unbalanced.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Chouhan¹,

Gangsar²,

Porwal³

et al. 2020

Vib. proced.

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This paper describes an Artificial Neural Network (ANN) based fault diagnosis methodology for Induction Motors (IM) operating under the same conditions for various speeds and loads. In this study, ten different IM fault conditions are considered. We considered five mechanical faults (bearing fault, unbalanced rotor, misaligned rotor, bowed rotor, rotor with broken bar), four electrical faults (phase unbalance fault with two levels of severity, stator winding fault with two levels of severity), and one healthy motor condition. The current and vibration signals were considered in this work as these signals are generally considered to be the most efficient for the detection of mechanical and electrical faults in IM when used simultaneously. A machine fault simulator was used for the generation of vibration and current signals from different fault conditions. An ANN model was developed in which raw time domain vibration (in three directions) as well as current (in three phases) data are used simultaneously as input and then the fault diagnosis (training and testing) is performed. In this work, the fault diagnosis was attempted when testing was done for the same operating conditions as training. The developed fault diagnosis methods were found to be robust for various operating conditions (speeds and loads) of the IM.

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Section: Introductionmentioning

confidence: 99%

Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Chouhan¹,

Gangsar²,

Porwal³

et al. 2020

Vib. proced.

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show abstract

“…Thus, whether local defects can be detected by this method is still a question. In recent years, artificial intelligence (AI) methods, such as neural networks, have been proposed to synthesize monitored features and diagnose stator faults [34]. To perform these data-driven methods, a relatively large data set is required to train the AI model.…”

Section: Accelerated Aging and Failure Precursormentioning

confidence: 99%

Partial Discharge Investigation of Form-Wound Electric Machine Winding for Electric Aircraft Propulsion

Wang

et al. 2020

IEEE Trans. Transp. Electrific.

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Electric machine for aircraft propulsion is required to have high power density and high reliability, but the harsh operating environment, such as low air pressure, will challenge the motor's insulation system. This article presents the partial discharge (PD) investigation of the form-wound winding in high-frequency electric machines for electric aircraft propulsion. Experimental results show that the PD activity is largely enhanced due to the decrease in air pressure. PD inception voltage (PDIV) decreases to ∼50%-60%, and the PD magnitude dramatically increases when the pressure drops from 1 to 0.2 atm. Several windings with introduced defects were also tested to extract features from phase-resolved PD (PRPD) analysis, which can be used for PD pattern recognition. In addition, a PD-based failure precursor, combining the apparent charge, number, and phase interval of the monitored PD profile, is proposed to monitor the insulating condition of the thermally and electrically aged windings. The proposed failure precursor generally shows a rising trend with the aging time in this article. The application of the proposed failure precursor on online condition monitoring needs more investigation through long-term online observation of the electric machine.

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“…The important part is to adjust the weights in such a way that application of inputs yields desired results. Back propagation method is utilised for updating the weights [31].…”

Section: Trained Processing Unitmentioning

confidence: 99%

Cyber–physical microgrid components fault prognosis using electromagnetic sensors

Agarwal

Niknejad

Rahimnejad

et al. 2019

IET cyber-phys. syst.

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Condition monitoring of induction motor using Artificial Neural Network

Cited by 8 publications

References 11 publications

Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Artificial neural network based fault diagnostics for three phase induction motors under similar operating conditions

Partial Discharge Investigation of Form-Wound Electric Machine Winding for Electric Aircraft Propulsion

Cyber–physical microgrid components fault prognosis using electromagnetic sensors

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