Three-Phase Induction Motor Short Circuits Fault Diagnosis using MCSA and NSC

Deeb, Muhammad; Kotelenets, N. F.; Assaf, T.; Sultan, Hamdy M.; akaysheedept, Ahmed S.Al

doi:10.1109/reepe51337.2021.9388051

Cited by 8 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most popular and widespread diagnostic methods used in the detection and classification of interturn short circuits of electrical machines are motor current signature analysis (MCSA) [21][22][23][24], motor vibration signature analysis (MVSA) [25][26][27], the Park vector approach (PVA) [28][29][30], or wavelet transform (WT) [31,32]. These techniques allow for noninvasive analysis of a diagnostic signal without having to turn off the machine, and thus without interrupting the current operating state of the machine.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Pietrowski,

Górny

2024

Energies

View full text Add to dashboard Cite

The objective of the investigation was to increase the effectiveness of damage detection in the stator of the squirrel-cage induction machine. The analysis aimed to enhance the operational trustworthiness of the squirrel-cage induction machine by employing nonintrusive diagnostic methods based on a current signal and modern artificial intelligence methods. The authors of the study introduced a diagnostic technique for identifying multiphase interturn short circuits of stator winding. These short circuits are one of the most common faults in induction machines. The proposed method focusses on deriving a diagnostic signal from the phase-current waveforms of the machine. The noninvasive nature of the diagnostic technique presented is attributed to the application of the field model of electromagnetic phenomena to determine the diagnostic signal. For this purpose, a field model of a squirrel-cage machine was developed. The waveforms of phase currents obtained from the field model were used as input into an elaborated machine failure neural classifier. A deep neural network was used to develop a neural classifier. The effectiveness of the developed classifier has been experimentally verified, and the obtained results have been presented, concluded, and discussed. The scientific novelty presented in the article is the presentation of research results on the use of a neural classifier to detect damage in all phases of the stator winding at an early stage of its appearance. The features of this type of damage are very difficult to observe in signal waveforms such as a phase current or torque.

show abstract

Section: Introductionmentioning

confidence: 99%

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Pietrowski,

Górny

2024

Energies

View full text Add to dashboard Cite

show abstract

“…The objective of this research is, therefore, to develop a technique for detecting faults that appear on operational motors. The main faults detected and analyzed during induction-motor processing are related to stator faults, broken rotor bars, and air-gap eccentricity-related faults that have been investigated and reported extensively in the literature [4][5][6][7]. The motivation of this paper is derived from Internet resources and articles exploring faults using none-ANN techniques.…”

Section: Introductionmentioning

confidence: 99%

Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

Zuhaib¹,

Shaikh²,

Tanweer³

et al. 2022

Energies

View full text Add to dashboard Cite

This paper presents the high contact resistance (HCR) and rotor bar faults by an extraction method for an induction motor using Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). The root mean square (RMS) and mean features are obtained using DWT, and ANN is used for classification using activation functions. Activation provides output by assigning the specific input with respect to the transfer function according to the nature and type of the activation function. Method: The faulty conditions are induced using MATLAB by adopting the motor current signature analysis (MCSA) method to achieve current signature signals of the healthy and faulty motors. Results: The DWT technique has been applied to obtain fault-specific features of the average continuously varying signal (RMS) and an average of the data points (mean) at levels 5, 7, 8, and 9, followed by ANN to classify the faults for condition monitoring. Utility: The utility of the results is to reduce unscheduled downtime in the industry, thus saving revenue and reducing production losses. This work will help provide support to ensure early indication of faults in induction motors under operating conditions, enabling in-service engineers to take timely preventive measures as part of the availability of resources in IoT-enabled systems. Application: Resource availability and cybersecurity are becoming vital in an environment that supports the Internet of Things (IoT) as the essential components of Industry 4.0 scenarios. The novelty of this research lies in the implementation of high contact resistance and rotor bar faults using DWT and ANN with different activation functions to achieve accuracy up to 98%.

show abstract

Inter-Turn Short Circuit Detection in a Stator Induction Motor Using an External Axial Flux Sensor

2022

View full text Add to dashboard Cite

Three-Phase Induction Motor Short Circuits Fault Diagnosis using MCSA and NSC

Cited by 8 publications

References 8 publications

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Enhancing the Efficiency of Failure Recognition in Induction Machines through the Application of Deep Neural Networks

Faults Feature Extraction Using Discrete Wavelet Transform and Artificial Neural Network for Induction Motor Availability Monitoring—Internet of Things Enabled Environment

Inter-Turn Short Circuit Detection in a Stator Induction Motor Using an External Axial Flux Sensor

Contact Info

Product

Resources

About