A method for broken bar fault diagnosis in three phase induction motor drive system using Artificial Neural Networks

Kumar, Rahul; Raj, I. Gerald Christopher; Suresh, K.; Leninpugalhanthi, P.; Suresh, M.; Panchal, Hitesh; Meenakumari, R.; Sadasivuni, Kishor Kumar

doi:10.1080/01430750.2021.1934117

Cited by 24 publications

(9 citation statements)

References 46 publications

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“…For the hyperplane to be optimally efficient, it must maintain a maximum distance between the sample classes; which is often explained using (7) and (8). Due to the constraints shown in (9), the pursuit of maximizing the hyperplane frequently generates the issue of minimizing the function.…”

Section: ) Linear Classification In Svmmentioning

confidence: 99%

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An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

Okwuosa

Hur

2023

IEEE Access

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In industries, squirrel cage induction motors are critical for supplying rotary motion in power tools. This research presents a robust but simple framework for an inter-turn fault classification at minor loading across diverse fault occurrence conditions, which is one of the most common defects in a squirrel cage induction motor. Early detection of this issue is critical to prevent the system from completely failing as a result of it evolving to a more severe stator winding fault. This study employs a hybrid feature selection strategy (a hybrid of a filterbased and a wrapper-based approach) using the Hilbert Transform signal processing technique and a statistical feature extraction approach, which is then fed to a support vector machine as the classifier. The suggested framework is tested and validated against other known classifier models. The results demonstrate that the model has a computationally low diagnostic performance process with exceptional accuracy. Furthermore, when compared to the other classifier models, the suggested model provided the best diagnostic outcome on the stator winding fault classification, demonstrating its dependability in fault diagnostic classification for squirrel cage induction motors.INDEX TERMS Diagnostics, squirrel cage induction motor, fault classification, turn-to-turn winding fault, machine learning, Hilbert transform, support vector machine.

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Section: ) Linear Classification In Svmmentioning

confidence: 99%

“…Where α i is the Larange multiplier that satisfy the constraint α ≥ 0, i = 1, ..., l. When the condition in (9) reaches its minimum or maximum points, the resultant should satisfy both ( 11) and (12).…”

Section: ) Linear Classification In Svmmentioning

confidence: 99%

An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

Okwuosa

Hur

2023

IEEE Access

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“…Scientists in industry and academia have turned their focus in recent decades to stator current testing, or MCSA as it is commonly known. When compared to other forms of motor condition-based maintenance (such as vibration), it can provide a similar indication without having to open the motor itself [7]. Motor protection usually involves measuring stator current.…”

Section: Related Workmentioning

confidence: 99%

“…Various mechanical operation properties can be used to monitor a motor's health, including leakage, thermal monitoring of trouble spots, chemical content, acoustics, machine power management, machine axial leakage flux, torque, machine acoustic emission signals, and induction machine signatures [6,7]. The method of rotor circuit electric current interpretation is particularly well known among these techniques.…”

Section: Introductionmentioning

confidence: 99%

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

et al. 2022

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The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

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“…Research efforts, particularly in engineering, have been dedicated to advancing diagnostic tools and techniques. Notably, the exploration of sophisticated diagnostic methods, including the application of machine learning algorithms such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), has gained substantial attention for analyzing extensive datasets and identifying patterns indicative of faults or anomalies (Almounajjed et al, 2022;Bilal Djamal Eddine et al, 2020;Cherif et al, 2020;Senthil Kumar et al., 2022;Tian et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis of harmonic sensitivity for stator fault diagnosis in induction motors

Abderrahim,

Zakaria,

Khechekhouche

et al. 2024

J. Eng. Exact Sci.

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Induction motors play a vital role in various industrial applications due to their commendable efficiency and reliability. However, their susceptibility to faults, especially in challenging industrial environments, highlights the need for vigilant fault detection to prevent unforeseen downtimes and reduce subsequent repair costs. Early fault diagnosis is crucial in this context. A systematic approach to diagnosing faults in asynchronous motors involves the application of signal processing techniques, particularly utilizing the Fast Fourier Transform (FFT). The FFT, as a mathematical tool, enables a comprehensive analysis of signals, facilitating the identification and isolation of their frequency components. Monitoring the frequency components within a motor's signal provides a means to determine the existence and severity of faults. FFT analysis allows for the monitoring of four distinct categories of harmonics: time harmonics (TH), rotor slot harmonics (RSH), rotor bar fault harmonics (RBFH), and eccentricity fault harmonics (EFH). Each type of harmonic offers valuable insights into specific fault categories. Empirical evidence, drawn from experimental results, emphasizes the heightened sensitivity of rotor slot harmonics (RSH) in detecting stator faults. Continuous monitoring of the RSH frequency component enables the prompt detection and localization of stator faults, along with an assessment of their severity. Additionally, this diagnostic methodology proves effective in identifying micro short-circuits between stator coils, allowing for a proactive strategy in predictive maintenance. This proactive approach enables anticipatory part replacement before degradation progresses to the point of causing comprehensive failure in the production chain. The combination of FFT-based signal processing and harmonic analysis establishes a robust framework for the early detection and localization of faults in asynchronous motors within industrial settings. This contributes to enhanced operational reliability and efficiency, ultimately ensuring smoother industrial processes.

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A method for broken bar fault diagnosis in three phase induction motor drive system using Artificial Neural Networks

Cited by 24 publications

References 46 publications

An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

An Intelligent Hybrid Feature Selection Approach for SCIM Inter-Turn Fault Classification at Minor Load Conditions Using Supervised Learning

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

Comparative analysis of harmonic sensitivity for stator fault diagnosis in induction motors

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