Fault Diagnosis of 3-phase Induction Machine Using Harmonic Content of Stator Current Spectrum

Deeb, Muhammad; Kotelenets, N. F.

doi:10.1109/reepe49198.2020.9059213

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…The sidebands presented in Fig. (6-a) verify the formula (7). Other sidebands around the rotor slot harmonics (RSHs) were verified in Fig.…”

Section: B 75% Of the Load Operation Of Scimsupporting

confidence: 61%

“…These faults produce some symptoms such as air-gap variation, increased losses in efficiency, unbalanced voltages and/or line currents, decreased average torque, increased torque pulsations, excessive heating, etc. Generally, the anomaly of electromagnetic field in the air-gap creates sidebands harmonic components in the stator current spectra [5,7]. The verification of some sideband frequencies can be used as a good indicator to BRB faults detection.…”

Section: Introductionmentioning

confidence: 99%

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A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

Sbaa

Bessous

Pusca

et al. 2020

2020 International Conference on Electrical Engineering (ICEE)

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“…The sidebands presented in Fig. (6-a) verify the formula (7). Other sidebands around the rotor slot harmonics (RSHs) were verified in Fig.…”

Section: B 75% Of the Load Operation Of Scimsupporting

confidence: 61%

Section: Introductionmentioning

confidence: 99%

A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

Sbaa

Bessous

Pusca

et al. 2020

2020 International Conference on Electrical Engineering (ICEE)

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“…Motor Current Signature Analysis (MCSA) effectively builds the fault library and monitors conditions [114]. The method is achieved by observing the changes between the harmonic content of the motor line current under the fault occurrence [115]. However, the real-time deadline is hard to meet when implementing this technique on the controller.…”

Section: Controller-embeddable Power Electronicsmentioning

confidence: 99%

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

Wang

et al. 2023

Drones

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In the Machine Learning (ML) era, faced with challenges, including exponential multi-sensor data, an increasing number of actuators, and data-intensive algorithms, the development of Unmanned Aerial Vehicles (UAVs) is standing on a new footing. In particular, the Flight Management System (FMS) plays an essential role in UAV design. However, the trade-offs between performance and SWaP-C (Size, Weight, Power, and Cost) and reliability–efficiency are challenging to determine for such a complex system. To address these issues, the identification of a successful approach to managing heterogeneity emerges as the critical question to be answered. This paper investigates Heterogeneous Computing (HC) integration in FMS in the UAV domain from academia to industry. The overview of cross-layer FMS design is firstly described from top–down in the abstraction layer to left–right in the figurative layer. In addition, the HC advantages from Light-ML, accelerated Federated Learning (FL), and hardware accelerators are highlighted. Accordingly, three distinct research focuses detailed with visual-guided landing, intelligent Fault Diagnosis and Detection (FDD), and controller-embeddable Power Electronics (PE) to distinctly illustrate advancements of the next-generation FMS design from sensing, and computing, to driving. Finally, recommendations for future research and opportunities are discussed. In summary, this article draws a road map that considers the heterogeneous advantages to conducting the Flight-Management-as-a-Service (FMaaS) platform for UAVs.

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“…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

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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%.

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Fault Diagnosis of 3-phase Induction Machine Using Harmonic Content of Stator Current Spectrum

Cited by 8 publications

References 9 publications

A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

A comparative study dedicated to rotor failure detection in induction motors using MCSA, DWT, and EMD techniques

Heterogeneous Flight Management System (FMS) Design for Unmanned Aerial Vehicles (UAVs): Current Stages, Challenges, and Opportunities

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

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