An Empirical Mode Decomposition Approach for Multiple Broken Rotor Bars Detection in Three-Phase Induction Motors at No-Load Condition

This paper presents an empirical mode decomposition (EMD) approach for multiple broken rotor bars detection in squirrel cage induction motors running at no-load condition, using the resultant magnetic flux density measured by a Hall Effect sensor installed between two stator slots of the electrical machine. Usually, the traditional motor current signature analysis (MCSA) has produced many cases of false indications related to, among other reasons, incorrect speed estimation, operation at low load (low slip) and nonadjacent broken bars. This study has investigated the application of the EMD technique in the signal collected from the Hall sensor, in order to detect broken rotor bars for an induction motor running at very low slip and subjected to adjacent and nonadjacent broken bars. The present approach has been validated from some experiments carried out by a 7.5 kW induction motor fed by a sinusoidal power supply in the laboratory

A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors

[EN] Induction machines (IMs) play a critical role in various industrial processes but are susceptible to degenerative failures, such as broken rotor bars. Effective diagnostic techniques are essential in addressing these issues. In this study, we propose the utilization of convolutional neural networks (CNNs) for detection of broken rotor bars. To accomplish this, we generated a dataset comprising current samples versus angular position using finite element method magnetics (FEMM) software for a squirrel-cage rotor with 28 bars, including scenarios with 0 to 6 broken bars at every possible relative position. The dataset consists of a total of 16,050 samples per motor. We evaluated the performance of six different CNN architectures, namely Inception V4, NasNETMobile, ResNET152, SeNET154, VGG16, and VGG19. Our automatic classification system demonstrated an impressive 99% accuracy in detecting broken rotor bars, with VGG19 performing exceptionally well. Specifically, VGG19 exhibited high accuracy, precision, recall, and F1-Score, with values approaching 0.994 and 0.998. Notably, VGG19 exhibited crucial activations in its feature maps, particularly after domain-specific training, highlighting its effectiveness in fault detection. Comparing CNN architectures assists in selecting the most suitable one for this application based on processing time, effectiveness, and training losses. This research suggests that deep learning can detect broken bars in induction machines with accuracy comparable to that of traditional methods by analyzing current signals using CNNs.K Barrera-Llanga appreciates the financial support of the Secretary of Higher Education, Science, Technology and Innovation of Ecuador as a personal sponsor entity.Barrera-Llanga, K.; Burriel-Valencia, J.; Sapena-Bano, A.; Martinez-Roman, J. (2023). A Comparative Analysis of Deep Learning Convolutional Neural Network Architectures for Fault Diagnosis of Broken Rotor Bars in Induction Motors. Sensors. 23(19):1-20. https://doi.org/10.3390/s23198196120231

Rotor Asymmetries Faults Detection in Induction Machines under the Impacts of Low-Frequency Load Torque Oscillation

Low-frequency torque oscillations (LTOs) characteristic components emerge in the stator current spectrum of induction machines (IMs) as additive frequencies near the rotor asymmetry fault (RAF) indices especially in gearbox-based electromechanical system. The interactions between these two components make the fault detection process complicated and lead to false alarm. In this paper, a new technique for detection and separation of RAF from LTOs in IMs based on single phase stator current data is proposed. The method benefits from a novel pre-processing stage based on several sign functions. Hence, a two-axis rotating reference frame with a single phase of stator current of IMs with no prior knowledge of the rotational speed is introduced. The proposed method maps the static reference frame obtained through single stator current and its associated Hilbert transform to the proposed rotating reference frame which can separate the effects of LTOs from RAF, effectively. The validity of the proposed technique is tested through theoretical analysis, and experiments in both steady-state and transient conditions. In this regard, Synchro-squeezing Wavelet Transforms (SWT) is used for time-frequency analysis of faulty stator current in transient conditions. The obtained results confirm the effectiveness of the proposed approach to separate the RAF characteristic frequency from LTOs even in line-fed IMs applications

Machine Learning based Early Fault Diagnosis of Induction Motor for Electric Vehicle Application

Electrified vehicular industry is growing at a rapid pace with a global increase in production of electric vehicles (EVs) along with several new automotive cars companies coming to compete with the big car industries. The technology of EV has evolved rapidly in the last decade. But still the looming fear of low driving range, inability to charge rapidly like filling up gasoline for a conventional gas car, and lack of enough EV charging stations are just a few of the concerns. With the onset of self-driving cars, and its popularity in integrating them into electric vehicles leads to increase in safety both for the passengers inside the vehicle as well as the people outside. Since electric vehicles have not been widely used over an extended period of time to evaluate the failure rate of the powertrain of the EV, a general but definite understanding of motor failures can be developed from the usage of motors in industrial application. Since traction motors are more power dense as compared to industrial motors, the possibilities of a small failure aggravating to catastrophic issue is high. Understanding the challenges faced in EV due to stator fault in motor, with major focus on induction motor stator winding fault, this dissertation presents the following: 1. Different Motor Failures, Causes and Diagnostic Methods Used, With More Importance to Artificial Intelligence Based Motor Fault Diagnosis. 2. Understanding of Incipient Stator Winding Fault of IM and Feature Selection for Fault Diagnosis 3. Model Based Temperature Feature Prediction under Incipient Fault Condition 4. Design of Harmonics Analysis Block for Flux Feature Prediction 5. Flux Feature based On-line Harmonic Compensation for Fault-tolerant Control 6. Intelligent Flux Feature Predictive Control for Fault-Tolerant Control 7. Introduction to Machine Learning and its Application for Flux Reference Prediction 8. Dual Memorization and Generalization Machine Learning based Stator Fault Diagnosi

Recursive Singular Spectrum Analysis for Induction Machines Unbalanced Rotor Fault Diagnosis

One of the major challenges of diagnosing rotor symmetry faults in induction machines is severe modulation of fault and supply frequency components. In particular, existing techniques are not able to identify fault components in the case of low slips. In this paper, this problem is tackled by proposing a novel approach. First, a new use of singular spectrum analysis (SSA), as a powerful spectrum analyser, is introduced for fault detection. Our idea is to treat the stator current signature of the wound rotor induction machine as a time series. In this approach, the current signature is decomposed into several eigenvalue spectra (rather than frequency spectra) to find a subspace where the fault component is recognisable. Subsequently, the fault component is detected using some data-driven filters constructed with the knowledge about characteristics of supply and fault components. Then, an inexpensive peak localisation procedure is applied to the power spectrum of the fault component to identify the exact frequency of the fault. The fault detection and localisation methods are then combined in a recursive regime to further improve the diagnosis’ performance particularly at high rotor speeds and small rotor faults. The proposed approach is data-driven and is directly applied to the raw signal with no suppression or filtration of the frequency harmonics with a low computational complexity. The numerical results obtained with real data at several rotation speeds and fault severities, unveil the effectiveness and real-time feature of the proposed approach

Proceedings of the 8th International Conference EEMODS'2013 Energy Efficiency in Motor Driven Systems

This book contains the papers presented at the eighth international conference on Energy Efficiency in Motor Driven Systems EEMODS 2013 EEMODS 2013 was organised in Rio de Janeiro, Brasil from 28 to 30 October 2013. This major international conference, which was previously been staged in Lisbon (1996), London (1999), Treviso (2002), Heidelberg (2005), Beijing (2007), Nantes (2009) and Washington DC (2011) has been very successful in attracting an international and distinguished audience, representing a wide variety of stakeholders in policy implementation and development, manufacturing and promotion of energy-efficient motor systems, including key policy makers, equipment manufacturers, academia and end-users. Potential readers who may benefit from this book include researchers, engineers, policymakers, energy agencies, electric utilities, and all those who can influence the design, selection, application, and operation of electrical motor driven systems.JRC.F.7-Renewables and Energy Efficienc

Modeling and Simulation in Engineering

The general aim of this book is to present selected chapters of the following types: chapters with more focus on modeling with some necessary simulation details and chapters with less focus on modeling but with more simulation details. This book contains eleven chapters divided into two sections: Modeling in Continuum Mechanics and Modeling in Electronics and Engineering. We hope our book entitled "Modeling and Simulation in Engineering - Selected Problems" will serve as a useful reference to students, scientists, and engineers