scholarly journals Winding Tensor Approach for the Analytical Computation of the Inductance Matrix in Eccentric Induction Machines

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3058
Author(s):  
Javier Martinez-Roman ◽  
Ruben Puche-Panadero ◽  
Angel Sapena-Bano ◽  
Manuel Pineda-Sanchez ◽  
Juan Perez-Cruz ◽  
...  
Keyword(s):  
Early Stage ◽  
Induction Machines ◽  
Air Gap ◽  
Analytical Models ◽  
Tensor Algebra ◽  
Production Lines ◽  
Complex Interactions ◽  
Spatial Waves ◽  
Rotor Core ◽  
Critical Components

Induction machines (IMs) are critical components of many industrial processes, what justifies the use of condition-based maintenance (CBM) systems for detecting their faults at an early stage, in order to avoid costly breakdowns of production lines. The development of CBM systems for IMs relies on the use of fast models that can accurately simulate the machine in faulty conditions. In particular, IM models must be able to reproduce the characteristic harmonics that the IM faults impress in the spatial waves of the air gap magneto-motive force (MMF), due to the complex interactions between spatial and time harmonics. A common type of fault is the eccentricity of the rotor core, which provokes an unbalanced magnetic pull, and can lead to destructive rotor-stator rub. Models developed using the finite element method (FEM) can achieve the required accuracy, but their high computational costs hinder their use in online CBM systems. Analytical models are much faster, but they need an inductance matrix that takes into account the asymmetries generated by the eccentricity fault. Building the inductance matrix for eccentric IMs using traditional techniques, such as the winding function approach (WFA), is a highly complex task, because these functions depend on the combined effect of the winding layout and of the air gap asymmetry. In this paper, a novel method for the fast and simple computation of the inductance matrix for eccentric IMs is presented, which decouples the influence of the air gap asymmetry and of the winding configuration using two independent tensors. It is based on the construction of a primitive inductance tensor, which formulates the eccentricity fault using single conductors as the simplest reference frame; and a winding tensor that converts it into the inductance matrix of a particular machine, taking into account the configuration of the windings. The proposed approach applies routine procedures from tensor algebra for performing such transformation in a simple way. It is theoretically explained and experimentally validated with a commercial induction motor with a mixed eccentricity fault.

scholarly journals Analytical Model of Induction Machines with Multiple Cage Faults Using the Winding Tensor Approach

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5076
Author(s):  
Javier Martinez-Roman ◽  
Ruben Puche-Panadero ◽  
Angel Sapena-Bano ◽  
Carla Terron-Santiago ◽  
Jordi Burriel-Valencia ◽  
...  
Keyword(s):  
Computational Cost ◽  
Induction Machines ◽  
Diagnostic Techniques ◽  
Analytical Models ◽  
Tensor Algebra ◽  
Diagnostic Systems ◽  
Technical Literature ◽  
Twin Model ◽  
Ring Segment ◽  
The One

Induction machines (IMs) are one of the main sources of mechanical power in many industrial processes, especially squirrel cage IMs (SCIMs), due to their robustness and reliability. Their sudden stoppage due to undetected faults may cause costly production breakdowns. One of the most frequent types of faults are cage faults (bar and end ring segment breakages), especially in motors that directly drive high-inertia loads (such as fans), in motors with frequent starts and stops, and in case of poorly manufactured cage windings. A continuous monitoring of IMs is needed to reduce this risk, integrated in plant-wide condition based maintenance (CBM) systems. Diverse diagnostic techniques have been proposed in the technical literature, either data-based, detecting fault-characteristic perturbations in the data collected from the IM, and model-based, observing the differences between the data collected from the actual IM and from its digital twin model. In both cases, fast and accurate IM models are needed to develop and optimize the fault diagnosis techniques. On the one hand, the finite elements approach can provide highly accurate models, but its computational cost and processing requirements are very high to be used in on-line fault diagnostic systems. On the other hand, analytical models can be much faster, but they can be very complex in case of highly asymmetrical machines, such as IMs with multiple cage faults. In this work, a new method is proposed for the analytical modelling of IMs with asymmetrical cage windings using a tensor based approach, which greatly reduces this complexity by applying routine tensor algebra to obtain the parameters of the faulty IM model from the healthy one. This winding tensor approach is explained theoretically and validated with the diagnosis of a commercial IM with multiple cage faults.

Effect of Stator Insulation Failure on the Motor Drive System Performance

2021 ◽  
Vol 35 (11) ◽  
pp. 1370-1371
Author(s):  
Hassan Eldeeb ◽  
Haisen Zhao ◽  
Osama Mohammed
Keyword(s):  
Flux Density ◽  
System Performance ◽  
Induction Machines ◽  
Air Gap ◽  
Drive System ◽  
Self Control ◽  
Motor Drive ◽  
Iron Core ◽  
Insulation Failure ◽  
Motor Drive System

This study investigates the influence of the stator’s turn-to-turn failures (TTFs) on the electromagnetic (EM) fields, such as air gap flux density, flux density in the stator, and rotor iron core inside of direct self-control (DSC) driven induction machines (IMs). The purpose of the investigation is to capture the fault signatures in the air gap EM flux for detecting the stator’s fault at its embryonic stage.

Study of Magnetics Performance of Inset Permanent Magnet Synchronous Machine (Inset - PMSM) Using Finite Element Method

2012 ◽  
Vol 260-261 ◽  
pp. 554-558 ◽  
Author(s):  
Jeeng Min Ling ◽  
Tajuddin Nur
Keyword(s):  
Magnetic Flux ◽  
Permanent Magnet ◽  
Mechanical Energy ◽  
Synchronous Machine ◽  
Air Gap ◽  
Electrical Machine ◽  
Permanent Magnet Synchronous Machine ◽  
Pole Structure ◽  
Rotor Core ◽  
Rotor Structure

Inset Permanent Magnet synchronous machine (Inset - PMSM) is a specific kind of permanent magnet synchronous machine. The magnetic flux in air gap of electrical machine is one of the important parameters and it imply to the electric or mechanical energy of machine. Using radial pole structure, the characteristics of magnetic field in air gap of an Inset PMSM with 6 poles and 36 slots are simulated. In this paper, we simulated and observed the influence of axial channel rotor core of Inset-PMSM to the value/quantity of magnetic flux in air gap. Magnetic flux quality is also considered in this simulation. The simulations were compared with the conventional rotor structure. The flux density per pole in the air gap was measured with three different angles, 00, 250 and 400.

scholarly journals Data-driven Analytical Models of COVID-2019 for Epidemic Prediction, Clinical Diagnosis, Policy Effectiveness and Contact Tracing: A Survey

2020 ◽  
Author(s):  
Ying Mao ◽  
Susiyan Jiang ◽  
Daniel Nametz
Keyword(s):  
Clinical Diagnosis ◽  
Early Stage ◽  
Disease Outbreaks ◽  
Analytical Models ◽  
Contact Tracing ◽  
Policy Effectiveness ◽  
Cross Model ◽  
Related Data ◽  
Comprehensive Survey ◽  
Epidemic Prediction

The widely spread CoronaVirus Disease (COVID)- 19 is one of the worst infectious disease outbreaks in history and has become an emergency of primary international concern. As the pandemic evolves, academic communities have been actively involved in various capacities, including accurate epidemic estimation, fast clinical diagnosis, policy effectiveness evaluation and development of contract tracing technologies. There are more than 23,000 academic papers on the COVID-19 outbreak, and this number is doubling every 20 days while the pandemic is still on-going [1]. The literature, however, at its early stage, lacks a comprehensive survey from a data analytics perspective. In this paper, we review the latest models for analyzing COVID19 related data, conduct post-publication model evaluations and cross-model comparisons, and collect data sources from different projects.

scholarly journals Multi-Band Frequency Window for Time-Frequency Fault Diagnosis of Induction Machines

2019 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bano ◽  
Martin Riera-Guasp ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Time Window ◽  
Early Stage ◽  
Induction Machines ◽  
Frequency Domain Analysis ◽  
Frequency Resolution ◽  
Good Time ◽  
Band Frequency ◽  
Time Frequency ◽  
Multi Band

Induction machines drive many industrial processes, and their unexpected failure can cause heavy production losses. The analysis of the current spectrum can identify online the characteristic fault signatures at an early stage, avoiding unexpected breakdowns. Nevertheless, frequency domain analysis requires stable working conditions, which is not the case for wind generators, motors driving varying loads, etc. In these cases an analysis in the time-frequency domain -such as a spectrogram- is required for detecting faults signatures. The spectrogram is built using the short frequency Fourier transform, but its resolution depends critically on the time window used to generate it: short windows provide good time resolution, but poor frequency resolution, just the opposite than long windows. Therefore, the window must be adapted at each time to the shape of the expected fault harmonics, by highly skilled maintenance personnel. In this paper, this problem is solved with the design of a new multi-band window, which generates simultaneously many different narrow-band current spectrograms, and combines them into a single, high resolution one, without the need of manual adjustments. The proposed method is validated with the diagnosis of bar breakages during the start-up of a commercial induction motor.

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