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.

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 Tick-borne zoonoses and commonly used diagnostic methods in human and veterinary medicine

2021 ◽  
Author(s):  
Andrea Springer ◽  
Antje Glass ◽  
Julia Probst ◽  
Christina Strube
Keyword(s):  
Veterinary Medicine ◽  
Diagnostic Tests ◽  
One Health ◽  
Animal Health ◽  
Diagnostic Techniques ◽  
Diagnostic Methods ◽  
Diagnostic Tools ◽  
Zoonotic Pathogens ◽  
Health Concept ◽  
The One

AbstractAround the world, human health and animal health are closely linked in terms of the One Health concept by ticks acting as vectors for zoonotic pathogens. Animals do not only maintain tick cycles but can either be clinically affected by the same tick-borne pathogens as humans and/or play a role as reservoirs or sentinel pathogen hosts. However, the relevance of different tick-borne diseases (TBDs) may vary in human vs. veterinary medicine, which is consequently reflected by the availability of human vs. veterinary diagnostic tests. Yet, as TBDs gain importance in both fields and rare zoonotic pathogens, such as Babesia spp., are increasingly identified as causes of human disease, a One Health approach regarding development of new diagnostic tools may lead to synergistic benefits. This review gives an overview on zoonotic protozoan, bacterial and viral tick-borne pathogens worldwide, discusses commonly used diagnostic techniques for TBDs, and compares commercial availability of diagnostic tests for humans vs. domestic animals, using Germany as an example, with the aim of highlighting existing gaps and opportunities for collaboration in a One Health framework.

scholarly journals The S66 Non-Covalent Interactions Benchmark Reconsidered Using Explicitly Correlated Methods Near the Basis Set Limit

2018 ◽  
Vol 71 (4) ◽  
pp. 238 ◽  
Author(s):  
Manoj K. Kesharwani ◽  
Amir Karton ◽  
Nitai Sylvetsky ◽  
Jan M. L. Martin
Keyword(s):  
Computational Cost ◽  
The Other ◽  
Basis Set ◽  
Basis Sets ◽  
Explicitly Correlated ◽  
Non Covalent Interactions ◽  
Explicitly Correlated Methods ◽  
The One ◽  
High Level ◽  
Covalent Interactions

The S66 benchmark for non-covalent interactions has been re-evaluated using explicitly correlated methods with basis sets near the one-particle basis set limit. It is found that post-MP2 ‘high-level corrections’ are treated adequately well using a combination of CCSD(F12*) with (aug-)cc-pVTZ-F12 basis sets on the one hand, and (T) extrapolated from conventional CCSD(T)/heavy-aug-cc-pV{D,T}Z on the other hand. Implications for earlier benchmarks on the larger S66×8 problem set in particular, and for accurate calculations on non-covalent interactions in general, are discussed. At a slight cost in accuracy, (T) can be considerably accelerated by using sano-V{D,T}Z+ basis sets, whereas half-counterpoise CCSD(F12*)(T)/cc-pVDZ-F12 offers the best compromise between accuracy and computational cost.

scholarly journals Progress of non-invasive diagnostic of liver fibrosis: review of modern laboratory methods

2020 ◽  
pp. 224-232
Author(s):  
E. A. Kulebina ◽  
A. N. Surkov
Keyword(s):  
Liver Fibrosis ◽  
Gold Standard ◽  
Diagnostic Techniques ◽  
Serum Markers ◽  
Diagnostic Systems ◽  
Non Invasive ◽  
Type Iv ◽  
Acid Type ◽  
Experimental Laboratory ◽  
Invasive Techniques

Fibrosis and cirrhosis are traditionally diagnosed by making a biopsy. However, in recent decades, scientists around the world have shown that the accepted “gold standard of diagnosis” – morphological assessment of biopsy – has a number of limitations. The search for non-invasive techniques to diagnose fibrosis has led to the development of many scales using laboratory indices. Non-invasive diagnostic techniques are safer for the patient than liver biopsy. In addition, they can be repeated in a dynamic to assess the condition of the liver over time. Most currently available non-invasive diagnostic techniques are considerably cheaper than the accepted “gold standard”. Their practical use is increasing every year, and in a number of countries the frequency of liver biopsies in viral hepatitis B and C is steadily decreasing due to the development of serum and imaging diagnostic systems. Recent studies show that the assessment of the degree of fibrosis by non-invasive methods is as accurate as a morphological study. In recent years, a number of serum markers have been considered as non-invasive diagnostics of the stages of liver fibrosis, among which the largest number of studies are devoted to hyaluronic acid, type IV collagen, and their combination with various common laboratory tests. The latest non-invasive techniques will make a significant paradigm shift in the evaluation of liver fibrosis in the near future. In this review we have analyzed widely used as well as experimental laboratory techniques used in the diagnosis of liver fibrosis.

Y-Net: Dual-branch Joint Network for Semantic Segmentation

2021 ◽  
Vol 17 (4) ◽  
pp. 1-22
Author(s):  
Yizhen Chen ◽  
Haifeng Hu
Keyword(s):  
Feature Vector ◽  
State Of The Art ◽  
Computational Cost ◽  
Receptive Fields ◽  
Semantic Segmentation ◽  
Global Context ◽  
Multi Level ◽  
The One ◽  
Public Datasets ◽  
High Level

Most existing segmentation networks are built upon a “ U -shaped” encoder–decoder structure, where the multi-level features extracted by the encoder are gradually aggregated by the decoder. Although this structure has been proven to be effective in improving segmentation performance, there are two main drawbacks. On the one hand, the introduction of low-level features brings a significant increase in calculations without an obvious performance gain. On the other hand, general strategies of feature aggregation such as addition and concatenation fuse features without considering the usefulness of each feature vector, which mixes the useful information with massive noises. In this article, we abandon the traditional “ U -shaped” architecture and propose Y-Net, a dual-branch joint network for accurate semantic segmentation. Specifically, it only aggregates the high-level features with low-resolution and utilizes the global context guidance generated by the first branch to refine the second branch. The dual branches are effectively connected through a Semantic Enhancing Module, which can be regarded as the combination of spatial attention and channel attention. We also design a novel Channel-Selective Decoder (CSD) to adaptively integrate features from different receptive fields by assigning specific channelwise weights, where the weights are input-dependent. Our Y-Net is capable of breaking through the limit of singe-branch network and attaining higher performance with less computational cost than “ U -shaped” structure. The proposed CSD can better integrate useful information and suppress interference noises. Comprehensive experiments are carried out on three public datasets to evaluate the effectiveness of our method. Eventually, our Y-Net achieves state-of-the-art performance on PASCAL VOC 2012, PASCAL Person-Part, and ADE20K dataset without pre-training on extra datasets.

An appropriate biomechanical model of seated human subjects exposed to whole-body vibration

2021 ◽  
pp. 146441932110394
Author(s):  
Raj Desai ◽  
Anirban Guha ◽  
Pasumarthy Seshu
Keyword(s):  
Degrees Of Freedom ◽  
Human Subjects ◽  
Computational Cost ◽  
Biomechanical Model ◽  
Whole Body ◽  
Body Parts ◽  
Lumped Parameter ◽  
Long Duration ◽  
The One ◽  
The Right

Long duration automobile-induced vibration is the cause of many ailments to humans. Predicting and mitigating these vibrations through seat requires a good model of seated human body. A good model is the one that strikes the right balance between modelling difficulty and simulation results accuracy. Increasing the number of body parts which have been separately modelled and increasing the number of ways these parts are connected to each other increase the number of degrees of freedom of the entire model. A number of such models have been reported in the literature. These range from simple lumped parameter models with limited accuracy to advanced models with high computational cost. However, a systematic comparison of these models has not been reported till date. This work creates eight such models ranging from 8 to 26 degrees of freedom and tries to identify the model which strikes the right balance between modelling complexity and results accuracy. A comparison of the models’ prediction with experimental data published in the literature allows the identification of a 12 degree of freedom backrest supported model as optimum for modelling complexity and prediction accuracy.

scholarly journals Geomatics and Soft Computing Techniques for Infrastructural Monitoring

2020 ◽  
Vol 12 (4) ◽  
pp. 1606 ◽  
Author(s):  
Vincenzo Barrile ◽  
Antonino Fotia ◽  
Giovanni Leonardi ◽  
Raffaele Pucinotti
Keyword(s):  
Soft Computing ◽  
Structural Model ◽  
Control Unit ◽  
Machine Learning Techniques ◽  
Analytical Models ◽  
Risk Level ◽  
Behavior Monitoring ◽  
Learning Techniques ◽  
Soft Computing Techniques ◽  
The One

Structural Health Monitoring (SHM) allows us to have information about the structure under investigation and thus to create analytical models for the assessment of its state or structural behavior. Exceeded a predetermined danger threshold, the possibility of an early warning would allow us, on the one hand, to suspend risky activities and, on the other, to reduce maintenance costs. The system proposed in this paper represents an integration of multiple traditional systems that integrate data of a different nature (used in the preventive phase to define the various behavior scenarios on the structural model), and then reworking them through machine learning techniques, in order to obtain values to compare with limit thresholds. The risk level depends on several variables, specifically, the paper wants to evaluate the possibility of predicting the structure behavior monitoring only displacement data, transmitted through an experimental transmission control unit. In order to monitor and to make our cities more “sustainable”, the paper describes some tests on road infrastructure, in this contest through the combination of geomatics techniques and soft computing.

Joint inversion of seismic traveltime and frequency-domain airborne electromagnetic data for hydrocarbon exploration

Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. U9-U22 ◽  
Author(s):  
Jide Nosakare Ogunbo ◽  
Guy Marquis ◽  
Jie Zhang ◽  
Weizhong Wang
Keyword(s):  
Frequency Domain ◽  
Joint Inversion ◽  
Sedimentary Environment ◽  
Computational Cost ◽  
Velocity Model ◽  
Two Dimensions ◽  
Hydrocarbon Exploration ◽  
Three Dimensions ◽  
Data Sets ◽  
The One

Geophysical joint inversion requires the setting of a few parameters for optimum performance of the process. However, there are yet no known detailed procedures for selecting the various parameters for performing the joint inversion. Previous works on the joint inversion of electromagnetic (EM) and seismic data have reported parameter applications for data sets acquired from the same dimensional geometry (either in two dimensions or three dimensions) and few on variant geometry. But none has discussed the parameter selections for the joint inversion of methods from variant geometry (for example, a 2D seismic travel and pseudo-2D frequency-domain EM data). With the advantage of affordable computational cost and the sufficient approximation of a 1D EM model in a horizontally layered sedimentary environment, we are able to set optimum joint inversion parameters to perform structurally constrained joint 2D seismic traveltime and pseudo-2D EM data for hydrocarbon exploration. From the synthetic experiments, even in the presence of noise, we are able to prescribe the rules for optimum parameter setting for the joint inversion, including the choice of initial model and the cross-gradient weighting. We apply these rules on field data to reconstruct a more reliable subsurface velocity model than the one obtained by the traveltime inversions alone. We expect that this approach will be useful for performing joint inversion of the seismic traveltime and frequency-domain EM data for the production of hydrocarbon.

Sensitization of the SST Turbulence Model to Rotation and Curvature by Applying the Spalart–Shur Correction Term

2009 ◽  
Vol 131 (4) ◽  
Author(s):  
Pavel E. Smirnov ◽  
Florian R. Menter
Keyword(s):  
Turbulent Flows ◽  
Turbulence Model ◽  
Transport Model ◽  
Correction Term ◽  
Computational Cost ◽  
Turbulence Models ◽  
Reynolds Stress Transport Model ◽  
Wide Range ◽  
Sst Model ◽  
The One

A rotation-curvature correction suggested earlier by Spalart and Shur (1997, “On the Sensitization of Turbulence Models to Rotation and Curvature,” Aerosp. Sci. Technol., 1(5), pp. 297–302) for the one-equation Spalart–Allmaras turbulence model is adapted to the shear stress transport model. This new version of the model (SST-CC) has been extensively tested on a wide range of both wall-bounded and free shear turbulent flows with system rotation and/or streamline curvature. Predictions of the SST-CC model are compared with available experimental and direct numerical simulations (DNS) data, on the one hand, and with the corresponding results of the original SST model and advanced Reynolds stress transport model (RSM), on the other hand. It is found that in terms of accuracy the proposed model significantly improves the original SST model and is quite competitive with the RSM, whereas its computational cost is significantly less than that of the RSM.

Verification of a Finite Element Method for Solving One-Dimensional Equations of Blood Flow Incorporating a Viscoelastic Wall Model

2009 ◽  
Author(s):  
Rashmi Raghu ◽  
Charles A. Taylor
Keyword(s):  
Blood Flow ◽  
Surgical Planning ◽  
Computational Cost ◽  
Three Dimensional ◽  
Momentum Equation ◽  
Cross Sectional ◽  
One Dimensional ◽  
The One ◽  
Modeling Flow ◽  
Three Dimensional Simulations

The one-dimensional (1-D) equations of blood flow consist of the conservation of mass equation, balance of momentum equation and a wall constitutive equation with arterial flow rate, cross-sectional area and pressure as the variables. 1-D models of blood flow enable the solution of large networks of blood vessels including wall deformability. Their level of detail is appropriate for applications such as modeling flow and pressure waves in surgical planning and their computational cost is low compared to three-dimensional simulations.

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