scholarly journals Smart-Sensors to Estimate Insulation Health in Induction Motors via Analysis of Stray Flux

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1658 ◽  
Author(s):  
Israel Zamudio-Ramirez ◽  
Roque Alfredo Osornio-Rios ◽  
Miguel Trejo-Hernandez ◽  
Rene de Jesus Romero-Troncoso ◽  
Jose Alfonso Antonino-Daviu
Keyword(s):  
Induction Motors ◽  
Early Stage ◽  
Industrial Sector ◽  
Smart Sensors ◽  
Discrete Wavelet ◽  
Electrical Machine ◽  
Smart Sensor ◽  
Time Frequency ◽  
Essential Components ◽  
Winding Insulation

Induction motors (IMs) are essential components in industrial applications. These motors have to perform numerous tasks under a wide variety of conditions, which affects performance and reliability and gradually brings faults and efficiency losses over time. Nowadays, the industrial sector demands the necessary integration of smart-sensors to effectively diagnose faults in these kinds of motors before faults can occur. One of the most frequent causes of failure in IMs is the degradation of turn insulation in windings. If this anomaly is present, an electric motor can keep working with apparent normality, but factors such as the efficiency of energy consumption and mechanical reliability may be reduced considerably. Furthermore, if not detected at an early stage, this degradation could lead to the breakdown of the insulation system, which could in turn cause catastrophic and irreversible failure to the electrical machine. This paper proposes a novel methodology and its application in a smart-sensor to detect and estimate the healthiness of the winding insulation in IMs. This methodology relies on the analysis of the external magnetic field captured by a coil sensor by applying suitable time-frequency decomposition (TFD) tools. The discrete wavelet transform (DWT) is used to decompose the signal into different approximation and detail coefficients as a pre-processing stage to isolate the studied fault. Then, due to the importance of diagnosing stator winding insulation faults during motor operation at an early stage, this proposal introduces an indicator based on wavelet entropy (WE), a single parameter capable of performing an efficient diagnosis. A smart-sensor is able to estimate winding insulation degradation in IMs using two inexpensive, reliable, and noninvasive primary sensors: a coil sensor and an E-type thermocouple sensor. The utility of these sensors is demonstrated through the results obtained from analyzing six similar IMs with differently induced severity faults.

Nonlinear Time-Frequency Control of Permanent Magnet Synchronous Motors

2016 ◽  
Author(s):  
Xin Wang ◽  
C. Steve Suh
Keyword(s):  
Permanent Magnet ◽  
Frequency Control ◽  
Permanent Magnet Synchronous Motor ◽  
Synchronous Motor ◽  
Discrete Wavelet ◽  
Permanent Magnet Synchronous Motors ◽  
Synchronous Motors ◽  
Time Frequency ◽  
Essential Components ◽  
Wide Range

Permanent magnet synchronous motors are essential components in a wide range of applications in which their unique benefits are explored. However, in order for a permanent magnet synchronous motor to achieve satisfactory performance, particular control frameworks are essential. After all, permanent magnet synchronous motor is an AC machine, which is characterized by its complex structure and strongly coupled system states. Therefore, in order for it to achieve satisfactory dynamic performance, advanced control techniques are the only solution. This paper presents a precise speed control of permanent magnet synchronous motors using the nonlinear time-frequency control concept. The novel aspect of this nonlinear time-frequency control, which is an integration of discrete wavelet transformation and adaptive control, is its ability in analyzing the fundamental temporal and spectral qualities inherent of a permanent magnet synchronous motor and exerting control signals accordingly. Simulation results verifies that the proposed nonlinear time-frequency control scheme is feasible for alleviating the nonlinear behavior of the permanent magnet synchronous motor which hampers the tracking of speed with desired precision.

scholarly journals A New Hybrid Diagnosis of Bearing Faults Based on Time-Frequency Images and Sparse Representation

2020 ◽  
Vol 37 (6) ◽  
pp. 907-918
Author(s):  
Ilhan Aydin ◽  
Seyfullah Kaner
Keyword(s):  
Deep Learning ◽  
Induction Motors ◽  
Early Stage ◽  
Training Data ◽  
Bearing Faults ◽  
Time Frequency ◽  
Histograms Of Oriented Gradients ◽  
Diagnosis Method ◽  
And Performance ◽  
Learning Machine

Induction motors are an essential component of many applications in industry due to their robust and simple construction. Since bearing faults are the most occurred fault type in the induction motors, it is important to implement the fault detection procedure at an early stage to prevent a sudden interruption of industrial systems. In recent years, deep learning-based techniques have become important tools for converting raw data into images and for producing high-quality images. However, deep learning-based techniques are still difficult to apply in real-time because the techniques require large training data, which slows down the learning process. In the present study, we propose a novel bearing faults diagnosis method at different operating speeds and load conditions. We obtain the time-frequency (TF) representation by applying continuous wavelet analysis to the raw vibration signals. The results of TF representation is recorded as an image. We apply co-occurrence Histograms of Oriented Gradients (coHOG) to the image to obtain features and classify the features with extreme learning machine with a sparse classifier (ELMSRC) to diagnose faults. We obtained better results in terms of time and performance compared with the proposed method of other classification and deep learning techniques.

scholarly journals BASIC ALGORITHM FOR INDUCTION MOTORS ROTOR FAULTS PRE-DETERMINATION

2017 ◽  
Vol 3 ◽  
pp. 254
Author(s):  
Aleksei Petrov ◽  
Anton Rassolkin ◽  
Toomas Vaimann ◽  
Anouar Belahcen ◽  
Ants Kallaste ◽  
...  
Keyword(s):  
Induction Motors ◽  
Early Stage ◽  
Diagnostic Techniques ◽  
Electrical Machine ◽  
Basic Algorithm ◽  
Rotor Faults ◽  
Area Of Interest ◽  
Squirrel Cage ◽  
Squirrel Cage Induction Motor ◽  
Cage Induction Motor

Due to importance of squirrel cage induction motor in today’s industry, the fault detection on that type of motors has become a highly developed area of interest for researchers. The electrical machine is designed for stable operations with minimum noise and vibrations under the normal conditions. When the fault emerges, some additional distortions appear. The necessity to detect the fault in an early stage, to prevent further damage of the equipment due to fault propagation, is one of the most important features of any condition monitoring or diagnostic techniques for electrical machines nowadays. In this paper possible induction motors faults classified and basic algorithm for rotor faults pre-determination is presented.

scholarly journals Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

Electronics ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 6 ◽  
Author(s):  
Jordi Burriel-Valencia ◽  
Ruben Puche-Panadero ◽  
Javier Martinez-Roman ◽  
Angel Sapena-Bano ◽  
Manuel Pineda-Sanchez ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expert Systems ◽  
Induction Motors ◽  
Early Stage ◽  
Diagnostic System ◽  
Transient Regime ◽  
Economic Losses ◽  
Diagnostic Systems ◽  
Time Frequency ◽  
Cage Induction Motor

Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine’s condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.

scholarly journals Increasing Electric Vehicles Reliability by Non-Invasive Diagnosis of Motor Winding Faults

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2510
Author(s):  
Konrad Górny ◽  
Piotr Kuwałek ◽  
Wojciech Pietrowski
Keyword(s):  
Electric Vehicles ◽  
Diagnostic Method ◽  
Induction Motors ◽  
Early Stage ◽  
Short Circuit ◽  
Damage Classification ◽  
Non Invasive ◽  
Signal Features ◽  
Empirical Wavelet Transform ◽  
Simulation Results

The article proposes a proprietary approach to the diagnosis of induction motors allowing increasing the reliability of electric vehicles. This approach makes it possible to detect damage in the form of an inter-turn short-circuit at an early stage of its occurrence. The authors of the article describe an effective diagnostic method using the extraction of diagnostic signal features using an Enhanced Empirical Wavelet Transform and an algorithm based on the method of Ensemble Bagged Trees. The article describes in detail the methodology of the carried out research, presents the method of extracting features from the diagnostic signal and describes the conclusions resulting from the research. Phase current waveforms obtained from a real object as well as simulation results based on the field-circuit model of an induction motor were used as a diagnostic signal in the research. In order to determine the accuracy of the damage classification, simple metrics such as accuracy, sensitivity, selectivity, precision as well as complex metrics weight F1 and macro F1 were used.

scholarly journals Smart Sensor for Online Detection of Multiple-Combined Faults in VSD-Fed Induction Motors

Sensors ◽  
2012 ◽  
Vol 12 (9) ◽  
pp. 11989-12005 ◽  
Author(s):  
Armando G. Garcia-Ramirez ◽  
Roque A. Osornio-Rios ◽  
David Granados-Lieberman ◽  
Arturo Garcia-Perez ◽  
Rene J. Romero-Troncoso
Keyword(s):  
Induction Motors ◽  
Smart Sensor ◽  
Online Detection

scholarly journals An innovative image fusion algorithm based on wavelet transform and discrete fast curvelet transform

2011 ◽  
Vol 1 (3) ◽  
Author(s):  
T. Sumathi ◽  
M. Hemalatha
Keyword(s):  
Wavelet Transform ◽  
Image Fusion ◽  
Curvelet Transform ◽  
Relevant Information ◽  
Good Time ◽  
Discrete Wavelet ◽  
Fusion Algorithm ◽  
Time Frequency ◽  
Fused Image

AbstractImage fusion is the method of combining relevant information from two or more images into a single image resulting in an image that is more informative than the initial inputs. Methods for fusion include discrete wavelet transform, Laplacian pyramid based transform, curvelet based transform etc. These methods demonstrate the best performance in spatial and spectral quality of the fused image compared to other spatial methods of fusion. In particular, wavelet transform has good time-frequency characteristics. However, this characteristic cannot be extended easily to two or more dimensions with separable wavelet experiencing limited directivity when spanning a one-dimensional wavelet. This paper introduces the second generation curvelet transform and uses it to fuse images together. This method is compared against the others previously described to show that useful information can be extracted from source and fused images resulting in the production of fused images which offer clear, detailed information.

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia

2016 ◽  
Vol 27 (02) ◽  
pp. 1650039 ◽  
Author(s):  
Francesco Carlo Morabito ◽  
Maurizio Campolo ◽  
Nadia Mammone ◽  
Mario Versaci ◽  
Silvana Franceschetti ◽  
...  
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Early Stage ◽  
Processing System ◽  
Fine Tuning ◽  
Permutation Entropy ◽  
Support Vector ◽  
Progressive Dementia ◽  
Time Frequency ◽  
Jakob Disease

A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt–Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features from the time-frequency representation of the EEG signals through continuous wavelet transform (CWT). An average measure of complexity of the EEG signal obtained by permutation entropy (PE) is also included. The dimensionality of the feature space is reduced through a multilayer processing system based on the recently emerged deep learning (DL) concept. The DL processor includes a stacked auto-encoder, trained by unsupervised learning techniques, and a classifier whose parameters are determined in a supervised way by associating the known category labels to the reduced vector of high-level features generated by the previous processing blocks. The supervised learning step is carried out by using either support vector machines (SVM) or multilayer neural networks (MLP-NN). A subset of EEG from patients suffering from Alzheimer’s Disease (AD) and healthy controls (HC) is considered for differentiating CJD patients. When fine-tuning the parameters of the global processing system by a supervised learning procedure, the proposed system is able to achieve an average accuracy of 89%, an average sensitivity of 92%, and an average specificity of 89% in differentiating CJD from RPD. Similar results are obtained for CJD versus AD and CJD versus HC.

scholarly journals The Impact of Thermal Degradation on Properties of Electrical Machine Winding Insulation Material

2016 ◽  
Vol 52 (4) ◽  
pp. 2951-2960 ◽  
Author(s):  
Malgorzata Sumislawska ◽  
Konstantinos N. Gyftakis ◽  
Darren F. Kavanagh ◽  
Malcolm D. McCulloch ◽  
Keith J. Burnham ◽  
...  
Keyword(s):  
Thermal Degradation ◽  
Electrical Machine ◽  
Insulation Material ◽  
Winding Insulation ◽  
The Impact
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