scholarly journals Smart-Sensor for the Automatic Detection of Electromechanical Faults in Induction Motors Based on the Transient Stray Flux Analysis

Sensors ◽  
2020 ◽  
Vol 20 (5) ◽  
pp. 1477 ◽  
Author(s):  
Israel Zamudio-Ramírez ◽  
Roque Alfredo Osornio-Ríos ◽  
Jose Alfonso Antonino-Daviu ◽  
Alfredo Quijano-Lopez
Keyword(s):  
Induction Motor ◽  
Induction Motors ◽  
Feature Reduction ◽  
Flux Analysis ◽  
Smart Sensor ◽  
Feed Forward Neural Network ◽  
Statistical Parameters ◽  
Irreversible Damage ◽  
Linear Discriminant ◽  
Motor Test

Induction motors are essential and widely used components in many industrial processes. Although these machines are very robust, they are prone to fail. Nowadays, it is a paramount task to obtain a reliable and accurate diagnosis of the electric motor health, so that a subsequent reduction of the required time and repairing costs can be achieved. The most common approaches to accomplish this task are based on the analysis of currents, which has some well-known drawbacks that may lead to false diagnosis. With the new developments in the technology of the sensors and signal processing field, the possibility of combining the information obtained from the analysis of different magnitudes should be explored, in order to achieve more reliable diagnostic conclusions, before the fault can develop into an irreversible damage. This paper proposes a smart-sensor that explores the weighted analysis of the axial, radial, and combination of both stray fluxes captured by a low-cost, easy setup, non-invasive, and compact triaxial stray flux sensor during the start-up transient through the short time Fourier transform (STFT) and characterizes specific patterns appearing on them using statistical parameters that feed a feature reduction linear discriminant analysis (LDA) and then a feed-forward neural network (FFNN) for classification purposes, opening the possibility of offering an on-site automatic fault diagnosis scheme. The obtained results show that the proposed smart-sensor is efficient for monitoring and diagnosing early induction motor electromechanical faults. This is validated with a laboratory induction motor test bench for individual and combined broken rotor bars and misalignment faults.

scholarly journals Peningkatan Kinerja Motor Induksi Melalui Variasi Diameter Lilitan Kawat

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Yudhi Agussationo ◽  
Keyword(s):  
Induction Motor ◽  
Induction Motors ◽  
Wire Diameter ◽  
Maximum Power ◽  
Maximum Torque ◽  
Motor Test ◽  
The Wire ◽  
Winding Wire ◽  
The Ideal

Testing of 3 phase induction motors with a variety of wire diameters. First, find out the ideal wire size on an induction motor. Second, ratio of the power used on an induction motor with different winding wire sizes. Third, to find out the torque produced by an induction motor with different wire winding sizes. Then, The induction motor test was performed by taking the power data used on two motors with a diameter of 0.6 mm and 0.5 mm winding wire, RPM data and torque produced by an induction motor with a diameter of 0.6 mm and 0.5 mm. So, we can get the results the induction motor with a diameter of 0.6 mm wire uses as maximum power of 549.10 Watt or more than the induction motor with a diameter of a wire wound of 0.5 mm which only uses a maximum power of 345.95 Watt, the wire diameter induction motor winding 0.6 mm produces a maximum torque of 746.92 Nm or greater than an induction motor with a diameter of 0.5 mm winding wire which only produces a maximum torque of 383.97 Nm. So, It can be conclude that the more number of revolutions per minute (RPM), the torque produced will be smaller, then, the greater the torque produced, the more power is used.

scholarly journals Fault Diagnosis of Three Phase Induction Motor Using Current Signal, MSAF-Ratio15 and Selected Classifiers

2017 ◽  
Vol 62 (4) ◽  
pp. 2413-2419 ◽  
Author(s):  
A. Glowacz ◽  
W. Glowacz ◽  
Z. Glowacz ◽  
J. Kozik ◽  
M. Gutten ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Induction Motor ◽  
Induction Motors ◽  
Vibration Signal ◽  
Degradation Process ◽  
Conveyor Belt ◽  
Metallurgical Equipment ◽  
Financial Loss ◽  
Linear Discriminant ◽  
Three Phase

AbstractA degradation of metallurgical equipment is normal process depended on time. Some factors such as: operation process, friction, high temperature can accelerate the degradation process of metallurgical equipment. In this paper the authors analyzed three phase induction motors. These motors are common used in the metallurgy industry, for example in conveyor belt. The diagnostics of such motors is essential. An early detection of faults prevents financial loss and downtimes. The authors proposed a technique of fault diagnosis based on recognition of currents. The authors analyzed 4 states of three phase induction motor: healthy three phase induction motor, three phase induction motor with 1 faulty rotor bar, three phase induction motor with 2 faulty rotor bars, three phase induction motor with faulty ring of squirrel-cage. An analysis was carried out for original method of feature extraction called MSAF-RATIO15 (Method of Selection of Amplitudes of Frequencies – Ratio 15% of maximum of amplitude). A classification of feature vectors was performed by Bayes classifier, Linear Discriminant Analysis (LDA) and Nearest Neighbour classifier. The proposed technique of fault diagnosis can be used for protection of three phase induction motors and other rotating electrical machines. In the near future the authors will analyze other motors and faults. There is also idea to use thermal, acoustic, electrical, vibration signal together.

scholarly journals Gradual Wear Diagnosis of Outer-Race Rolling Bearing Faults through Artificial Intelligence Methods and Stray Flux Signals

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1486
Author(s):  
Israel Zamudio-Ramirez ◽  
Roque A. Osornio-Rios ◽  
Jose A. Antonino-Daviu ◽  
Jonathan Cureño-Osornio ◽  
Juan-Jose Saucedo-Dorantes
Keyword(s):  
Kinematic Chain ◽  
Industrial Applications ◽  
Electric Motors ◽  
Feed Forward Neural Network ◽  
Mechatronic Systems ◽  
Linear Discriminant ◽  
Bearing Faults ◽  
Outer Race ◽  
Non Invasive ◽  
Operation Conditions

Electric motors have been widely used as fundamental elements for driving kinematic chains on mechatronic systems, which are very important components for the proper operation of several industrial applications. Although electric motors are very robust and efficient machines, they are prone to suffer from different faults. One of the most frequent causes of failure is due to a degradation on the bearings. This fault has commonly been diagnosed at advanced stages by means of vibration and current signals. Since low-amplitude fault-related signals are typically obtained, the diagnosis of faults at incipient stages turns out to be a challenging task. In this context, it is desired to develop non-invasive techniques able to diagnose bearing faults at early stages, enabling to achieve adequate maintenance actions. This paper presents a non-invasive gradual wear diagnosis method for bearing outer-race faults. The proposal relies on the application of a linear discriminant analysis (LDA) to statistical and Katz’s fractal dimension features obtained from stray flux signals, and then an automatic classification is performed by means of a feed-forward neural network (FFNN). The results obtained demonstrates the effectiveness of the proposed method, which is validated on a kinematic chain (composed by a 0.746 KW induction motor, a belt and pulleys transmission system and an alternator as a load) under several operation conditions: healthy condition, 1 mm, 2 mm, 3 mm, 4 mm, and 5 mm hole diameter on the bearing outer race, and 60 Hz, 50 Hz, 15 Hz and 5 Hz power supply frequencies

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

IMPROVEMENT OF THE PERFORMANCE OF FINGERPRINT VERIFICATION USING A COMBINATORIAL APPROACH

2018 ◽  
Vol 30 (03) ◽  
pp. 1850019
Author(s):  
Fatemeh Alimardani ◽  
Reza Boostani
Keyword(s):  
Nearest Neighbor ◽  
State Of The Art ◽  
Recognition Rate ◽  
Acceptance Rate ◽  
Feature Reduction ◽  
Support Vector ◽  
Fingerprint Verification ◽  
Linear Discriminant ◽  
Comparative Results ◽  
Verification Systems

Fingerprint verification systems have attracted much attention in secure organizations; however, conventional methods still suffer from unconvincing recognition rate for noisy fingerprint images. To design a robust verification system, in this paper, wavelet and contourlet transforms (CTS) were suggested as efficient feature extraction techniques to elicit a coverall set of descriptive features to characterize fingerprint images. Contourlet coefficients capture the smooth contours of fingerprints while wavelet coefficients reveal its rough details. Due to the high dimensionality of the elicited features, across group variance (AGV), greedy overall relevancy (GOR) and Davis–Bouldin fast feature reduction (DB-FFR) methods were adopted to remove the redundant features. These features were applied to three different classifiers including Boosting Direct Linear Discriminant Analysis (BDLDA), Support Vector Machine (SVM) and Modified Nearest Neighbor (MNN). The proposed method along with state-of-the-art methods were evaluated, over the FVC2004 dataset, in terms of genuine acceptance rate (GAR), false acceptance rate (FAR) and equal error rate (EER). The features selected by AGV were the most significant ones and provided 95.12% GAR. Applying the selected features, by the GOR method, to the modified nearest neighbor, resulted in average EER of [Formula: see text]%, which outperformed the compared methods. The comparative results imply the statistical superiority ([Formula: see text]) of the proposed approach compared to the counterparts.

Starting Processes in Electric Drive of a Passenger Elevator

2021 ◽  
Vol 1 (1) ◽  
pp. 40-49
Author(s):  
S. Rachev ◽  
K. Dimitrova ◽  
D. Koeva ◽  
L. Dimitrov
Keyword(s):  
Coordinate System ◽  
Induction Motor ◽  
Electric Drive ◽  
High Speed ◽  
Induction Motors ◽  
Dynamic Loads ◽  
Design Stage ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Electric Induction

During the operation of electric induction motors used to drive passenger elevators, electro-mechanical transient processes occur, which can cause unacceptable dynamic loads and vibrations. In this regard, research is needed both at the design stage and for operating elevator systems to determine the arising impact currents and torques, in order to propose solutions for their limitation within pre-set limits. Paper deals with starting processes in a two-speed induction motor drive of a passenger elevator. The equations for the voltages of the induction motor are presented in relative units in a coordinate system rotating at a synchronous speed. The values have been obtained for the torques, the rotational frequencies and the currents when starting at a high speed and passing from high to low speed.

Research and Application of Energy Saving Technology of Inverter in Light Load

2014 ◽  
Vol 577 ◽  
pp. 498-501
Author(s):  
Jiu Yan Zhou
Keyword(s):  
Energy Saving ◽  
Induction Motor ◽  
Induction Motors ◽  
Speed Control ◽  
Variable Frequency ◽  
Light Load ◽  
Variable Voltage ◽  
Energy Saving Technology

In order to analysis the variable-voltage energy saving theory and its implementation for induction motor with light-load in detail, This paper gives out a variable-voltage energy saving method, and discusses the design of variable frequency adjusting speed control and the points of attention. It is useful for the application of energy saving technology for induction motors.

scholarly journals Automatic and Online Detection of Rotor Fault State

2018 ◽  
Vol 7 (1) ◽  
pp. 43 ◽  
Author(s):  
Ali Ouanas ◽  
Ammar Medoued ◽  
Salim Haddad ◽  
Mourad Mordjaoui ◽  
D. Sayad
Keyword(s):  
Induction Motor ◽  
Dimensional Space ◽  
Operating Conditions ◽  
Online Detection ◽  
K Nearest Neighbor ◽  
Simple Method ◽  
Important Place ◽  
Motor Test ◽  
Wide Range ◽  
Fault State

In this work, we propose a new and simple method to insure an online and automatic detection of faults that affect induction motor rotors. Induction motors now occupy an important place in the industrial environment and cover an extremely wide range of applications. They require a system installation that monitors the motor state to suit the operating conditions for a given application. The proposed method is based on the consideration of the spectrum of the single-phase stator current envelope as input of the detection algorithm. The characteristics related to the broken bar fault in the frequency domain extracted from the Hilbert Transform is used to estimate the fault severity for different load levels through classification tools. The frequency analysis of the envelope gives the frequency component and the associated amplitude which define the existence of the fault. The clustering of the indicator is chosen in a two-dimensional space by the fuzzy c mean clustering to find the center of each class. The distance criterion, the K-Nearest Neighbor (KNN) algorithm and the neural networks are used to determine the fault type. This method is validated on a 5.5-kW induction motor test bench.Article History: Received July 16th 2017; Received: October 5th 2017; Accepted: Januari 6th 2018; Available onlineHow to Cite This Article: Ouanas, A., Medoued, A., Haddad, S., Mordjaoui, M., and Sayad, D. (2017) Automatic and online Detection of Rotor Fault State. International Journal of Renewable Energy Development, 7(1), 43-52.http://dx.doi.org/10.14710/ijred.7.1.43-52

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