scholarly journals Non‐invasive sound‐based classifier of bearing faults in electric induction motors

2021 ◽  
Author(s):  
Herman Santos ◽  
Paulo Scalassara ◽  
Wagner Endo ◽  
Alessandro Goedtel ◽  
Jacqueline Guedes ◽  
...  
Keyword(s):  
Induction Motors ◽  
Bearing Faults ◽  
Non Invasive ◽  
Electric Induction

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 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.

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.

scholarly journals Combination of non-invasive approaches for general assessment of induction motors

2014 ◽  
Author(s):  
M.J. Picazo-Rodenas ◽  
J. Antonino-Daviu ◽  
V. Climente-Alarcon ◽  
R. Royo-Pastor ◽  
A. Mota-Villar
Keyword(s):  
Induction Motors ◽  
Non Invasive

scholarly journals Detection of Mechanical Abnormalities in Induction Motors by Electric Measurements

1999 ◽  
Vol 5 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Andrzej M. Trzynadlowski
Keyword(s):  
Induction Motors ◽  
Input Power ◽  
Motor Drives ◽  
Operating Conditions ◽  
Induction Motor Drives ◽  
Non Invasive ◽  
Stator Current ◽  
Rotor Imbalance ◽  
Rotor Cage ◽  
Invasive Diagnostics

The paper gives an overview of the issues and means of detection of mechanical abnormalities in induction motors by electric measurements. If undetected and untreated, the worn or damaged bearings, rotor imbalance and eccentricity, broken bars of the rotor cage, and torsional and lateral vibration lead to roughly a half of all failures of induction motor drives. The detection of abnormalities is based on the fact that they cause periodic disturbance of motor variables, such as the speed, torque, current, and magnetic flux. Thus, spectral analysis of those or related quantities may yield a warning about an incipient failure of the drive system. Although the traditional non-invasive diagnostics has mostly been based on the signature analysis of the stator current, other media can also be employed. In particular, the partial instantaneous input power is shown, theoretically and experimentally, to offer distinct advantages under noisy operating conditions. Use of torque and flux estimates is also discussed.

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