scholarly journals FPGA based Signal Prefiltering System for Vibration Analysis of Induction Motor Failure Detection

2012 ◽  
Vol 4 ◽  
pp. 442-448 ◽  
Author(s):  
Saikat Kumar Shome ◽  
Uma Datta ◽  
S.R.K. Vadali
Keyword(s):  
Induction Motor ◽  
Vibration Analysis ◽  
Failure Detection ◽  
Motor Failure

FPGA-Based Multiple-Channel Vibration Analyzer for Industrial Applications in Induction Motor Failure Detection

2010 ◽  
Vol 59 (1) ◽  
pp. 63-72 ◽  
Author(s):  
L.M.C. Medina ◽  
R. de Jesus Romero-Troncoso ◽  
E. Cabal-Yepez ◽  
J. de Jesus Rangel-Magdaleno ◽  
J.R. Millan-Almaraz
Keyword(s):  
Induction Motor ◽  
Failure Detection ◽  
Industrial Applications ◽  
Multiple Channel ◽  
Motor Failure

Virtual Instrumentation Based Analysis of Induction Motor

2014 ◽  
Vol 984-985 ◽  
pp. 970-976
Author(s):  
Memala W. Abitha ◽  
V. Rajini
Keyword(s):  
Induction Motor ◽  
Life Time ◽  
Open Circuit ◽  
Virtual Instrumentation ◽  
Repair Work ◽  
Three Phase ◽  
Stationary Reference Frame ◽  
Stator Fault ◽  
Motor Failure ◽  
Vector Approach

The three phase induction motor is a popularly used machine in many of the industries, which is well known for its robustness, reliability, cost effectiveness, efficient and safe operation. The unnoticed manufacturing failure, mistakes during repair work, exceeding life time may be some of the causes of the induction motor failure, which may lead to the unknown shut down time of the industry. The condition monitoring plays important role as it has the influence on the production of materials and profit. In our work, the induction motor is modelled using stationary reference frame and analysed for single phasing stator fault. The techniques used in detecting the single phasing (open circuit) failures are Park’s vector approach and Fast Fourier Transform (FFT). Park’s vector approach is used for detecting the faults occurring at various phases and FFT is used for detecting the faults of the induction motor working under no load and varying loading conditions.

Non-invasive intelligent monitoring system for fault detection in induction motor based on bio piezoelectric sensor using ANN

2022 ◽  
Author(s):  
Massine GANA ◽  
Hakim ACHOUR ◽  
Kamel BELAID ◽  
Zakia CHELLI ◽  
Mourad LAGHROUCHE ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Induction Motor ◽  
Vibration Analysis ◽  
Low Cost ◽  
Piezoelectric Sensor ◽  
Integrated System ◽  
Easy Access ◽  
Non Invasive ◽  
Artificial Neural

Abstract This paper presents a design of a low-cost integrated system for the preventive detection of unbalance faults in an induction motor. In this regard, two non-invasive measurements have been collected then monitored in real time and transmitted via an ESP32 board. A new bio-flexible piezoelectric sensor developed previously in our laboratory, was used for vibration analysis. Moreover an infrared thermopile was used for non-contact temperature measurement. The data is transmitted via Wi-Fi to a monitoring station that intervenes to detect an anomaly. The diagnosis of the motor condition is realized using an artificial neural network algorithm implemented on the microcontroller. Besides, a Kalman filter is employed to predict the vibrations while eliminating the noise. The combination of vibration analysis, thermal signature analysis and artificial neural network provides a better diagnosis. It ensures efficiency, accuracy, easy access to data and remote control, which significantly reduces human intervention.

Forced Vibration Analysis of Induction Motor

2020 ◽  
Vol 2020 (0) ◽  
pp. 207
Author(s):  
Akira Saito ◽  
Mirei Kaneko ◽  
Takumi Namatame ◽  
Tatsuya Suzuki
Keyword(s):  
Induction Motor ◽  
Forced Vibration ◽  
Vibration Analysis ◽  
Forced Vibration Analysis

Use of Neural Networks in Diagnostics of Rolling-Element Bearing of the Induction Motor

2013 ◽  
Vol 588 ◽  
pp. 333-342 ◽  
Author(s):  
Leon Swędrowski ◽  
Kazimierz Duzinkiewicz ◽  
Michał Grochowski ◽  
Tomasz Rutkowski
Keyword(s):  
Neural Networks ◽  
Induction Motor ◽  
Measurement Data ◽  
Failure Detection ◽  
Rolling Element Bearing ◽  
Bearing Defect ◽  
Instantaneous Current ◽  
Rolling Element ◽  
Current Change ◽  
Element Bearing

Bearing defect is statistically the most frequent cause of an induction motor fault. The research described in the paper utilized the phenomenon of the current change in the induction motor with bearing defect. Methods based on the analysis of the supplying current are particularly useful when it is impossible to install diagnostic devices directly on the motor. The presented method of rolling-element bearing diagnostics used indirect transformation, namely Clark transformation. It determines the vector of the spatial stator current based on instantaneous current measurements of the induction motor supply phases current. The analysis of the processed measurement data used multilayered, one-directional neural networks, which are particularly attractive due to their nonlinear structure and ability to learn. During the research 40 bearings: undamaged, with damages of three types and various degrees of fault extent, were used. The conducted research proves the efficiency of neural networks for detection and recognition of faults in induction motor bearings. In case of tests of the unknown state bearings, an efficiency approach to failure detection equaled 77%.

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