Minimum Distance-based Detection of Incipient Induction Motor Faults using Rayleigh Quotient Spectrum of Conditioned Vibration Signal

2021 ◽  
pp. 1-1
Author(s):  
Anik Kumar Samanta ◽  
Aurobinda Routray ◽  
Swanand R. Khare ◽  
Arunava Naha
Keyword(s):  
Induction Motor ◽  
Minimum Distance ◽  
Vibration Signal ◽  
Rayleigh Quotient

Pendeteksian Misalignment Menggunakan Multi Level Transformasi Wavelet Haar dan Coiflet pada Motor Induksi

2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
P.P.S Saputra
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Induction Motor ◽  
High Frequency ◽  
High Efficiency ◽  
Vibration Signal ◽  
Normal Operation ◽  
Discrete Wavelet ◽  
High Frequency Signal ◽  
Motor Operation

Currently induction motors are widely used in industry due to strong construction, high efficiency, and cheap maintenance. Machine maintenance is needed to prolong the life of the induction motor. As studied, bearing faults may account for 42% -50% of all motor failures. In general it is due to manufacturing faults, lack of lubrication, and installation errors. Misalignment of motor is one of the installation errors. This paper is concerned to simulation of discrete wavelet transform for identifying misalignment in induction motor. Modelling of motor operation is introduced in this paper as normal operation and two variations of misalignment. For this task, haar and coiflet discrete wavelet transform in first level until fifth level is used to extract vibration signal of motor into high frequency of signal. Then, energy signal and other signal extraction gotten from high frequency signal is evaluated to analysis condition of motor. The results show that haar discrete wavelet transform at thirth level can identify normal motor  and misalignment motor conditions well

Vibration signature analysis for monitoring rotor broken bar in double squirrel cage induction motors based on wavelet analysis

2014 ◽  
Vol 33 (5) ◽  
pp. 1625-1641 ◽  
Author(s):  
Rosario Miceli ◽  
Yasser Gritli ◽  
Antonino Di Tommaso ◽  
Fiorenzo Filippetti ◽  
Claudio Rossi
Keyword(s):  
Wavelet Transform ◽  
Induction Motor ◽  
Vibration Signal ◽  
Operating Conditions ◽  
Single Frequency ◽  
Content Type ◽  
Time Frequency ◽  
Wavelet Transform Analysis ◽  
Cage Induction Motor ◽  
Diagnosis Technique

Purpose – The purpose of this paper is to present a diagnosis technique, for rotor broken bar in double cage induction motor, based on advanced use of wavelet transform analysis. The proposed technique is experimentally validated. Design/methodology/approach – The proposed approach is based on a combined use of frequency sliding and wavelet transform analysis, to isolate the contribution of the rotor fault components issued from vibration signals in a single frequency band. Findings – The proposed technique is reliable for tracking the rotor fault components over time-frequency domain. The quantitative analysis results based on this technique are the proof of its robustness. Research limitations/implications – The validity of the proposed diagnosis approach is not limited to the analysis under steady-state operating conditions, but also for time-varying conditions where rotor fault components are spread in a wide frequency range. Practical implications – The developed approach is best suited for automotive or high power traction systems, in which safe-operating and availability are mandatory. Originality/value – The paper presents a diagnosis technique for rotor broken bar in double cage induction motor base on advanced use of wavelet transform which allows the extraction of the most relevant rotor fault component issued from axial vibration signal and clamping it in a single frequency bandwidth, avoiding confusions with other components and false interpretations.

scholarly journals Detection of Induction Motor Improper Bearing Lubrication by Discrete Wavelet Transforms (DWT) Decomposition

2020 ◽  
Vol 19 (5) ◽  
pp. 347-354
Author(s):  
Bellal Belkacemi ◽  
Salah Saad ◽  
Zine Ghemari ◽  
Fares Zaamouche ◽  
Adel Khazzane
Keyword(s):  
Induction Motor ◽  
Wavelet Transforms ◽  
Vibration Signal ◽  
Complete Analysis ◽  
Discrete Wavelet ◽  
Signal Acquisition ◽  
Three Phase ◽  
Stationary Vibration ◽  
Recorded Data ◽  
Faults Diagnosis

The present paper deals with healthy and improper bearing lubrication signals analysis using Discrete Wavelet Transform (DWT) enhanced by MATLAB/ Wavelets toolbox analysis. The identification of bearing faults from the time or the frequency domain are difficult due to non stationary vibration signal. Therefore, for more accurate faults information and identification of bearing with lubrication defects (improper or absence of lubrication), the DWT is used. The validation of this procedure is conducted by an experimental setup designed for vibration signal acquisition and the complete analysis is finalized by MATLAB/ Wavelets toolbox. The recorded data used for the validation are the signals of healthy and un-lubricated bearing driven at a rotation speed of 1500 rpm by 0.78 KW three phase induction motor. From the obtained results it can be observed that, for medium speeds DWT decomposition enhanced by MATLAB Wavelets Toolbox procedure is efficient for improper lubricated bearing related faults diagnosis and detection.

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.

Detection of Deterioration of Three-phase Induction Motor using Vibration Signals

2019 ◽  
Vol 19 (6) ◽  
pp. 241-249 ◽  
Author(s):  
Adam Glowacz ◽  
Witold Glowacz ◽  
Jarosław Kozik ◽  
Krzysztof Piech ◽  
Miroslav Gutten ◽  
...  
Keyword(s):  
Induction Motor ◽  
Nearest Neighbor ◽  
Recognition Rate ◽  
Vibration Signal ◽  
Diagnostic Techniques ◽  
Support Vector ◽  
Vibration Signals ◽  
Linear Discriminant ◽  
Three Phase ◽  
Vector Sum

Abstract Nowadays detection of deterioration of electrical motors is an important topic of research. Vibration signals often carry diagnostic information of a motor. The authors proposed a setup for the analysis of vibration signals of three-phase induction motors. In this paper rotor fault diagnostic techniques of a three-phase induction motor (TPIM) were presented. The presented techniques used vibration signals and signal processing methods. The authors analyzed the recognition rate of vibration signal readings for 3 states of the TPIM: healthy TPIM, TPIM with 1 broken bar, and TPIM with 2 broken bars. In this paper the authors described a method of the feature extraction of vibration signals Method of Selection of Amplitudes of Frequencies – MSAF-12. Feature vectors were obtained using FFT, MSAF-12, and mean of vector sum. Three methods of classification were used: Nearest Neighbor (NN), Linear Discriminant Analysis (LDA), and Linear Support Vector Machine (LSVM). The obtained results of analyzed classifiers were in the range of 97.61 % – 100 %.

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