# Diagnostics of mechanical and electrical faults in induction motors using wavelet-based features of vibration and current through support vector machine algorithms for various operating conditions

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## Abstract

Fault diagnosis of induction motors (IMs) is always a challenging task in the practical industrial field, and it is even more challenging in the case of inadequate information of IM working conditions. In this paper, a new methodology for fault detection has been proposed for IMs to detect various electrical and mechanical faults as well as their severities, where the data are unavailable at required operating conditions (i.e., speed and load) based on wavelet and support vector machine (SVM). For this, the radial, axial and tangential vibrations, and three-phase current signals are acquired from IMs having different faults. The acquired time domain signal is then transformed to time–frequency signals using continuous wavelet transform (CWT). Ten different base wavelets are used to investigate the impact of different wavelet function on the fault diagnosis of IMs. Statistical features are extracted based on the CWT, and then appropriate feature(s) are selected using the wrapper model. These features are fed to the SVM to detect whether a defect has occurred. The fault detection is performed for identical speed and load case using a number of mother wavelets. To analyze the robustness of the present system, diagnosis is attempted for various operational conditions of IMs. The result showed that the feature(s) selected using the Shannon wavelet diagnose, the fault categories of IM more accurately as compared to other wavelets, and remarkably found to be robust at all working conditions of IMs. The work is finally extended to perform the fault diagnosis when limited information is available for the training. From the results, it is observed that the proposed methodology does not only take care of the practical problem of unavailability of data at different operating conditions, but also shows good performance and takes low computation time, which are vital requirements of a condition monitoring and diagnostic system.

## Keywords

Induction motor (IM) fault Continuous wavelet transform (CWT) Multi-fault diagnostic Support vector machine (SVM) Intermediary working condition## Abbreviations

- ABCA
Artificial-bee-colony algorithm

- AI
Artificial intelligence

- ANN
Artificial neural network

- BF
Bearing fault

- BP
Back propagation

- BR
Bowed rotor

- BRB
Broken rotor bar

- CACO
Chaotic ant colony optimization algorithm

- CF
Crest factor

- CWC
Continues wavelet coefficient

- CWT
Continues wavelet transform

- CV
Cross-validation

- DAG
Direct acyclic graph

- DAQ
Data acquisition system

- DWT
Discrete wavelet transform

- FDI
Fault diagnosis and isolation

- FL
Fuzzy logic

- FFT
Fast Fourier transform

- FNN
Fuzzy neural network

- GA
Genetic algorithm

- IM
Induction motor

- LIBSVM
A library for support vector machine

- MCSA
Motor current signature analysis

- SLBS
Single-level basis selection

- MFS
Machine fault simulator

- MLP
Multilayer perception

- MR
Misaligned rotor

- ND
No defect condition of induction motor

- OVA
One versus all

- OVO
One versus one

- PSO
Particle swarm optimization

- PUF
Phase unbalance fault

- PUF1
Phase unbalance fault level-1

- PUF2
Phase unbalance fault level-2

- RBF
Radial basis function

- RMS
Root mean square

- RUWPT
Recursive un-decimated wavelet packet transform

- RWE
Relative wavelet energy

- SOM
Self-organizing map

- SVM
Support vector machine

- SWF
Stator winding fault

- SWF1
Stator winding fault level-1

- SWF2
Stator winding fault level-2

- SVs
Support vectors

- UR
Unbalanced rotor

- VFD
Variable frequency drive

- WPT
Wavelet packet transform

- WT
Wavelet transform

## Notes

### Acknowledgements

The authors are thankful to Dr. Dhruba Jyoti Bordoloi, Technical Officer at IIT Guwahati for his support during experimentation.

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