# 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.

## References

- 1.Bazzi AM, Krein PT (2010) Review of methods for real-time loss minimization in induction machines. IEEE Trans Ind Appl 46(6):2319–2328CrossRefGoogle Scholar
- 2.Henao H, Capolino GA, Fernandez-Cabanas M, Filippetti F, Bruzzese C, Strangas E, Hedayati-Kia S (2014) Trends in fault diagnosis for electrical machines: a review of diagnostic techniques. IEEE Ind Electron Mag 8(2):31–42CrossRefGoogle Scholar
- 3.Nandi S, Toliyat HA, Li X (2005) Condition monitoring and fault diagnosis of electrical motors—a review. IEEE Trans Energy Convers 20(4):719–729CrossRefGoogle Scholar
- 4.Lee S, Bryant MD, Karlapalem L (2006) Model-and information theory-based diagnostic method for induction motors. J Dyn Syst Meas Control 128(3):584–591CrossRefGoogle Scholar
- 5.Alsaedi MA (2015) Fault diagnosis of three-phase induction motor: a review. Opt Spec Issue Appl Opt Signal Process 4(1–1):1–8Google Scholar
- 6.Thomson WT, Orpin P (2002) Current and vibration monitoring for fault diagnosis and root cause analysis of induction motor drives. In: Proceedings of the thirty-first turbomachinery symposium, pp 61–67Google Scholar
- 7.Seshadrinath J, Singh B, Panigrahi BK (2014) Investigation of vibration signatures for multiple fault diagnosis in variable frequency drives using complex wavelets. IEEE Trans Power Electron 29(2):936–945CrossRefGoogle Scholar
- 8.Li W, Mechefχe CK (2006) Detection of induction motor faults: a comparison of stator current, vibration and acoustic methods. J Vib Control 12(2):165–188CrossRefGoogle Scholar
- 9.Chebil J, Noel G, Mesbah M, Deriche M (2009) Wavelet decomposition for the detection and diagnosis of faults in rolling element bearings. Jordan J Mech Ind Eng 3(4):260–267Google Scholar
- 10.Rafiee J, Rafiee MA, Tse PW (2010) Application of mother wavelet functions for automatic gear and bearing fault diagnosis. Expert Syst Appl 37(6):4568–4579CrossRefGoogle Scholar
- 11.Bordoloi DJ, Tiwari R (2014) Support vector machine based optimization of multi-fault classification of gears with evolutionary algorithms from time–frequency vibration data. Measurement 55:1–14CrossRefGoogle Scholar
- 12.Yaqub MF, Gondal I, Kamruzzaman J (2011) Envelope-wavelet packet transform for machine condition monitoring. World Acad Sci Eng Technol 59:1597–1603Google Scholar
- 13.Zhao H, Yao R, Xu L, Yuan Y, Li G, Deng W (2018) Study on a novel fault damage degree identification method using high-order differential mathematical morphology gradient spectrum entropy. Entropy 20(9):682CrossRefGoogle Scholar
- 14.Silva VAD, Pederiva R (2013) Fault detection in induction motors based on artificial intelligence. In: Surveillance 7, international conference—October 29–30, 2013, Institute of Technology of Chartres, FranceGoogle Scholar
- 15.Samanta B (2004) Gear fault detection using artificial neural networks and support vector machines with genetic algorithms. Mech Syst Signal Process 18(3):625–644CrossRefGoogle Scholar
- 16.Deng W, Chen R, He B, Liu Y, Yin L, Guo J (2012) A novel two-stage hybrid swarm intelligence optimization algorithm and application. Soft Comput 16(10):1707–1722CrossRefGoogle Scholar
- 17.Deng W, Zhao H, Liu J, Yan X, Li Y, Yin L, Ding C (2015) An improved CACO algorithm based on adaptive method and multi-variant strategies. Soft Comput 19(3):701–713CrossRefGoogle Scholar
- 18.Deng W, Zhao H, Yang X, Xiong J, Sun M, Li B (2017) Study on an improved adaptive PSO algorithm for solving multi-objective gate assignment. Appl Soft Comput 59:288–302CrossRefGoogle Scholar
- 19.Gangsar P, Tiwari R (2017) Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms. Mech Syst Signal Process 94:464–481CrossRefGoogle Scholar
- 20.Filippetti F, Franceschini G, Tassoni C, Vas P (2000) Recent developments of induction motor drives fault diagnosis using AI techniques. IEEE Trans Ind Electron 47(5):994–1004CrossRefGoogle Scholar
- 21.Zhao H, Sun M, Deng W, Yang X (2016) A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy 19(1):14CrossRefGoogle Scholar
- 22.Deng W, Yao R, Zhao H, Yang X, Li G (2017) A novel intelligent diagnosis method using optimal LS-SVM with improved PSO algorithm. Soft Comput. https://doi.org/10.1007/s00500-017-2940-9 CrossRefGoogle Scholar
- 23.Peng ZK, Chu FL (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221CrossRefGoogle Scholar
- 24.Konar P, Chattopadhyay P (2011) Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs). Appl Soft Comput 11(6):4203–4211CrossRefGoogle Scholar
- 25.Yan R (2007) Base wavelet selection criteria for non-stationary vibration analysis in bearing health diagnosis. University of Massachusetts Amherst, ProQuest Dissertations Publishing, 3275786Google Scholar
- 26.Rafiee J, Rafiee MA, Prause N, Tse PW (2009) Application of Daubechies 44 in machine fault diagnostics. In: 2nd international conference on computer, control and communication. IC4 2009. IEEE, pp 1–6Google Scholar
- 27.Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using continuous wavelet transform. Appl Soft Comput 11(2):2300–2312CrossRefGoogle Scholar
- 28.Chattopadhyay P, Konar P (2014) Feature extraction using wavelet transform for multi-class fault detection of induction motor. J Inst Eng India Ser B 95(1):73–81CrossRefGoogle Scholar
- 29.Vishwakarma, H. O., Sajan, K. S., Maheshwari, B., & Dhiman, Y. D. (2015, August). Intelligent bearing fault monitoring system using support vector machine and wavelet packet decomposition for induction motors. In Power and Advanced Control Engineering (ICPACE), 2015 International Conference on (pp. 339-343). IEEEGoogle Scholar
- 30.Deng W, Zhang S, Zhao H, Yang X (2018) A novel fault diagnosis method based on integrating empirical wavelet transform and fuzzy entropy for motor bearing. IEEE Access 6(1):35042–35056CrossRefGoogle Scholar
- 31.Zgarni S, Keskes H, Braham A (2018) Nested SVDD in DAG SVM for induction motor condition monitoring. Eng Appl Artif Intell 71:210–215CrossRefGoogle Scholar
- 32.Palácios RHC, da Silva IN, Goedtel A, Godoy WF (2015) A comprehensive evaluation of intelligent classifiers for fault identification in three-phase induction motors. Electr Power Syst Res 127:249–258CrossRefGoogle Scholar
- 33.Bessam B, Menacer A, Boumehraz M, Cherif H (2016) Detection of broken rotor bar faults in induction motor at low load using neural network. ISA Trans 64:241–246CrossRefGoogle Scholar
- 34.Sadooghi MS, Khadem SE (2018) Improving one class support vector machine novelty detection scheme using nonlinear features. Pattern Recogn 83:14–33CrossRefGoogle Scholar
- 35.Baccarini LMR, Silva VVR, De Menezes BR, Caminhas WM (2011) SVM practical industrial application for mechanical faults diagnostic. Expert Syst Appl 38(6):6980–6984CrossRefGoogle Scholar
- 36.Zhao H, Zuo S, Hou M, Liu W, Yu L, Yang X, Deng W (2018) A novel adaptive signal processing method based on enhanced empirical wavelet transform technology. Sensors 18(10):3323CrossRefGoogle Scholar
- 37.Devi NR, Sarma DS, Rao PR (2016) Diagnosis and classification of stator winding insulation faults on a three-phase induction motor using wavelet and MNN. IEEE Trans Dielectr Electr Insul 23(5):2543–2555CrossRefGoogle Scholar
- 38.Keskes H, Braham A (2015) Recursive undecimated wavelet packet transform and DAG SVM for induction motor diagnosis. IEEE Trans Ind Inf 11(5):1059–1066CrossRefGoogle Scholar
- 39.Yan R, Gao RX, Chen X (2014) Wavelets for fault diagnosis of rotary machines: a review with applications. Signal Process 96:1–15CrossRefGoogle Scholar
- 40.Da Silva AM, Povinelli RJ, Demerdash NA (2008) Induction machine broken bar and stator short-circuit fault diagnostics based on three-phase stator current envelopes. IEEE Trans Ind Electron 55(3):1310–1318CrossRefGoogle Scholar
- 41.Gangsar P, Tiwari R (2018) Multifault diagnosis of induction motor at intermediate operating conditions using wavelet packet transform and support vector machine. Trans ASME J Dyn Syst Meas Control 140(8):081014CrossRefGoogle Scholar
- 42.Vapnik VN (1999) An overview of statistical learning theory. IEEE Trans Neural Netw 10(5):988–999CrossRefGoogle Scholar
- 43.Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167CrossRefGoogle Scholar
- 44.Hsu CW, Lin CJ (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRefGoogle Scholar
- 45.Chang CC, Lin CJ (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol TIST 2(3):27:1–27:27Google Scholar
- 46.Junsheng C, Dejie Y, Yu Y (2007) Application of an impulse response wavelet to fault diagnosis of rolling bearings. Mech Syst Signal Process 21(2):920–929CrossRefGoogle Scholar
- 47.Tiwari R (2017) Rotor systems: analysis and identification. CRC Press, Taylor and Francis Group, Boca RatonGoogle Scholar
- 48.Du W, Tao J, Li Y, Liu C (2014) Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mech Syst Signal Process 43(1):57–75CrossRefGoogle Scholar
- 49.Muralidharan V, Sugumaran V, Pandey G (2011) SVM based fault diagnosis of monoblock centrifugal pump using stationary wavelet features. Int J Des Manuf Technol IJDMT 2(1):1–6Google Scholar