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Condition monitoring and fault detection of induction motor based on wavelet denoising with ensemble learning

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Abstract

This paper puts forward a novel fault diagnosis scheme to detect incipient stator fault in induction motor, in addition to estimating the failure severity. Early detection of the stator fault during the motor running can improve the operational efficiency, minimize the risk of further damage to the phase winding, and ensure machine availability. The sensitive fault features are often immersed in random noise and then hard to capture. An improved method to extract the distinctive features by wavelet threshold denoising is developed in this work. Stationary wavelet transform (SWT) is employed to analyze the raw current signals in the time domain. Next, SWT denoising by thresholding is applied to acquired coefficients to accomplish the noise elimination process. Apart from this, statistical norm \(L_1\) is computed from the error signal that clearly demonstrates unique characteristics linked to the fault. The parameter values are arranged and then inputted into the classifier to determine the motor status and qualify the fault intensity. As additional functionality, aiming at the minority training samples cannot be efficiently diagnosed when these samples are imbalanced and limited, ensemble AdaBoost decision tree (EADT) is used to implement the classification task. The EADT can improve the accuracy and solve several problems of traditional machine learning algorithms. The adopted approach is further tested under various loading situations to validate its effectiveness and robustness. To verify the practical feasibility, multiple hardware experiments are carried out on the motor. The results obtained from both Simulink and experiment affirm the superiority of the proposed method as compared to other related works, with a higher level of correctness, reaching 98.48%.

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Abbreviations

f :

Main frequency

p :

Pole pairs’ number

s :

The slip

\(U_{ss}\) :

Stator voltage

\(R_{ss}\) :

Stator resistance

\(I_{ss}\) :

Stator current

\(R_{rr}\) :

Rotor resistance

\(I_{rr}\) :

Rotor current

\(L_{ss}\) :

Inductance of stator

M :

Mutual inductance

\(L_{rr}\) :

Inductance of rotor

\(i_{\alpha ss}\) :

Stator current on stationary\(\alpha \)-axis

\(i_{\beta ss}\) :

Stator current on stationary\(\beta \)-axis

\(\phi _{\alpha rr}\) :

Rotor field on stationary\(\alpha \)-axis

\(\phi _{\beta rr}\) :

Rotor field on stationary\(\beta \)-axis

\(U_{\alpha ss}\) :

Stator voltage at stationary\(\alpha \)-axis

\(U_{\beta ss}\) :

Stator voltage at stationary\(\beta \)-axis

\(\omega _r\) :

Shaft speed

\(E_s\) :

Coefficient of time at stationary\(\alpha \)-axis

\(E_r\) :

Coefficient of time at stationary\(\beta \)-axis

\(S_{ce}\) :

Induced electromagnetic motor torque

\(C_{ee}\) :

Friction coefficient

\(J_f\) :

Moment of inertia

\(S_{st}\) :

Friction torque

\(P_n\) :

Machine power

References

  1. Mayadevi N, Mini VP, Kumar RH, Prins S (2020) Fuzzy-based intelligent algorithm for diagnosis of drive faults in induction motor drive system. Arab J Sci Eng 45(3):1385–1395

    Article  Google Scholar 

  2. Almounajjed A, Ashwin KS, Mani KK, Muhannad A (2021) Investigation techniques for rolling bearing fault diagnosis using machine learning algorithms. In: 2021 5th international conference on intelligent computing and control systems (ICICCS). IEEE, pp 1290–1294

  3. Almounajjed A, Ashwin KS, Mani KK, Mhamad WB (2021) Condition monitoring and fault diagnosis of induction motor-an experimental analysis. In: 2021 7th international conference on electrical energy systems (ICEES). IEEE, pp 433–438

  4. Aghazadeh F, Tahan A, Thomas M (2018) Tool condition monitoring using spectral subtraction and convolutional neural networks in milling process. Int J Adv Manuf Technol 98(9–12):3217–3227

    Article  Google Scholar 

  5. Almounajjed A, Sahoo AK, Kumar MK (2021) Diagnosis of stator fault severity in induction motor based on discrete wavelet analysis. Measurement 109780

  6. Lahoud N, Faucher J, Malec D, Maussion P (2013) Electrical aging of the insulation of low-voltage machines: Model definition and test with the design of experiments. IEEE Trans Industr Electron 60(9):4147–4155

    Article  Google Scholar 

  7. Sonje DM, Kundu P, Chowdhury A (2019) A novel approach for sensitive inter-turn fault detection in induction motor under various operating conditions. Arab J Sci Eng 44(8):6887–6900

    Article  Google Scholar 

  8. Almounajjed A, Kumar AS, Kumar MK, Assaf T (2021) Fault diagnosis and investigation techniques for induction motor. Int J Amb Energy 1–47 (just-accepted)

  9. Bazan GH, Scalassara PR, Endo W, Goedtel A, Palácios RHC, Godoy WF (2018) Stator short-circuit diagnosis in induction motors using mutual information and intelligent systems. IEEE Trans Industr Electron 66(4):3237–3246

    Article  Google Scholar 

  10. Baruti KH, Gurusamy V, Erturk F, Akin B (2020) A robust and practical approach to estimate number of shorted turns in PMSM with ITSC faults. IEEE J Emerg Selected Topics Power Electron

  11. Cherif H, Benakcha A, Laib I, Chehaidia SE, Menacer A, Soudan B et al (2020) Early detection and localization of stator interturn faults based on discrete wavelet energy ratio and neural networks in induction motor. Energy 212:118684

    Article  Google Scholar 

  12. Bessam B, Menacer A, Boumehraz M, Cherif H (2017) Wavelet transform and neural network techniques for inter-turn short circuit diagnosis and location in induction motor. Int J Syst Assur Eng Manage 8(1):478–488

    Article  Google Scholar 

  13. Liu T, Chen H (2005) Detection of stator turn fault in induction motors using the extension of multiple reference frames theory. In: 31st annual conference of IEEE industrial electronics society. IECON, 4 pp

  14. Mirzaeva G, Saad KI (2018) Advanced diagnosis of stator turn-to-turn faults and static eccentricity in induction motors based on internal flux measurement. IEEE Trans Ind Appl 54(4):3961–3970

    Article  Google Scholar 

  15. Vinayak BA, Anand KA, Jagadanand G (2020) Wavelet-based real-time stator fault detection of inverter-fed induction motor. IET Electr Power Appl 14(1):82–90

    Article  Google Scholar 

  16. Palácios RHC, da Silva IN, Goedtel A, Godoy WF, Lopes TD (2017) Diagnosis of stator faults severity in induction motors using two intelligent approaches. IEEE Trans Industr Inf 13(4):1681–1691

    Article  Google Scholar 

  17. Godoy WF, da Silva IN, Goedtel A, Palácios RHC (2015) Evaluation of stator winding faults severity in inverter-fed induction motors. Appl Soft Comput 32:420–431

    Article  Google Scholar 

  18. Bazan GH, Scalassara PR, Endo W, Goedtel A, Godoy WF, Palácios RHC (2017) Stator fault analysis of three-phase induction motors using information measures and artificial neural networks. Electric Power Syst Res 143:347–356

    Article  Google Scholar 

  19. Sugumaran V, Ramachandran KI (2007) Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing. Mech Syst Signal Process 21(5):2237–2247

    Article  Google Scholar 

  20. Lin J, Zuo MJ, Fyfe KR (2004) Mechanical fault detection based on the wavelet de-noising technique. J Vib Acoust 126(1):9–16

    Article  Google Scholar 

  21. Liu X, Pei D, Lodewijks G, Zhao Z, Mei J (2020) Acoustic signal based fault detection on belt conveyor idlers using machine learning. Adv Powder Technol 31(7):2689–2698

    Article  Google Scholar 

  22. Guo X, Shen C, Chen L (2017) Deep fault recognizer: An integrated model to denoise and extract features for fault diagnosis in rotating machinery. Appl Sci 7(1):41

    Article  Google Scholar 

  23. Lei Y, Liu Z, Ouazri J, Lin J (2017) A fault diagnosis method of rolling element bearings based on CEEMDAN. Proc Inst Mech Eng C J Mech Eng Sci 231(10):1804–1815

    Article  Google Scholar 

  24. Zhang Y, Bingham C, Yang Z, Ling BWK, Gallimore M (2014) Machine fault detection by signal denoising-with application to industrial gas turbines. Measurement 58:230–240

    Article  Google Scholar 

  25. Wang R, Sun S, Guo X, Yan D (2018) EMD threshold denoising algorithm based on variance estimation. Circ Syst Sig Process 37(12):5369–5388

    Article  MathSciNet  Google Scholar 

  26. Lu L, Yan J, de Silva CW (2015) Dominant feature selection for the fault diagnosis of rotary machines using modified genetic algorithm and empirical mode decomposition. J Sound Vib 344:464–483

    Article  Google Scholar 

  27. SChegini SN, Bagheri A, Najafi F, (2019) Application of a new EWT-based denoising technique in bearing fault diagnosis. Measurement 144:275–297

  28. Xu G, Liu M, Jiang Z, Söffker D, Shen W (2019) Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning. Sensors 19(5):1088

    Article  Google Scholar 

  29. Zhang T, Chen J, Li F, Zhang K, Lv H, He S, Xu E (2021) Intelligent fault diagnosis of machines with small & imbalanced data: a state-of-the-art review and possible extensions. ISA Trans

  30. Li L, Huang Y, Tao J, Liu C, Li K (2018) Featured temporal segmentation method and AdaBoost-BP detector for internal leakage evaluation of a hydraulic cylinder. Measurement 130:279–289

    Article  Google Scholar 

  31. Wagner T, Sommer S (2020) Bearing fault detection using deep neural network and weighted ensemble learning for multiple motor phase current sources. In: 2020 international conference on innovations in intelligent systems and applications (INISTA). IEEE, pp 1–7

  32. Zhang X, Wang B, Chen X (2015) Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine. Knowl-Based Syst 89:56–85

    Article  Google Scholar 

  33. Slaheddine Zgarni, Hassen Keskes, Ahmed Braham (2018) Nested SVDD in DAG SVM for induction motor condition monitoring. Eng Appl Artif Intell 71:210–215

    Article  Google Scholar 

  34. Chen R, Zhu J, Hu X, Wu H, Xu X, Han X (2021) Fault diagnosis method of rolling bearing based on multiple classifier ensemble of the weighted and balanced distribution adaptation under limited sample imbalance. ISA Trans 114:434–443

    Article  Google Scholar 

  35. Douzas G, Bacao F, Last F (2018) Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Inf Sci 465:1–20

    Article  Google Scholar 

  36. Xia T, Zhuo P, Xiao L, Du S, Wang D, Xi L (2021) Multi-stage fault diagnosis framework for rolling bearing based on OHF Elman AdaBoost-Bagging algorithm. Neurocomputing 433:237–251

    Article  Google Scholar 

  37. Wang Y, Guo Y, Zeng X, Chen J, Kong Y, Sun S (2020) Stator single-line-to-ground fault protection for bus-connected powerformers based on s-transform and bagging ensemble learning. IEEE Access 8:88322–88332

    Article  Google Scholar 

  38. Bachir S, Tnani S, Trigeassou JC, Champenois G (2006) Diagnosis by parameter estimation of stator and rotor faults occurring in induction machines. IEEE Trans Industr Electron 53(3):963–973

    Article  Google Scholar 

  39. Bouzida A, Touhami O, Ibtiouen R, Belouchrani A, Fadel M, Rezzoug A (2010) Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Trans Industr Electron 58(9):4385–4395

    Article  Google Scholar 

  40. Aneesh C, Sachin Kumar P, Hisham M, Soman KP (2015) Performance comparison of variational mode decomposition over empirical wavelet transform for the classification of power quality disturbances using support vector machine. Procedia Comput Sci 46:372–380

    Article  Google Scholar 

  41. Leal MM, Costa FB, Campos JTLS (2019) Improved traditional directional protection by using the stationary wavelet transform. Int J Electric Power Energy Syst 105:59–69

    Article  Google Scholar 

  42. Shifat TA, Jangwook H (2020) An improved stator winding short-circuit fault diagnosis using adaboost algorithm. In: 2020 international conference on artificial intelligence in information and communication (ICAIIC). IEEE, pp 382–387

  43. Maciej Skowron, Teresa Orlowska-Kowalska, Marcin Wolkiewicz, Kowalski Czeslaw T (2020) Convolutional neural network-based stator current data-driven incipient stator fault diagnosis of inverter-fed induction motor. Energies 13(6):1475

    Article  Google Scholar 

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Authors and Affiliations

  1. Electrical Engineering department, C.V Raman Global University, Bhubaneswar, India

    Abdelelah Almounajjed & Ashwin Kumar Sahoo

  2. Electronics and Communication Engineering Department, C.V Raman Global University, Bhubaneswar, India

    Mani Kant Kumar

Authors
  1. Abdelelah Almounajjed
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  2. Ashwin Kumar Sahoo
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  3. Mani Kant Kumar
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Appendix

Appendix

Specifications and parameters of the motor utilized for analysis

$$\begin{aligned} \begin{array}{lll} I_{ss}=4.4\hbox { A};&{} P_n=2600\hbox {W};&{} p=4\\ R_{ss}=4.85\varOmega ;&{} R_{rr}=3.8\varOmega ;&{} L_{ss}=0.274\hbox {H}\\ L_{rr}=0.274H;&{} M=0.258H;&{} J_f=0.031\hbox {Kg.}m^2\\ C_{ee}=0;&{} n_{tot}=464;&{} S_{st}=0.5{\hbox {N}\cdot \hbox {m}}\\ \end{array} \end{aligned}$$

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Almounajjed, A., Sahoo, A.K. & Kumar, M.K. Condition monitoring and fault detection of induction motor based on wavelet denoising with ensemble learning. Electr Eng 104, 2859–2877 (2022). https://doi.org/10.1007/s00202-022-01523-6

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