Skip to main content
SpringerLink
Log in

A fault classification method using dynamic centered one-dimensional local angular binary pattern for a PMSM and drive system

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Nowadays, fault classification of the electric motors has become a hot-topic research area. Therefore, many machine learning method has been presented to create an intelligent fault detection system for electric motor. In this work, a novel classification method is presented for five different fault conditions of a motor and its drive system by using dynamic centered one-dimensional local angular binary pattern (DC-1D-LABP). It is proposed a novel multi-leveled feature extraction network, and one-dimensional discrete wavelet transform (1D-DWT) is used in order to create levels. Features are extracted from each level by using the proposed DC-1D-LABP. Neighborhood component analysis (NCA) and ReliefF-based 2-layered feature selector (NCARF) are used to select most discriminative features, and four conventional classifiers are selected for testing. A novel fault dataset is acquired, and this dataset is used for tests. Four cases were defined according to input current signal. The achieved best classification accuracy rates are 0.9692, 0.9571, 0.9650 and 1 for Case 1, Case 2, Case 3 and Case 4, respectively. These results indicate that the proposed DC-1D-LABP-based method is very effective for fault classification. Consequently, it is proposed a highly accurate and cognitive method for a fault classification in this study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. AQ Tao, S Li, SL Zhi (2015) Current residual vector-based open-switch fault diagnosis of inverters in PMSM drive systems. IEEE Trans Power Elect 30(5):2814–2827

    Article  Google Scholar 

  2. Welchko Ba, Lipo TA, Jahns TM, Schulz SE (2004) Fault tolerant three-phase AC motor drive topologies: A comparison of features, cost, and limitations. IEEE Trans Power Elect 19(4):1108–1116

    Article  Google Scholar 

  3. Jian-Jian Z, Yong C, Zhang-Yong C, Anjian Z (2019) Open-switch fault diagnosis method in voltage-source inverters based on phase currents. IEEE Access 7:63619–63625

    Article  Google Scholar 

  4. Shaoyong Y, Dawei X, Angus B, Philip M, Li R, Peter T (2010) Condition monitoring for device reliability in power electronic converters: A review

  5. Zhang W, Dehong X, Enjeti PN, Li H, Hawke JT, Krishnamoorthy HS (2014) Survey on fault-tolerant techniques for power electronic converters. IEEE Trans Power Elect 29(12):6319–6331

    Article  Google Scholar 

  6. Mingchen G, Peng X, Li Z, Kai S (2015) A SiC-based T-type three-phase three-level gridtied inverter. In Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015, pages 1116–1121. Institute of Electrical and Electronics Engineers Inc.,

  7. Goodem PV, Chow MY (1995) Using a neural/fuzzy system to extract heuristic knowledge of incipient faults in induction motors: part I-methodology. IEEE Trans Ind Elect 42(2):131–138

    Article  Google Scholar 

  8. Merizalde Y, Hernández-Callejo L, Duque-Perez O (2017) State of the art and trends in the monitoring, detection and diagnosis of failures in electric induction motors. Energies 10(7):1–34

    Article  Google Scholar 

  9. Dai X, Gao Z (2013) From model, signal to knowledge: a data-driven perspective of fault detection and diagnosis. IEEE Trans Ind Inf 9(4):2226–2238

    Article  Google Scholar 

  10. Wen L, Li X, Gao L, Zhang Y (2018) A new convolutional neural network-based data-driven fault diagnosis method. IEEE Trans Ind Elect 65(7):5990–5998

    Article  Google Scholar 

  11. Seera M, Lim CP, Nahavandi S, Loo CK (2014) Condition monitoring of induction motors: a review and an application of an ensemble of hybrid intelligent models. Expert Syst Appl 41(10):4891–4903

    Article  Google Scholar 

  12. Subhasis N, Toliyat HA (1999) Condition monitoring and fault diagnosis of electrical machines - a review. In Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), volume 1, pages 197–204. IEEE

  13. Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques-part II: fault diagnosis with knowledge-based and hybrid/active approaches. IEEE Trans Ind Elect 62(6):3768–3774

    Google Scholar 

  14. Zhiwei G, Carlo C, Steven XD (2015) A survey of fault diagnosis and fault-tolerant techniques-part I: Fault diagnosis with model-based and signal-based approaches

  15. Principi E, Rossetti D, Squartini S, Piazza F (2019) Unsupervised electric motor fault detection by using deep autoencoders. IEEE/CAA J Automatica Sinica 6(2):441–451

    Article  Google Scholar 

  16. Bouzid MBK, Champenois G (2013) New expressions of symmetrical components of the induction motor under stator faults. IEEE Trans Ind Elect 60(9):4093–4102

    Article  Google Scholar 

  17. Mohammad ZA, Md Nasmus SKS, Xiaodong L, Yu Z, Ting H (2019) Machine learning-based fault diagnosis for single- and multi-faults in induction motors using measured stator currents and vibration signals. In IEEE Transactions on Industry Applications, volume 55, pages 2378–2391. Institute of Electrical and Electronics Engineers Inc.,

  18. Filippetti F, Franceschini G, Carla T (1993) Neural networks aided on-line diagnostics of induction motor rotor faults. In Conference Record - IAS Annual Meeting (IEEE Industry Applications Society), volume 1, pages 316–323. Publ by IEEE

  19. Farag S, Lin BK, Habetler TG, Schlag JH (1995) An unsupervised, on-line system for induction motor fault detection using stator current monitoring. IEEE Trans Ind Appl 31(6):1280–1286

    Article  Google Scholar 

  20. Martins JF, Fernão Pires V, Pires AJ (2007) Unsupervised neural-network-based algorithm for an on-line diagnosis of three-phase induction motor stator fault. IEEE Trans Ind Elect 54(1):259–264

    Article  Google Scholar 

  21. Hamid Nejjari, Mohamed El Hachemi, Benbouzid. (2000) Monitoring and diagnosis of induction motors electrical faults using a current Park’s vector pattern learning approach. IEEE Trans Ind Appl. 36(3):730–735

  22. Boukra Tahar, Lebaroud Abdesselam, Clerc Guy (2013) Statistical and neural-network approaches for the classification of induction machine faults using the ambiguity plane representation. IEEE Trans Ind Elect 60(9):4034–4042

    Article  Google Scholar 

  23. Seera Manjeevan, Lim Chee Peng, Ishak Dahaman, Singh Harapajan (2012) Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART Model. IEEE Trans Neural Netw Learn Syst 23(1):97–108

    Article  Google Scholar 

  24. Shoeb Hussain, Mohammad Abid Bazaz (2016) Sensorless control of PMSM drive using Neural Network Observer. In 2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pages 1–5. IEEE

  25. Ignacio Martin-Diaz, Daniel Morinigo-Sotelo, Oscar Duque-Perez, Rene J. Romero-Troncoso (2018) An Experimental Comparative Evaluation of Machine Learning Techniques for Motor Fault Diagnosis under Various Operating Conditions. In IEEE Transactions on Industry Applications, volume 54, pages 2215–2224. Institute of Electrical and Electronics Engineers Inc.,

  26. Godoy Wagner Fontes, Nunes Ivan, da Silva Alessandro, Goedtel Rodrigo Henrique, Palácios Cunha, Lopes Tiago Drummond (2016) Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter. IET Elect Power Appl 10(5):430–439

    Article  Google Scholar 

  27. Adams Stephen, Meekins Ryan, Beling Peter A, Farinholt Kevin, Brown Nathan, Polter Sherwood, Dong Qing (2019) Hierarchical fault classification for resource constrained systems. Mech Syst Sig Proc. 134

  28. Ruiz Magda, Mujica Luis E, Alférez Santiago, Acho Leonardo, Tutivén Christian, Vidal Yolanda, Rodellar José, Pozo Francesc (2018) Wind turbine fault detection and classification by means of image texture analysis. Mech Syst Sig Proc 107:149–167

    Article  Google Scholar 

  29. Prashant K, Ananda SH (2020) Review on machine learning algorithm based fault detection in induction motors. Arch Comput Meth Eng 28(3):1929–1940

    Google Scholar 

  30. Boztas G, Aydogmus O, Caner M, Guldemir H (2019) Design, optimisation and implementation of low-voltage synchronous reluctance motor for solar-powered systems. IET Power Elect, 12(7)

  31. Tuncer Turker, Ertam Fatih (2020) Neighborhood component analysis and relieff based survival recognition methods for hepatocellular carcinoma. Physica A Stat Mech Appl, 540:123143

  32. Li Chun Na, Shao Yuan Hai, Wang Zhen, Deng Nai Yang (2019) Robust bilateral Lp-norm two-dimensional linear discriminant analysis. Inf Sci 500:274–297

    Article  MathSciNet  Google Scholar 

  33. Ganaie MA, Tanveer M, Suganthan PN (2020) Oblique Decision Tree Ensemble via Twin Bounded SVM. Exp Syst Appl, 143

  34. Xiao Jianli (2019) SVM and KNN ensemble learning for traffic incident detection. Physica A Stat Mech Appl 517:29–35

    Article  Google Scholar 

  35. Shi Bibo, Liu Jundong (2018) Nonlinear metric learning for kNN and SVMs through geometric transformations. Neurocomputing 318:18–29

    Article  Google Scholar 

  36. Ömer Faruk Ertuǧrul, YIlmaz Kaya, Ramazan Tekin, Mehmet Nuri Almali (2016) Detection of Parkinson’s disease by shifted one dimensional local binary patterns from gait. Exp Syst Appl, 56:156–163

  37. Tuncer Turker, Dogan Sengul (2019) Novel dynamic center based binary and ternary pattern network using M4 pooling for real world voice recognition. Appl Acoust 156:176–185

    Article  Google Scholar 

  38. Naït-Ali A, Adam O, Motsch JF (2000) Modelling and recognition of brainstem auditory evoked potentials using Symlet wavelet. ITBM-RBM 21(3):150–157

    Article  Google Scholar 

  39. Qin Chen, Song Shiji, Huang Gao, Zhu Lei (2015) Unsupervised neighborhood component analysis for clustering. Neurocomputing 168:609–617

    Article  Google Scholar 

  40. Robnik-Šikonja Marko, Kononenko Igor (2003) Theoretical and empirical analysis of relieff and rrelieff. Mach Learn 53(1–2):23–69

    Article  Google Scholar 

  41. Yogendra Kumar Jain, Santosh Kumar Bhandare Min max normalization based data perturbation method for privacy protection. Comp Sci, 4

  42. Syed Umar Amin, Mansour Alsulaiman, Ghulam Muhammad, Mohamed Amine Mekhtiche, M. Shamim Hossain (2019) Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion. Fut Gen Comp Syst, 101:542–554

  43. Mohammad Gohari, Amir Mohammad Eydi (2019) Modelling of shaft unbalance: modelling a multi discs rotor using k- nearest neighbor and decision tree algorithms. Measurement, page 107253

  44. Elkhadir Zyad, Mohammed Benattou (2019) A cyber network attack detection based on GM Median Nearest Neighbors LDA. Comput Sec 86:63–74

    Article  Google Scholar 

  45. Tang Xianlun, Wang Ting, Yiming Du, Dai Yuyan (2019) Motor imagery EEG recognition with KNN-based smooth auto-encoder. Artif Intell Med, 101

  46. Tuncer Turker, Dogan Sengul (2019) A novel octopus based Parkinson’s disease and gender recognition method using vowels. Appl Acoust, 155:75–83

  47. Melih Kuncan, Kaplan Kaplan, Mehmet Recep Minaz, Yilmaz Kaya, Metin Ertunc H (2020) A novel feature extraction method for bearing fault classification with one dimensional ternary patterns. ISA Trans, 100:346–357

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Faculty of Technology, Firat University, Elazig, 23200, Turkey

    Gullu Boztas

  2. Department of Digital Forensics Engineering, Faculty of Technology, Firat University, Elazig, 23200, Turkey

    Turker Tuncer

Authors
  1. Gullu Boztas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Turker Tuncer
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gullu Boztas.

Ethics declarations

Conflict of interest

The authors have NO affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boztas, G., Tuncer, T. A fault classification method using dynamic centered one-dimensional local angular binary pattern for a PMSM and drive system. Neural Comput & Applic 34, 1981–1992 (2022). https://doi.org/10.1007/s00521-021-06534-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-021-06534-1

Keywords

Search

Navigation