Skip to main content

Bearing fault diagnosis algorithm based on granular computing

Abstract

Granular computing, as an emerging soft computing classification method, provides a theoretical framework for solving complex classification problems based on information granulation and is one of the core technologies for simulating human thinking and solving complex classification problems in the current computational intelligence field. In this paper, we propose a design method of bearing fault diagnosis model based on granular computing: Convolutional Neural Networks-Granular Computing (CNN-GC). The method consists of two main components: fault features extraction and fault types determination. In this case, the bearing fault features are extracted using a convolutional neural network (CNN) with hyperparameter optimization to obtain bearing fault features with different output dimensions; fault types determination is obtained by using the extracted fault features as the input of hypersphere information granule based on granular computing. Compared with existing bearing fault diagnosis models, the CNN-GC model proposed in this paper, which accomplishes the conversion from numerical space to grain space, can obtain more accurate values and better grain size results. The superiority of the CNN-GC model in terms of accuracy and interpretability was demonstrated by the Case Western Reserve University(CWRU) bearing dataset.The experimental results show an accuracy rate of 99.8\(\%\).

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Data availability

Datasets involved in this manuscript are publicly available from https://engineering.case.edu/bearingdatacenter.

References

  • Amezcua J, Melin P (2019) A new fuzzy learning vector quantization method for classification problems based on a granular approach. Granul Comput 4(2):197–209. https://doi.org/10.1007/s41066-018-0120-7

    Article  Google Scholar 

  • Bas E, Egrioglu E, Kolemen E (2021) Training simple recurrent deep artificial neural network for forecasting using particle swarm optimization. Granul Comput. https://doi.org/10.1007/s41066-021-00274-2

    Article  Google Scholar 

  • Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, Melbourne

    MATH  Google Scholar 

  • Ejegwa PA (2019) Improved composite relation for pythagorean fuzzy sets and its application to medical diagnosis. Granul Comput. https://doi.org/10.1007/s41066-019-00156-8

    Article  Google Scholar 

  • Fu C, Lu W, Pedrycz W et al (2020) Rule-based granular classification: a hypersphere information granule-based method. Knowl-Based Syst 194(105):500. https://doi.org/10.1016/j.knosys.2020.105500

    Article  Google Scholar 

  • Gacek A, Pedrycz W (2006) A granular description of ECG signals. IEEE Trans Biomed Eng 153:1972–1982. https://doi.org/10.1109/TBME.2006.881782

    Article  Google Scholar 

  • Gan M, Wang C et al (2016) Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech Syst Signal Process 72:92–104

    Article  Google Scholar 

  • Guidotti R, Monreale A, Ruggieri S et al (2018) A survey of methods for explaining black box models. ACM Comput Surv CSUR 51:1–42

    Google Scholar 

  • Harmouche J, Delpha C, Diallo D (2014) Improved fault diagnosis of ball bearings based on the global spectrum of vibration signals. IEEE Trans Energy Convers 30(1):376–383

    Article  Google Scholar 

  • Hu X, Pedrycz W, Wang X (2018) Fuzzy classifiers with information granules in feature space and logic-based computing. Pattern Recogn 80:156–167. https://doi.org/10.1016/j.patcog.2018.03.011

    Article  Google Scholar 

  • Janssens O, Slavkovikj V, Vervisch B et al (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345. https://doi.org/10.1016/j.jsv.2016.05.027

    Article  Google Scholar 

  • Jia F, Lei Y, Lin J et al (2016) Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech Syst Signal Process 72:303–315

    Article  Google Scholar 

  • Joshi R (2021) Multi-criteria decision-making based on bi-parametric exponential fuzzy information measures and weighted correlation coefficients. Granul Comput. https://doi.org/10.1007/s41066-020-00249-9

    Article  Google Scholar 

  • Kaburlasos VG, Tsoukalas V, Moussiades L (2014) Fcknn: a granular knn classifier based on formal concepts. In: 2014 IEEE international conference on fuzzy systems (FUZZ-IEEE), pp 61–68

  • Kim DW, Lee HJ, Park JB et al (2006) Ga-based construction of fuzzy classifiers using information granules. Int J Control Autom Syst 4(2):187–196

    Google Scholar 

  • Kumar DA, Meher SK, Kumari KP (2019) Fusion of progressive granular neural networks for pattern classification. Soft Comput 23(12):4051–4064

    Article  Google Scholar 

  • Lecun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  • Li X, Zhang W, Ding Q (2019) Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliab Eng Syst Saf 182:208–218

    Article  Google Scholar 

  • Liu H, Cocea M (2017) Granular computing-based approach for classification towards reduction of bias in ensemble learning. Granul Comput 2(3):1–9. https://doi.org/10.1007/s41066-016-0034-1

    Article  Google Scholar 

  • Lu C, Wang ZY, Qin WL et al (2017) Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process 130:377–388. https://doi.org/10.1016/j.sigpro.2016.07.028

    Article  Google Scholar 

  • Lu W, Chen X, Pedrycz W et al (2015) Using interval information granules to improve forecasting in fuzzy time series. Int J Approx Reason 57:1–18

    Article  Google Scholar 

  • Lu W, Shan D, Pedrycz W et al (2018) Granular fuzzy modeling for multidimensional numeric data: a layered approach based on hyperbox. IEEE Trans Fuzzy Syst 27(4):775–789

    Article  Google Scholar 

  • Lu W, Pedrycz W, Yang J, et al (2020) Granular description with multi-granularity for multidimensional data: a cone-shaped fuzzy set-based method. In: IEEE transactions on fuzzy systems

  • Lu W, Ma C, Pedrycz W et al (2021) Design of granular model: a method driven by hyper-box iteration granulation. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2021.3124235

    Article  Google Scholar 

  • Moosavi-Dezfooli SM, Fawzi A, Frossard P (2016) Deepfool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2574–2582

  • Ouyang T, Pedrycz W, Reyes-Galaviz OF et al (2021) Granular description of data structures: a two-phase design. IEEE Trans Cybern 51:1902–1912. https://doi.org/10.1109/TCYB.2018.2887115

    Article  Google Scholar 

  • Pedrycz W, Succi G, Sillitti A et al (2015) Data description: a general framework of information granules. Knowl-Based Syst 80:98–108

    Article  Google Scholar 

  • Singh P, Huang YP (2020) A four-way decision-making approach using interval-valued fuzzy sets, rough set and granular computing: a new approach in data classification and decision-making. Granul Comput 5(3):397–409. https://doi.org/10.1007/s41066-019-00165-7

    Article  Google Scholar 

  • Sun W, Shao S, Zhao R et al (2016) A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 89:171–178

    Article  Google Scholar 

  • Sun W, Shao S, Zhao R, et al (2016b) A sparse auto-encoder-based deep neural network approach for induction motor faults classification. Measurement 171–178

  • Szegedy C, Ioffe S, Vanhoucke V, et al (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Thirty-first AAAI conference on artificial intelligence

  • Tan J, Lu W, An J, et al (2015) Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder. In: The 27th Chinese control and decision conference (2015 CCDC)

  • Wang B, Lei Y, Li N et al (2018) A hybrid prognostics approach for estimating remaining useful life of rolling element bearings. IEEE Trans Reliab 69(1):401–412

    Article  Google Scholar 

  • Yao J, Yao Y (2002) Induction of classification rules by granular computing. In: International conference on rough sets and current trends in computing, pp 331–338

  • Zhang W, Li C, Peng G et al (2018) A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process 100:439–453. https://doi.org/10.1016/j.ymssp.2017.06.022

    Article  Google Scholar 

  • Zhao HH, Liu H (2019) Multiple classifiers fusion and CNN feature extraction for handwritten digits recognition. Granul Comput. https://doi.org/10.1007/s41066-019-00158-6

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Key Research and Development Program of China under Grant 2019YFB1705100.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Wei Lu.

Ethics declarations

Conflict of interest

The authors declare they have no conflicts of interest.

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

Verify currency and authenticity via CrossMark

Cite this article

Wang, X., Yang, J. & Lu, W. Bearing fault diagnosis algorithm based on granular computing. Granul. Comput. (2022). https://doi.org/10.1007/s41066-022-00328-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s41066-022-00328-z

Keywords

  • Bearing fault diagnosis
  • CNN-GC
  • Granular computing
  • Hypersphere information granules
  • CNN