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\(\%\).
Similar content being viewed by others
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
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
Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, Melbourne
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
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
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
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
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
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
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
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
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
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
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
Kumar DA, Meher SK, Kumari KP (2019) Fusion of progressive granular neural networks for pattern classification. Soft Comput 23(12):4051–4064
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
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
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
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
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
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
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
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
Pedrycz W, Succi G, Sillitti A et al (2015) Data description: a general framework of information granules. Knowl-Based Syst 80:98–108
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
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
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
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
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
Acknowledgements
This work was supported by the National Key Research and Development Program of China under Grant 2019YFB1705100.
Author information
Authors and Affiliations
Corresponding author
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
About this article
Cite this article
Wang, X., Yang, J. & Lu, W. Bearing fault diagnosis algorithm based on granular computing. Granul. Comput. 8, 333–344 (2023). https://doi.org/10.1007/s41066-022-00328-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41066-022-00328-z