Skip to main content
main-content
Top

Hint

Swipe to navigate through the articles of this issue

31-05-2022 | Original Paper

Bearing fault diagnosis algorithm based on granular computing

Authors: Xiaoyong Wang, Jianhua Yang, Wei Lu

Published in: Granular Computing

Login to get access
share
SHARE

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\(\%\).
Literature
go back to reference Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, Melbourne MATH Bezdek JC (2013) Pattern recognition with fuzzy objective function algorithms. Springer Science & Business Media, Melbourne MATH
go back to reference 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 CrossRef 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 CrossRef
go back to reference 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 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
go back to reference 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 CrossRef 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 CrossRef
go back to reference 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 CrossRef 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 CrossRef
go back to reference 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 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
go back to reference 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 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
go back to reference Kumar DA, Meher SK, Kumari KP (2019) Fusion of progressive granular neural networks for pattern classification. Soft Comput 23(12):4051–4064 CrossRef Kumar DA, Meher SK, Kumari KP (2019) Fusion of progressive granular neural networks for pattern classification. Soft Comput 23(12):4051–4064 CrossRef
go back to reference 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 CrossRef 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 CrossRef
go back to reference 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 CrossRef 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 CrossRef
go back to reference 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 CrossRef 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 CrossRef
go back to reference 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, 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
go back to reference 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 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
go back to reference Pedrycz W, Succi G, Sillitti A et al (2015) Data description: a general framework of information granules. Knowl-Based Syst 80:98–108 CrossRef Pedrycz W, Succi G, Sillitti A et al (2015) Data description: a general framework of information granules. Knowl-Based Syst 80:98–108 CrossRef
go back to reference 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 CrossRef 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 CrossRef
go back to reference 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 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
go back to reference 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 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
go back to reference 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) 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)
go back to reference 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 CrossRef 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 CrossRef
go back to reference 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 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
Metadata
Title
Bearing fault diagnosis algorithm based on granular computing
Authors
Xiaoyong Wang
Jianhua Yang
Wei Lu
Publication date
31-05-2022
Publisher
Springer International Publishing
Published in
Granular Computing
Print ISSN: 2364-4966
Electronic ISSN: 2364-4974
DOI
https://doi.org/10.1007/s41066-022-00328-z

Premium Partner