Chinese Journal of Mechanical Engineering >

2021 , Vol. 34 >Issue 3: 37 - 37

DOI: https://doi.org/10.1186/s10033-021-00553-8

Special Issue on AI-Enabled Monitoring Diagnosis & Prognosis

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

  • Hosameldin O. A. Ahmed ,
  • Asoke K Nandi
展开
  • 1. Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK;
    2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China

收稿日期: 2020-08-29

  修回日期: 2020-12-11

  网络出版日期: 2021-12-21

基金资助

Supported by the 111 Project (B20038) of China

收起

Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks

  • Hosameldin O. A. Ahmed ,
  • Asoke K Nandi
Expand
  • 1. Department of Electronic and Electrical Engineering, Brunel University London, Uxbridge UB8 3PH, UK;
    2. School of Mechanical Engineering, Xi'an Jiaotong University, Xi'an, China

Received date: 2020-08-29

  Revised date: 2020-12-11

  Online published: 2021-12-21

Supported by

Supported by the 111 Project (B20038) of China

Fold

摘要

Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

关键词： Bearing fault diagnosis; Image representation of vibrations; Deep learning; Convolutional neural networks

本文引用格式

Hosameldin O. A. Ahmed , Asoke K Nandi . Connected Components-based Colour Image Representations of Vibrations for a Two-stage Fault Diagnosis of Roller Bearings Using Convolutional Neural Networks[J]. Chinese Journal of Mechanical Engineering, 2021 , 34(3) : 37 -37 . DOI: 10.1186/s10033-021-00553-8

Abstract

Roller bearing failure is one of the most common faults in rotating machines. Various techniques for bearing fault diagnosis based on faults feature extraction have been proposed. But feature extraction from fault signals requires expert prior information and human labour. Recently, deep learning algorithms have been applied extensively in the condition monitoring of rotating machines to learn features automatically from the input data. Given its robust performance in image recognition, the convolutional neural network (CNN) architecture has been widely used to learn automatically discriminative features from vibration images and classify health conditions. This paper proposes and evaluates a two-stage method RGBVI-CNN for roller bearings fault diagnosis. The first stage in the proposed method is to generate the RGB vibration images (RGBVIs) from the input vibration signals. To begin this process, first, the 1-D vibration signals were converted to 2-D grayscale vibration Images. Once the conversion was completed, the regions of interest (ROI) were found in the converted 2-D grayscale vibration images. Finally, to produce vibration images with more discriminative characteristics, an algorithm was applied to the 2-D grayscale vibration images to produce connected components-based RGB vibration images (RGBVIs) with sets of colours and texture features. In the second stage, with these RGBVIs a CNN-based architecture was employed to learn automatically features from the RGBVIs and to classify bearing health conditions. Two cases of fault classification of rolling element bearings are used to validate the proposed method. Experimental results of this investigation demonstrate that RGBVI-CNN can generate advantageous health condition features from bearing vibration signals and classify the health conditions under different working loads with high accuracy. Moreover, several classification models trained using RGBVI-CNN offered high performance in the testing results of the overall classification accuracy, precision, recall, and F-score.

Key words： Bearing fault diagnosis; Image representation of vibrations; Deep learning; Convolutional neural networks

参考文献

[1] Asoke K Nandi, Ahmed Hosameldin. Condition monitoring with vibration signals: Compressive sampling and learning algorithms for rotating machines. John Wiley & Sons, 2020.
[2] A C McCormick, A K Nandi. A comparison of artificial neural networks and other statistical methods for rotating machine condition classification. In IEE Colloquium on Modeling and Signal Processing for Fault Diagnosis, 1996, Digest No: 1996/260.
[3] A C McCormick, A K Nandi. Classification of the rotating machine condition using artificial neural networks. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 1997, 211(6): 439-450.
[4] L B Jack, A K Nandi. Genetic algorithms for feature selection in machine condition monitoring with vibration signals. IEE Proceedings-Vision, Image and Signal Processing, 2000, 147(3): 205-212.
[5] A Rojas, A K Nandi. Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines. Mechanical Systems and Signal Processing, 2006, 20(7): 1523-1536.
[6] V Sugumaran, K I Ramachandran. Automatic rule learning using decision tree for fuzzy classifier in fault diagnosis of roller bearing. Mechanical Systems and Signal Processing, 2007, 21(5): 2237-2247.
[7] M D Prieto, G Cirrincione, A G Espinosa, et al. Bearing fault detection by a novel condition-monitoring scheme based on statistical-time features and neural networks. IEEE Transactions on Industrial Electronics, 2013, 60(8): 3398-3407.
[8] K Christian, N Mureithi, A Lakis, et al. On the use of time synchronous averaging, independent component analysis and support vector machines for bearing fault diagnosis. Proceedings of the 1st International Conference on Industrial Risk Engineering, 2007: 610-624.
[9] A K Nandi, Ed. Blind estimation using Higher-Order Statistics. Kluwer Academic Publishers, Amsterdam, Netherlands, and Springer, 1999.
[10] L Zhang, L B Jack, A K Nandi. Fault detection using genetic programming. Mechanical Systems and Signal Processing, 2005, 19(2): 271-289.
[11] L Zhang, A K Nandi. Fault classification using genetic programming. Mechanical Systems and Signal Processing, 2007, 21(3): 1273-1284.
[12] J Yang, Y Zhang, Y Zhu. Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mechanical Systems and Signal Processing, 2007, 21(5): 2012-2024.
[13] Z Zhang, Y Wang, K Wang. Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network. Journal of Intelligent Manufacturing, 2013, 24(6): 1213-1227.
[14] H Ahmed, A K Nandi. Compressive sampling and feature ranking framework for bearing fault classification with vibration signals. IEEE Access, 2018, 6: 44731-44746.
[15] G Betta, C Liguori, A Paolillo, et al. A DSP-based FFT-analyzer for the fault diagnosis of rotating machine based on vibration analysis. IEEE Transactions on Instrumentation and Measurement, 2002, 51(6): 1316-1322.
[16] H O A Ahmed, A K Nandi. Three-stage hybrid fault diagnosis for rolling bearings with compressively sampled data and subspace learning techniques. IEEE Transactions on Industrial Electronics, 2019, 66(7): 5516-5524.
[17] H O A Ahmed, A K Nandi. Multiple measurement vector compressive sampling and fisher score feature selection for fault classification of roller bearings. 2017 22nd International Conference on Digital Signal Processing (DSP), London, 2017: 1-5.
[18] O R Seryasat, M Aliyari shoorehdeli, F Honarvar, et al. Multi-fault diagnosis of ball bearing using FFT, wavelet energy entropy mean and root mean square (RMS). IEEE International Conference on Systems, Man and Cybernetics, Istanbul, 2010: 4295-4299.
[19] A Ghods, H Lee. A frequency-based approach to detect bearing faults in induction motors using discrete wavelet transform. IEEE International Conference on Industrial Technology (ICIT), Busan, 2014: 121-125.
[20] S Patidar, P K Soni. An overview on vibration analysis techniques for the diagnosis of rolling element bearing faults. International Journal of Engineering Trends and Technology (IJETT), 2013, 4(5): 1804-1809.
[21] A C McCormick, A K Nandi. Bispectral and trispectral features for machine condition diagnosis. IEE Proceedings-Vision, Image and Signal Processing, 1999, 146(5): 229-234.
[22] J Tian, C Morillo, M H Azarian, et al. Motor bearing fault detection using spectral kurtosis-based feature extraction coupled with K-nearest neighbor distance analysis. IEEE Transactions on Industrial Electronics, 2016, 63(3): 1793-1803.
[23] M He, D He. Deep learning based approach for bearing fault diagnosis. IEEE Transactions on Industry Applications, 2017, 53(3): 3057-3065.
[24] H Tao, P Wang, Y Chen, et al. An unsupervised fault diagnosis method for rolling bearing using STFT and generative neural networks. Journal of the Franklin Institute, 2020: 7286-7307.
[25] X Yu, F Dong, E Ding, et al. Rolling bearing fault diagnosis using modified LFDA and EMD with sensitive feature selection. IEEE Access, 2017, 6: 3715-3730.
[26] P K Kankar, S C Sharma, S P Harsha. Fault diagnosis of ball bearings using continuous wavelet transform. Applied Soft Computing, 2011, 11(2): 2300-2312.
[27] R Kumar, M Singh. Outer race defect width measurement in taper roller bearing using discrete wavelet transform of vibration signal. Measurement, 2013, 46(1): 537-545.
[28] F Al-Badour, M Sunar, L Cheded. Vibration analysis of rotating machinery using time–frequency analysis and wavelet techniques. Mechanical Systems and Signal Processing, 2011, 25(6): 2083-2101.
[29] Y Lei, J Lin, Z He, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mechanical Systems and Signal Processing, 2013, 35(1-2): 108-126.
[30] A Soualhi, K Medjaher, N Zerhouni. Bearing health monitoring based on Hilbert–Huang transform, support vector machine, and regression. IEEE Transactions on Instrumentation and Measurement, 2015, 64(1): 52-62.
[31] H Li, Y Zhang. Bearing faults diagnosis based on EMD and Wigner-Ville distribution. Intelligent Control and Automation, 2006, WCICA 2006, The Sixth World Congress, IEEE, 2006, 2: 5447-5451.
[32] J Yu, J Lv. Weak fault feature extraction of rolling bearings using local mean decomposition-based multilayer hybrid denoising. IEEE Transactions on Instrumentation and Measurement, 2017, 66(12): 3148-3159.
[33] Y Zhang, R B Randall. Rolling element bearing fault diagnosis based on the combination of genetic algorithms and fast kurtogram. Mechanical Systems and Signal Processing, 2009, 23(5): 1509-1517.
[34] F Jia, Y Lei, J Lin, et al. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mechanical Systems and Signal Processing, 2016, 72: 303-315.
[35] F Zhou, Y Gao, C Wen. A novel multimode fault classification method based on deep learning. Journal of Control Science and Engineering, 2017: 1-14.
[36] H O A Ahmed, M L D Wong, A K Nandi. Intelligent condition monitoring method for bearing faults from highly compressed measurements using sparse over-complete features. Mechanical Systems and Signal Processing, 2018, 99: 459-477.
[37] C Li, W Zhang, G Peng, et al. Bearing fault diagnosis using fully-connected winner-take-all autoencoder. IEEE Access, 2018, 6: 6103-6115.
[38] Z Chen, W Li. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network. IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1693-1702.
[39] W Mao, J He, Y Li, et al. Bearing fault diagnosis with auto-encoder extreme learning machine: A comparative study. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2017, 231(8): 1560-1578.
[40] Z Chen, S Deng, X Chen, et al. Deep neural networks-based rolling bearing fault diagnosis. Microelectronics Reliability, 2017, 75: 327-333.
[41] Z Chen, X Zeng, W Li, et al. Machine fault classification using deep belief network. Instrumentation and Measurement Technology Conference Proceedings (I2MTC), IEEE International, 2016: 1-6.
[42] J Tao, Y Liu, D Yang. Bearing fault diagnosis based on deep belief network and multisensor information fusion. Shock and Vibration, 2016: 1-9.
[43] H Shao, H Jiang, X Li, et al. Rolling bearing fault detection using continuous deep belief network with locally linear embedding. Computers in Industry, 2018, 96: 27-39.
[44] J Guo, P Zheng. A method of rolling bearing fault diagnose based on double sparse dictionary and deep belief network. IEEE Access, 2020, 8: 116239-116253.
[45] C Zhang, Y Zhang, C Hu, et al. A novel intelligent fault diagnosis method based on variational mode decomposition and ensemble deep belief network. IEEE Access, 2020, 8: 36293-36312.
[46] K Lee, J K Kim, J Kim, et al. Stacked convolutional bidirectional LSTM recurrent neural network for bearing anomaly detection in rotating machinery diagnostics. 1st IEEE International Conference on Knowledge Innovation and Invention (ICKII), 2018: 98-101.
[47] H Liu, J Zhou, Y Zheng, et al. Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. ISA Transactions, 2018, 77: 167-178.
[48] B Zhang, S Zhang, W Li. Bearing performance degradation assessment using long short-term memory recurrent network. Computers in Industry, 2019, 106: 14-29.
[49] M Qiao, S Yan, X Tang, et al. Deep convolutional and LSTM recurrent neural networks for rolling bearing fault diagnosis under strong noises and variable loads. IEEE Access, 2020, 8: 66257-66269.
[50] F Wang, G Deng, L Ma, et al. Convolutional neural network based on spiral arrangement of features and its application in bearing fault diagnosis. IEEE Access, 2019: 64092-64100.
[51] O Janssens, V Slavkovikj, B Vervisch, et al. Convolutional neural network based fault detection for rotating machinery. Journal of Sound and Vibration, 2016, 377: 331-345.
[52] X Guo, L Chen, C Shen. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 2016, 93: 490-502.
[53] X Ding, Q He. Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 2017, 66(8): 1926-1935.
[54] L Wen, X Li, L Gao, et al. A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 2018, 65(7): 5990-5998.
[55] U P Chong. Signal model-based fault detection and diagnosis for induction motors using features of vibration signal in two-dimension domain. Strojniški vestnik, 2011, 57(9): 655-666.
[56] M Amar, I Gondal, C Wilson. Vibration spectrum imaging: A novel bearing fault classification approach. IEEE Transactions on Industrial Electronics, 2014, 62(1): 494-502.
[57] M Kang, J M Kim. Reliable fault diagnosis of multiple induction motor defects using a 2-D representation of Shannon wavelets. IEEE Transactions on Magnetics, 2014, 50(10): 1-13.
[58] W Li, M Qiu, Z Zhu, et al. Bearing fault diagnosis based on spectrum images of vibration signals. Measurement Science and Technology, 2016, 27(3): 035005.
[59] C Lu, Y Wang, M Ragulskis, et al. Fault diagnosis for rotating machinery: A method based on image processing. PloS One, 2016, 11(10): e0164111.
[60] D Verstraete, A Ferrada, E L Droguett, et al. Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock and Vibration, 2017: 1-17.
[61] H Oh, J H Jung, B C Jeon, et al. Scalable and unsupervised feature engineering using vibration-imaging and deep learning for rotor system diagnosis. IEEE Transactions on Industrial Electronics, 2017, 65(4): 3539-3549.
[62] W Zhang, G Peng, C Li. Bearings fault diagnosis based on convolutional neural networks with 2-D representation of vibration signals as input. MATEC Web of Conferences, EDP Sciences, 2017, 95: 13001.
[63] M J Hasan, J M Kim. Bearing fault diagnosis under variable rotational speeds using stockwell transform-based vibration imaging and transfer learning. Applied Sciences, 2018, 8(12): 2357.
[64] H Wang, S Li, L Song, et al. A novel convolutional neural network based fault recognition method via image fusion of multi-vibration-signals. Computers in Industry, 2019, 105: 182-190.
[65] X Yan, Z Sun, J Zhao, et al. Fault diagnosis of active magnetic bearing–rotor system via vibration images. Sensors, 2019, 19(2): 244.
[66] D T Hoang, H J Kang. Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cognitive Systems Research, 2019, 53: 42-50.
[67] Z Zhu, G Peng, Y Chen, et al. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis. Neurocomputing, 2019, 323: 62-75.
[68] P Ma, H Zhang, W Fan, et al. A novel bearing fault diagnosis method based on 2D image representation and transfer learning-convolutional neural network. Measurement Science and Technology, 2019, 30(5): 055402.
[69] X Zhu, D Hou, P Zhou, et al. Rotor fault diagnosis using a convolutional neural network with symmetrized dot pattern images. Measurement, 2019, 138: 526-535.
[70] Y Zhang, K Xing, R Bai, et al. An enhanced convolutional neural network for bearing fault diagnosis based on time–frequency image. Measurement, 2020: 107667.
[71] K Kaplan, Y Kaya, M Kuncan, et al. An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis. Applied Soft Computing, 2020, 87: 106019.
[72] H P Singh, A Nigam, A K Gautam, et al. Noise reduction in images using enhanced average filter. International Journal of Computer Applications, 2014: 25-28.
[73] M Piccardi. Background subtraction techniques: a review. 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No. 04CH37583), 2004, 4: 3099-3104.
[74] N Otsu. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.
[75] L Di Stefano, A Bulgarelli. A simple and efficient connected components labeling algorithm. Proceedings 10th International Conference on Image Analysis and Processing, 1999: 322-327.
[76] Y LeCun, B E Boser, J S Denker, et al. Handwritten digit recognition with a back-propagation network. Advances in Neural Information Processing Systems, 1990: 396-404.
[77] Y LeCun, L Bottou, Y Bengio, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[78] N Tajbakhsh, J Y Shin, S R. Gurudu, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning. IEEE Transactions on Medical Imaging, 2016, 35(5): 1299-1312.
[79] D Maturana, S Scherer. Voxnet: A 3D convolutional neural network for real-time object recognition. Intelligent Robots and Systems (IROS), 2015 IEEE/RSJ International Conference on, 2015: 922-928.
[80] O Abdel-Hamid, A R Mohamed, H Jiang, et al. Convolutional neural networks for speech recognition. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2014, 22(10): 1533-1545.
[81] D Simard, P Y Steinkraus, J C Platt. Best practices for convolutional neural networks. Proc. Seventh Int'l Conf. Document Analysis and Recognition, 2003: 958.
[82] P Bankhead. Analyzing fluorescence microscopy images with ImageJ. Image, 2014, 1(195): 10-1109.
[83] Mathworks. com. (2020). Find connected components in binary image —MATLAB bwconncomp. [Online]. Available: https://uk.mathworks.com/help/images/ref/bwconncomp.html.
[84] D P Kingma, J Ba. Adam: A method for stochastic optimization. arXiv preprint. arXiv: 1412.6980, 2014.
[85] M L D Wong, M Zhang, A K Nandi. Effects of compressed sensing on classification of bearing faults with entropic features. 23rd European Signal Processing Conference (EUSIPCO), Nice, 2015: 2256-2260.
[86] H O A Ahmed, M L D Wong, A K Nandi. Compressive sensing strategy for classification of bearing faults. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017: 2182-2186.
[87] M Seera, M L D Wong, A K Nandi. Classification of ball bearing faults using a hybrid intelligent model. Applied Soft Computing, 2017, 57: 427-435.
[88] M D Wong, C Liu, A K Nandi. Classification of ball bearing faults using entropic measures. Proceeding Surveillance, 2014, 7.
[89] H Guo, L B Jack, A K Nandi. Feature generation using genetic programming with application to fault classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2005, 35(1): 89-99.
[90] Case Western Reserve University Bearing Data Center (http://csegroup.case.edu/bearingdatacenter/home).
[91] L F de Almeida, J W Bizarria, F C Bizarria, et al. Condition-based monitoring system for rolling element bearing using a generic multi-layer perceptron. Journal of Vibration and Control, 2015, 21(16): 3456-3464.
Options
文章导航

/

〈 〉

版权所有 © 《Chinese Journal of Mechanical Engineering》编辑部

技术支持：北京玛格泰克科技发展有限公司