Abstract
Recently, with the urgent demand for data-driven approaches in practical industrial scenarios, the deep learning diagnosis model in noise environments has attracted increasing attention. However, the existing research has two limitations: (1) the complex and changeable environmental noise, which cannot ensure the high-performance diagnosis of the model in different noise domains and (2) the possibility of multiple faults occurring simultaneously, which brings challenges to the model diagnosis. This paper presents a novel anti-noise multi-scale convolutional neural network (AM-CNN) for solving the issue of compound fault diagnosis under different intensity noises. First, we propose a residual pre-processing block according to the principle of noise superposition to process the input information and present the residual loss to construct a new loss function. Additionally, considering the strong coupling of input information, we design a multi-scale convolution block to realize multi-scale feature extraction for enhancing the proposed model’s robustness and effectiveness. Finally, a multi-label classifier is utilized to simultaneously distinguish multiple bearing faults. The proposed AM-CNN is verified under our collected compound fault dataset. On average, AM-CNN improves 39.93% accuracy and 25.84% Fl-macro under the no-noise working condition and 45.67% accuracy and 27.72% Fl-macro under different intensity noise working conditions compared with the existing methods. Furthermore, the experimental results show that AM-CNN can achieve good cross-domain performance with 100% accuracy and 100% F1-macro. Thus, AM-CNN has the potential to be an accurate and stable fault diagnosis tool.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Di Z Y, Shao H D, Xiang J W. Ensemble deep transfer learning driven by multisensor signals for the fault diagnosis of bevel-gear cross-operation conditions. Sci China Tech Sci, 2021, 64: 481–492
Huang H R, Li K, Su W S, et al. An improved empirical wavelet transform method for rolling bearing fault diagnosis. Sci China Tech Sci, 2020, 63: 2231–2240
Xu Y G, Wang L, Hu A J, et al. Time-extracting S-transform algorithm and its application in rolling bearing fault diagnosis. Sci China Tech Sci, 2022, 65: 932–942
Wang J, Li S, An Z, et al. Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines. Neurocomputing, 2019, 329: 53–65
Cui L, Wu N, Ma C, et al. Quantitative fault analysis of roller bearings based on a novel matching pursuit method with a new step-impulse dictionary. Mech Syst Signal Process, 2016, 68–69: 34–43
Chen J, Zi Y, He Z, et al. Compound faults detection of rotating machinery using improved adaptive redundant lifting multiwavelet. Mech Syst Signal Process, 2013, 38: 36–54
Lyu X, Hu Z, Zhou H, et al. Application of improved MCKD method based on QGA in planetary gear compound fault diagnosis. Measurement, 2019, 139: 236–248
Peng Z K, Tse P W, Chu F L. A comparison study of improved Hilbert-Huang transform and wavelet transform: Application to fault diagnosis for rolling bearing. Mech Syst Signal Process, 2005, 19: 974–988
Guo J, Zhen D, Li H, et al. Fault feature extraction for rolling element bearing diagnosis based on a multi-stage noise reduction method. Measurement, 2019, 139: 226–235
Lei Y, Lin J, He Z, et al. A review on empirical mode decomposition in fault diagnosis of rotating machinery. Mech Syst Signal Process, 2013, 35: 108–126
Li C, Tao Y, Ao W, et al. Improving forecasting accuracy of daily enterprise electricity consumption using a random forest based on ensemble empirical mode decomposition. Energy, 2018, 165: 1220–1227
Yu X, Dong F, Ding E, et al. Rolling bearing fault diagnosis using modified LFDA and EMD With sensitive feature selection. IEEE Access, 2018, 6: 3715–3730
Huang D, Ke L, Mi B, et al. A new incipient fault diagnosis method combining improved RLS and LMD algorithm for rolling bearings with strong background noise. IEEE Access, 2018, 6: 26001–26010
Yan R, Gao R X, Chen X. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Process, 2014, 96: 1–15
Tao J, Qin C, Liu C. A synchroextracting-based method for early chatter identification of robotic drilling process. Int J Adv Manuf Technol, 2019, 100: 273–285
Tao J, Qin C, Li W, et al. Intelligent fault diagnosis of diesel engines via extreme gradient boosting and high-accuracy time-frequency information of vibration signals. Sensors, 2019, 19: 3280
Yang B, Liu R, Chen X. Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD. IEEE Trans Ind Inf, 2017, 13: 1321–1331
Liu R, Yang B, Zhang X, et al. Time-frequency atoms-driven support vector machine method for bearings incipient fault diagnosis. Mech Syst Signal Process, 2016, 75: 345–370
Li W, Zhang S, Rakheja S. Feature denoising and nearest-farthest distance preserving projection for machine fault diagnosis. IEEE Trans Ind Inf, 2016, 12: 393–404
Guo L, Gao H, Huang H, et al. Multifeatures fusion and nonlinear dimension reduction for intelligent bearing condition monitoring. Shock Vib, 2016, 2016: 1–10
Shao H, Jiang H, Zhao H, et al. A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process, 2017, 95: 187–204
Chen Z Q, Li C, Sanchez R V. Gearbox fault identification and classification with convolutional neural networks. Shock Vib, 2015, 2015: 1–10
Huang W, Cheng J, Yang Y, et al. An improved deep convolutional neural network with multi-scale information for bearing fault diagnosis. Neurocomputing, 2019, 359: 77–92
Guo X, Chen L, Shen C. Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement, 2016, 93: 490–502
Cheng Y, Lin M, Wu J, et al. Intelligent fault diagnosis of rotating machinery based on continuous wavelet transform-local binary convolutional neural network. Knowledge-Based Syst, 2021, 216: 106796
Jin Y, Qin C, Huang Y, et al. Actual bearing compound fault diagnosis based on active learning and decoupling attentional residual network. Measurement, 2021, 173: 108500
Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybernetics, 1980, 36: 193–202
Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition. Proc IEEE, 1998, 86: 2278–2324
Qin C, Jin Y, Tao J, et al. DTCNNMI: A deep twin convolutional neural networks with multi-domain inputs for strongly noisy diesel engine misfire detection. Measurement, 2021, 180: 109548
Jin Y, Qin C, Tao J, et al. An accurate and adaptative cutterhead torque prediction method for shield tunneling machines via adaptative residual long-short term memory network. Mech Syst Signal Process, 2022, 165: 108312
Qin C, Shi G, Tao J, et al. An adaptive hierarchical decomposition-based method for multi-step cutterhead torque forecast of shield machine. Mech Syst Signal Process, 2022, 175: 109148
Gan J, Wang W, Lu K. A new perspective: Recognizing online handwritten Chinese characters via 1-dimensional CNN. Inf Sci, 2019, 478: 375–390
Jin Y, Li Z, Qin C, et al. A novel interpretable method based on attentional deep neural network for actual ECG quality assessment. Biomed Signal Process Control, 2023, 79: 104064
Zhang J, Tian J, Cao Y, et al. Deep time-frequency representation and progressive decision fusion for ECG classification. Knowledge-Based Syst, 2020, 190: 105402
He Z, Shao H, Zhong X, et al. Ensemble transfer CNNs driven by multi-channel signals for fault diagnosis of rotating machinery cross working conditions. Knowledge-Based Syst, 2020, 207: 106396
Wang S, Xiang J, Zhong Y, et al. Convolutional neural network-based hidden Markov models for rolling element bearing fault identification. Knowledge-Based Syst, 2018, 144: 65–76
Zhang K, Zuo W, Chen Y, et al. Beyond a gaussian denoiser: Residual learning of deep CNN for image denoising. IEEE Trans Image Process, 2017, 26: 3142–3155
Zeiler M D, Fergus R. Visualizing and understanding convolutional networks. In: Proceedings of the 13th European Conference on Computer Vision. Zurich, 2014. 818–833
Kingma D P, Ba J. Adam: A method for stochastic optimization. ar-Xiv: 1412.6980
Zou F, Zhang H, Sang S, et al. An anti-noise one-dimension convolutional neural network learning model applying on bearing fault diagnosis. Measurement, 2021, 186: 110236
Jin G, Zhu T, Akram M W, et al. An adaptive anti-noise neural network for bearing fault diagnosis under noise and varying load conditions. IEEE Access, 2020, 8: 74793–74807
Yuan Y, Ma G, Cheng C, et al. A general end-to-end diagnosis framework for manufacturing systems. Natl Sci Rev, 2020, 7: 418–429
Chen X, Zhang B, Gao D. Bearing fault diagnosis base on multi-scale CNN and LSTM model. J Intell Manuf, 2021, 32: 971–987
Dempster A, Petitjean F, Webb G I. ROCKET: Exceptionally fast and accurate time series classification using random convolutional kernels. Data Min Knowl Disc, 2020, 34: 1454–1495
Ma G, Zhang Y, Cheng C, et al. Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network. Appl Energy, 2019, 253: 113626
Qin C, Xiao D, Tao J, et al. Concentrated velocity synchronous linear chirplet transform with application to robotic drilling chatter monitoring. Measurement, 2022, 194: 111090
Shi G, Qin C, Tao J, et al. A VMD-EWT-LSTM-based multi-step prediction approach for shield tunneling machine cutterhead torque. Knowledge-Based Syst, 2021, 228: 107213
Jin Y, Qin C, Huang Y, et al. Multi-domain modeling of atrial fibrillation detection with twin attentional convolutional long short-term memory neural networks. Knowledge-Based Syst, 2020, 193: 105460
Jin Y, Qin C, Liu J, et al. A novel domain adaptive residual network for automatic atrial fibrillation detection. Knowledge-Based Syst, 2020, 203: 106122
Jin Y, Qin C, Liu J, et al. A novel incremental and interactive method for actual heartbeat classification with limited additional labeled samples. IEEE Trans Instrum Meas, 2021, 70: 1–12
Jin Y, Liu J, Liu Y, et al. A novel interpretable method based on dual-level attentional deep neural network for actual multilabel arrhythmia detection. IEEE Trans Instrum Meas, 2022, 71: 1–11
Jin Y, Li Z, Liu Y, et al. Multi-class 12-lead ECG automatic diagnosis based on a novel subdomain adaptive deep network. Sci China Tech Sci, 2022, doi: https://doi.org/10.1007/s11431-022-2080-6
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the National Key R&D Program of China (Grant No. 2020YFB1709604), the State Key Laboratory of Mechanical System and Vibration (Grant No. MSVZD202103), and the Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX0102).
Rights and permissions
About this article
Cite this article
Jin, Y., Qin, C., Zhang, Z. et al. A multi-scale convolutional neural network for bearing compound fault diagnosis under various noise conditions. Sci. China Technol. Sci. 65, 2551–2563 (2022). https://doi.org/10.1007/s11431-022-2109-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11431-022-2109-4