Abstract
Convolutional neural network (CNN) has achieved remarkable applications in fault diagnosis. However, the tuning aiming at obtaining the well-trained CNN model is mainly manual search. Tuning requires considerable experiences on the knowledge on CNN training and fault diagnosis, and is always time consuming and labor intensive, making the automatic hyper parameter optimization (HPO) of CNN models essential. To solve this problem, this paper proposes a novel automatic CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learning rate, batch size, and L2-regulation. First, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controlling these three hyper parameters along with the training of CNN models online. Second, a new structure of DRL is designed by combining deep deterministic policy gradient and long short-term memory, which takes the training loss of CNN models as its input and can output the adjustment on these three hyper parameters. Third, a new training method for ACNN is designed to enhance its stability. Two famous bearing datasets are selected to evaluate the performance of ACNN. It is compared with four commonly used HPO methods, namely, random search, Bayesian optimization, tree Parzen estimator, and sequential model-based algorithm configuration. ACNN is also compared with other published machine learning (ML) and deep learning (DL) methods. The results show that ACNN outperforms these HPO and ML/DL methods, validating its potential in fault diagnosis.
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.
Abbreviations
- ACNN:
-
Automatic convolutional neural network
- ANN:
-
Artificial neural network
- AVG:
-
Average prediction accuracy
- AWMS-CNN:
-
Adaptive weighted multiscale convolutional neural network
- BO:
-
Bayesian optimization
- CNN:
-
Convolutional neural network
- DAN:
-
Domain adaption network
- DDPG:
-
Deep deterministic policy gradient
- DL:
-
Deep learning
- DRL:
-
Deep reinforcement learning
- ELM:
-
Extreme learning machine
- FC:
-
Fully connected
- GS:
-
Grid search
- HCNN:
-
Hierarchical convolutional neural network
- HPO:
-
Hyper parameter optimization
- ICN:
-
CNN based on a capsule network with an inception block
- IF:
-
Inner race fault
- LSTM:
-
Long short-term memory
- ML:
-
Machine learning
- MSCNN:
-
Multiscale convolutional neural network
- NAS:
-
Neural architecture search
- OF:
-
Outer race fault
- RF:
-
Roller fault
- RL:
-
Reinforcement learning
- RS:
-
Random search
- SMAC:
-
Sequential model-based algorithm configuration
- SVM:
-
Support vector machine
- TPE:
-
Tree Parzen estimator
- VI-CNN:
-
CNN based on vibration image
- WDCNN:
-
Deep convolutional neural networks with wide first-layer kernels
- WMSCCN:
-
Wide convolution and multiscale convolution
- a :
-
Action of DDPG algorithm
- a t :
-
Current action
- b t :
-
Current batch size
- b max, b min :
-
The upper and lower boundaries for batch size
- E π :
-
Expected value under policy Π
- f :
-
Fourier frequency in short-time Fourier transform
- l t :
-
Current L2-regulation value
- l max, l min :
-
The upper and lower boundaries for L2-regulation value
- lr max, lr min :
-
The upper and lower boundaries for learning rate, respectivley
- lr t :
-
Current learning rate
- loss t :
-
Loss value of the CNN model at time step t
- L :
-
Training loss of critic network
- M :
-
Number of the sequencing training loss
- n :
-
Number of samples in the experience storage D
- p :
-
Transition probability function
- Q π(s, a):
-
Q-value function under policy Π
- r :
-
Reward of DDPG algorithm
- s :
-
State of DDPG algorithm
- s t :
-
State at time step t
- STFT :
-
Short-time Fourier transform formulation
- t :
-
Time step
- w(t):
-
Window function
- y t :
-
Actual Q-value
- α :
-
Factor to control the degree of soft updating
- γ :
-
Discount factor
- Π:
-
Policy of the agent to choose the action
- θ μ :
-
Online actor network
- θ μ′ :
-
Target actor network
- ω Q :
-
Online critic network
- ω Q′ :
-
Target critic network
References
Zhang X, Huang T, Wu B, Hu Y M, Huang S, Zhou Q, Zhang X. Multi-model ensemble deep learning method for intelligent fault diagnosis with high-dimensional samples. Frontiers of Mechanical Engineering, 2021, 16(2): 340–352
Chen X F, Wang S B, Qiao B J, Chen Q. Basic research on machinery fault diagnostics: past, present, and future trends. Frontiers of Mechanical Engineering, 2018, 13(2): 264–291
Lei Y G, Yang B, Jiang X W, Jia F, Li N P, Nandi A K. Applications of machine learning to machine fault diagnosis: a review and roadmap. Mechanical Systems and Signal Processing, 2020, 138: 106587
Nath A G, Udmale S S, Singh S K. Role of artificial intelligence in rotor fault diagnosis: a comprehensive review. Artificial Intelligence Review, 2021, 54: 2609–2668
Wang J L, Xu C Q, Dai L, Zhang J, Zhong R Y. An unequal deep learning approach for 3-D point cloud segmentation. IEEE Transactions on Industrial Informatics, 2021, 17(12): 7913–7922
Chen Z Y, Mauricio A, Li W H, Gryllias K. A deep learning method for bearing fault diagnosis based on cyclic spectral coherence and convolutional neural networks. Mechanical Systems and Signal Processing, 2020, 140: 106683
Wolpert D H, Macready W G. No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1997, 1(1): 67–82
Wolpert D H. The supervised learning no-free-lunch theorems. In: Roy R, Köppen M, Ovaska S, Furuhashi T, Hoffmann F, eds. Soft Computing and Industry. London: Springer, 2002, 25–42
Hutter F, Kotthoff L, Vanschoren J. Automated Machine Learning: Methods, Systems, Challenges. Cham: Springer, 2019
Wen L, Li X Y, Gao L. A new reinforcement learning based learning rate scheduler for convolutional neural network in fault classification. IEEE Transactions on Industrial Electronics, 2021, 68(12): 12890–12900
Wen L, Ye X C, Gao L. A new automatic machine learning based hyperparameter optimization for workpiece quality prediction. Measurement and Control, 2020, 53(7–8): 1088–1098
Feurer M, Eggensperger K, Falkner S, Lindauer M, Hutter F. Practical automated machine learning for the AutoML challenge 2018. In: Proceedings of International Workshop on Automatic Machine Learning at ICML. 2018, 1189–1232
He F X, Liu T L, Tao D C. Control batch size and learning rate to generalize well: theoretical and empirical evidence. In: Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS). Vancouver, 2019, 1143–1152
Li Y Z, Wei C, Ma T Y. Towards explaining the regularization effect of initial large learning rate in training neural networks. In: Proceedings of the 33rd International Conference on Neural Information Processing Systems. Vancouver, 2019, 11674–11685
Zhou P, Peng Z K, Chen S Q, Yang Y, Zhang W M. Non-stationary signal analysis based on general parameterized time-frequency transform and its application in the feature extraction of a rotary machine. Frontiers of Mechanical Engineering, 2018, 13(2): 292–300
Wang J L, Xu C Q, Yang Z L, Zhang J, Li X O. Deformable convolutional networks for efficient mixed-type wafer defect pattern recognition. IEEE Transactions on Semiconductor Manufacturing, 2020, 33(4): 587–596
Xu G W, Liu M, Jiang Z F, Shen W M, Huang C X. Online fault diagnosis method based on transfer convolutional neural networks. IEEE Transactions on Instrumentation and Measurement, 2020, 69(2): 509–520
Li Z X, Zheng T S, Wang Y, Cao Z, Guo Z Q, Fu H Y. A novel method for imbalanced fault diagnosis of rotating machinery based on generative adversarial networks. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3500417
Chen J B, Huang R Y, Zhao K, Wang W, Liu L C, Li W H. Multiscale convolutional neural network with feature alignment for bearing fault diagnosis. IEEE Transactions on Instrumentation and Measurement, 2021, 70: 3517010
Jiao J Y, Zhao M, Lin J, Liang K X. A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 2020, 417: 36–63
Yao Y, Zhang S, Yang S X, Gui G. Learning attention representation with a multi-scale CNN for gear fault diagnosis under different working conditions. Sensors, 2020, 20(4): 1233
Li S, Wang H Q, Song L Y, Wang P X, Cui L L, Lin T J. An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network. Measurement, 2020, 165: 108122
Kolar D, Lisjak D, Pająk M, Pavković D. Fault diagnosis of rotary machines using deep convolutional neural network with wide three axis vibration signal input. Sensors, 2020, 20(14): 4017
Wang R X, Jiang H K, Li X Q, Liu S W. A reinforcement neural architecture search method for rolling bearing fault diagnosis. Measurement, 2020, 154: 107417
Zhang K Y, Chen J L, He S L, Xu E Y, Li F D, Zhou Z T. Differentiable neural architecture search augmented with pruning and multi-objective optimization for time-efficient intelligent fault diagnosis of machinery. Mechanical Systems and Signal Processing, 2021, 158: 107773
Cabrera D, Guamán A, Zhang S H, Cerrada M, Sánchez R V, Cevallos J, Long J Y, Li C. Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor. Neurocomputing, 2020, 380: 51–66
Li L, Jamieson K, DeSalvo G, Rostamizadeh A, Talwalkar A. Hyperband: a novel bandit-based approach to hyperparameter optimization. The Journal of Machine Learning Research, 2018, 18(1): 6765–6816
Li H, Zhang Q, Qin X R, Sun Y T. Raw vibration signal pattern recognition with automatic hyper-parameter-optimized convolutional neural network for bearing fault diagnosis. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2020, 234(1): 343–360
Long J Y, Zhang S H, Li C. Evolving deep echo state networks for intelligent fault diagnosis. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4928–4937
Han J H, Choi D J, Park S U, Hong S K. Hyperparameter optimization using a genetic algorithm considering verification time in a convolutional neural network. Journal of Electrical Engineering & Technology, 2020, 15(2): 721–726
Wei J A, Huang H S, Yao L G, Hu Y, Fan Q S, Huang D. New imbalanced fault diagnosis framework based on cluster-MWMOTE and MFO-optimized LS-SVM using limited and complex bearing data. Engineering Applications of Artificial Intelligence, 2020, 96: 103966
Hansen S. Using deep Q-learning to control optimization hyperparameters. 2016, arXiv:1602.04062
Zhang Z Z, Chen J L, Chen Z B, Li W P. Asynchronous episodic deep deterministic policy gradient: toward continuous control in computationally complex environments. IEEE Transactions on Cybernetics, 2021, 51(2): 604–613
Zhu Z Y, Peng G L, Chen Y H, Gao H J. A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis. Neurocomputing, 2019, 323: 62–75
Wang Y, Ning D J, Feng S L. A novel capsule network based on wide convolution and multi-scale convolution for fault diagnosis. Applied Sciences, 2020, 10(10): 3659
Wen L, Li X Y, Gao L. A new two-level hierarchical diagnosis network based on convolutional neural network. IEEE Transactions on Instrumentation and Measurement, 2020, 69(2): 330–338
Zhang W, Peng G L, Li C H, Chen Y H, Zhang Z J. A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors, 2017, 17(2): 425
Hoang D T, Kang H J. Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cognitive Systems Research, 2019, 53: 42–50
Jiang G Q, He H B, Yan J, Xie P. Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Transactions on Industrial Electronics, 2019, 66(4): 3196–3207
Qiao H H, Wang T Y, Wang P, Zhang L, Xu M D. An adaptive weighted multiscale convolutional neural network for rotating machinery fault diagnosis under variable operating conditions. IEEE Access: Practical Innovations, Open Solutions, 2019, 7: 118954–118964
Song Y, Li Y B, Jia L, Qiu M K. Retraining strategy-based domain adaption network for intelligent fault diagnosis. IEEE Transactions on Industrial Informatics, 2020, 16(9): 6163–6171
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Grant Nos. 51805192 and U21B2029), the Major Special Science and Technology Project of Hubei Province, China (Grant No. 2020A EA009), and the State Key Laboratory of Digital Manufacturing Equipment and Technology of Huazhong University of Science and Technology, China (Grant No. DMETKF2020029).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Wen, L., Wang, Y. & Li, X. A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis. Front. Mech. Eng. 17, 17 (2022). https://doi.org/10.1007/s11465-022-0673-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11465-022-0673-7