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Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network

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Abstract

Axial piston pumps have wide applications in hydraulic systems for power transmission. Their condition monitoring and fault diagnosis are essential in ensuring the safety and reliability of the entire hydraulic system. Vibration and discharge pressure signals are two common signals used for the fault diagnosis of axial piston pumps because of their sensitivity to pump health conditions. However, most of the previous fault diagnosis methods only used vibration or pressure signal, and literatures related to multi-sensor data fusion for the pump fault diagnosis are limited. This paper presents an end-to-end multi-sensor data fusion method for the fault diagnosis of axial piston pumps. The vibration and pressure signals under different pump health conditions are fused into RGB images and then recognized by a convolutional neural network. Experiments were performed on an axial piston pump to confirm the effectiveness of the proposed method. Results show that the proposed multi-sensor data fusion method greatly improves the fault diagnosis of axial piston pumps in terms of accuracy and robustness and has better diagnostic performance than other existing diagnosis methods.

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Abbreviations

1D:

One-dimensional

2D:

Two-dimensional

CNN:

Convolutional neural network

SNR:

Signal-to-noise ratio

STFT:

Short-time Fourier transform

a hw :

Feature map element at pixel (h, w) in the pooling window

A l k :

The kth feature map at layer l

B l k :

Bias of the kth group filter at layer l

c :

Index of channels for input feature maps or the group filters

C :

Total number of filter channels

f(·):

Activation function

H :

Pooling window height

i′ :

Height index of element pixels

j:

Imaginary unit

j′ :

Width index of element pixels

J :

Loss function

k :

Index of group filters or output feature maps

l :

Index of network layers

L :

Total layer number

m, n :

Indices of discrete sampling points

N :

Size of Hanning window

p l+1 k :

Maximum element in the pooling window

q :

The qth class

Q :

Total classification number

s :

Index of samples

S :

Total number of samples

t :

Time

x(τ):

Vibration signal

x (s) :

The sth sample

X l−1 c :

The cth-channel component of the input feature map at layer (l − 1)

X l k :

The kth output feature map at layer l

y (s) :

Predicted label

w(τ), w*(τ):

Window function and its conjugated form

W :

Pooling window width

W l c,k :

The cth-channel component of the kth group filter weight at layer l

η :

Learning rate

θ L :

Trainable parameters at the last layer L

θ new, θ old :

Trainable parameters after and before update, respectively

τ :

Time variable of integration

ω :

Angular frequency

References

  1. Chao Q, Zhang J H, Xu B, Wang Q N, Lyu F, Li K. Integrated slipper retainer mechanism to eliminate slipper wear in high-speed axial piston pumps. Frontiers of Mechanical Engineering, 2022, 17(1): 1–13

    Article  Google Scholar 

  2. Chao Q, Xu Z, Tao J F, Liu C L, Zhai J. Cavitation in a high-speed aviation axial piston pump over a wide range of fluid temperatures. Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 2022, 236(4): 727–737

    Google Scholar 

  3. Maradey Lázaro J G, Borrás Pinilla C. Detection and classification of wear fault in axial piston pumps: using ANNs and pressure signals. In: Burgos D A T, Vejar M A, Pozo F, eds. Pattern Recognition Applications in Engineering. Hershey: IGI Global, 2020, 286–316

    Chapter  Google Scholar 

  4. Xia S Q, Zhang J H, Ye S G, Xu B, Huang W D, Xiang J W. A spare support vector machine based fault detection strategy on key lubricating interfaces of axial piston pumps. IEEE Access, 2019, 7: 178177–178186

    Article  Google Scholar 

  5. Lan Y, Hu J W, Huang J H, Niu L K, Zeng X H, Xiong X Y, Wu B. Fault diagnosis on slipper abrasion of axial piston pump based on extreme learning machine. Measurement, 2018, 124: 378–385

    Article  Google Scholar 

  6. Guo R, Zhao Z Q, Wang T, Liu G H, Zhao J Y, Gao D R. Degradation state recognition of piston pump based on ICEEMDAN and XGBoost. Applied Sciences, 2020, 10(18): 6593

    Article  Google Scholar 

  7. Keller N, Sciancalepore A, Vacca A. Demonstrating a condition monitoring process for axial piston pumps with damaged valve plates. International Journal of Fluid Power, 2022, 23(2): 205–236

    Google Scholar 

  8. Wang S H, Xiang J W, Zhong Y T, Tang H S. A data indicator-based deep belief networks to detect multiple faults in axial piston pumps. Mechanical Systems and Signal Processing, 2018, 112: 154–170

    Article  Google Scholar 

  9. Chao Q, Tao J F, Wei X L, Wang Y H, Meng L H, Liu C L. Cavitation intensity recognition for high-speed axial piston pumps using 1-D convolutional neural networks with multi-channel inputs of vibration signals. Alexandria Engineering Journal, 2020, 59(6): 4463–4473

    Article  Google Scholar 

  10. Chao Q, Tao J F, Wei X L, Liu C L. Identification of cavitation intensity for high-speed aviation hydraulic pumps using 2D convolutional neural networks with an input of RGB-based vibration data. Measurement Science and Technology, 2020, 31(10): 105102

    Article  Google Scholar 

  11. Wang S H, Xiang J W. A minimum entropy deconvolution-enhanced convolutional neural networks for fault diagnosis of axial piston pumps. Soft Computing, 2020, 24(4): 2983–2997

    Article  Google Scholar 

  12. Tang S N, Yuan S Q, Zhu Y, Li G P. An integrated deep learning method towards fault diagnosis of hydraulic axial piston pump. Sensors, 2020, 20(22): 6576

    Article  Google Scholar 

  13. Chao Q, Gao H H, Tao J F, Wang Y H, Zhou J, Liu C L. Adaptive decision-level fusion strategy for the fault diagnosis of axial piston pumps using multiple channels of vibration signals. Science China Technological Sciences, 2022, 65(2): 470–480

    Article  Google Scholar 

  14. Tang S N, Zhu Y, Yuan S Q. Intelligent fault diagnosis of hydraulic piston pump based on deep learning and Bayesian optimization. ISA Transactions, 2022 (in press)

  15. Tang S N, Zhu Y, Yuan S Q. A novel adaptive convolutional neural network for fault diagnosis of hydraulic piston pump with acoustic images. Advanced Engineering Informatics, 2022, 52: 101554

    Article  Google Scholar 

  16. Lu C Q, Wang S P, Makis V. Fault severity recognition of aviation piston pump based on feature extraction of EEMD paving and optimized support vector regression model. Aerospace Science and Technology, 2017, 67: 105–117

    Article  Google Scholar 

  17. Wang Y D, Zhu Y, Wang Q L, Yuan S Q, Tang S N, Zheng Z J. Effective component extraction for hydraulic pump pressure signal based on fast empirical mode decomposition and relative entropy. AIP Advances, 2020, 10(7): 075103

    Article  Google Scholar 

  18. Lu C Q, Wang S P, Zhang C. Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2016, 230(16): 2913–2928

    Google Scholar 

  19. Yu H, Li H R, Li Y L. Vibration signal fusion using improved empirical wavelet transform and variance contribution rate for weak fault detection of hydraulic pumps. ISA Transactions, 2020, 107: 385–401

    Article  Google Scholar 

  20. Yu H, Li H R, Li Y L, Li Y F. A novel improved full vector spectrum algorithm and its application in multi-sensor data fusion for hydraulic pumps. Measurement, 2019, 133: 145–161

    Article  Google Scholar 

  21. Safizadeh M S, Latifi S K. Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Information Fusion, 2014, 18: 1–8

    Article  Google Scholar 

  22. Xia M, Li T, Xu L, Liu L Z, de Silva C W. Fault diagnosis for rotating machinery using multiple sensors and convolutional neural networks. IEEE/ASME Transactions on Mechatronics, 2018, 23(1): 101–110

    Article  Google Scholar 

  23. Wang H Q, Li S, Song L Y, Cui L L. A novel convolutional neural network based fault recognition method via image fusion of multivibration-signals. Computers in Industry, 2019, 105: 182–190

    Article  Google Scholar 

  24. Gong W F, Chen H, Zhang Z H, Zhang M L, Wang R H, Guan C, Wang Q. A novel deep learning method for intelligent fault diagnosis of rotating machinery based on improved CNN-SVM and multichannel data fusion. Sensors, 2019, 19(7): 1693

    Article  Google Scholar 

  25. Wang J J, Fu P L, Zhang L B, Gao R X, Zhao R. Multilevel information fusion for induction motor fault diagnosis. IEEE/ASME Transactions on Mechatronics, 2019, 24(5): 2139–2150

    Article  Google Scholar 

  26. Chen H P, Hu N Q, Cheng Z, Zhang L, Zhang Y. A deep convolutional neural network based fusion method of two-direction vibration signal data for health state identification of planetary gearboxes. Measurement, 2019, 146: 268–278

    Article  Google Scholar 

  27. Azamfar M, Singh J, Bravo-Imaz I, Lee J. Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis. Mechanical Systems and Signal Processing, 2020, 144: 106861

    Article  Google Scholar 

  28. 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

    Article  Google Scholar 

  29. Yan X S, Sun Z, Zhao J J, Shi Z G, Zhang C A. Fault diagnosis of rotating machinery equipped with multiple sensors using spacetime fragments. Journal of Sound and Vibration, 2019, 456: 49–64

    Article  Google Scholar 

  30. Chao Q, Zhang J H, Xu B, Huang H P, Pan M. A review of high-speed electro-hydrostatic actuator pumps in aerospace applications: challenges and solutions. Journal of Mechanical Design, 2019, 141(5): 050801

    Article  Google Scholar 

  31. Ma J M, Chen J, Li J, Li Q L, Ren C Y. Wear analysis of swash plate/slipper pair of axis piston hydraulic pump. Tribology International, 2015, 90: 467–472

    Article  Google Scholar 

  32. Huang J H, Yan Z, Quan L, Lan Y, Gao Y S. Characteristics of delivery pressure in the axial piston pump with combination of variable displacement and variable speed. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2015, 229(7): 599–613

    Google Scholar 

  33. Chao Q, Tao J F, Lei J B, Wei X L, Liu C L, Wang Y H, Meng L H. Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston pump design. Frontiers of Mechanical Engineering, 2021, 16(1): 176–185

    Article  Google Scholar 

  34. Chacon R, Ivantysynova M. Virtual prototyping of axial piston machines: numerical method and experimental validation. Energies, 2019, 12(9): 1674

    Article  Google Scholar 

  35. Dahl G E, Sainath T N, Hinton G E. Improving deep neural networks for LVCSR using rectified linear units and dropout. In: Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver: IEEE, 2013, 8609–8613

    Google Scholar 

  36. Stankovic L, Daković M, Thayaparan T. Time-Frequency Signal Analysis with Applications. Boston: Artech House, 2013

    MATH  Google Scholar 

  37. LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11): 2278–2324

    Article  Google Scholar 

  38. Zhang W, Li C H, Peng G L, Chen Y H, Zhang Z J. A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mechanical Systems and Signal Processing, 2018, 100: 439–453

    Article  Google Scholar 

  39. Liu X C, Zhou Q C, Zhao J, Shen H H, Xiong X L. Fault diagnosis of rotating machinery under noisy environment conditions based on a 1-D convolutional autoencoder and 1-D convolutional neural network. Sensors, 2019, 19(4): 972

    Article  Google Scholar 

  40. Tang S N, Zhu Y, Yuan S Q, Li G P. Intelligent diagnosis towards hydraulic axial piston pump using a novel integrated CNN model. Sensors, 2020, 20(24): 7152

    Article  Google Scholar 

  41. Jiang W L, Wang C Y, Zou J Y, Zhang S Q. Application of deep learning in fault diagnosis of rotating machinery. Processes, 2021, 9(6): 919

    Article  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Key R&D Program of China (Grant No. 2018YFB1702503), the Open Foundation of the State Key Laboratory of Fluid Power and Mechatronic Systems, China (Grant No. GZKF-202108), the National Postdoctoral Program for Innovative Talents, China (Grant No. BX20200210), the China Postdoctoral Science Foundation (Grant No. 2019M660086), and Shanghai Municipal Science and Technology Major Project, China (Grant No. 2021SHZDZX0102).

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Authors and Affiliations

  1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Qun Chao, Haohan Gao, Jianfeng Tao & Chengliang Liu

  2. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China

    Qun Chao

  3. MoE Key Laboratory of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, 200240, China

    Qun Chao, Jianfeng Tao & Chengliang Liu

  4. China Electronic Product Reliability and Environmental Testing Research Institute, Guangzhou, 510610, China

    Yuanhang Wang & Jian Zhou

Authors
  1. Qun Chao
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  2. Haohan Gao
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  3. Jianfeng Tao
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  4. Chengliang Liu
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  5. Yuanhang Wang
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  6. Jian Zhou
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Correspondence to Jianfeng Tao.

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Chao, Q., Gao, H., Tao, J. et al. Fault diagnosis of axial piston pumps with multi-sensor data and convolutional neural network. Front. Mech. Eng. 17, 36 (2022). https://doi.org/10.1007/s11465-022-0692-4

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