Skip to main content

Advertisement

SpringerLink
Log in

Vibration-Based Fault Diagnosis of Broken Impeller and Mechanical Seal Failure in Industrial Mono-Block Centrifugal Pumps Using Deep Convolutional Neural Network

  • Original Paper
  • Published:
Journal of Vibration Engineering & Technologies Aims and scope Submit manuscript

Abstract

Purpose

Hydraulic pump failure results in a high rate of energy loss, performance degradation, high vibration levels, and continuous noise emission. An unexpected pump failure might result in a sudden collapse of the hydraulics, resulting in significant financial losses and the shutdown of the whole factory. Fault diagnosis plays a critical function in diagnosing flaws before they occur. Early detection is crucial for identifying problems and may save money, time, and potentially dangerous circumstances.

Methods

In recent years, many studies in intelligent fault diagnosis utilizing various machine learning approaches have been conducted. A vibration-based fault diagnosis in industrial mono-block centrifugal pumps is presented in this study. An experimental configuration for structuring databases, required for developing algorithms for running machine learning programs, is designed. Standard condition vibration signals are collected from the setup when the pump is healthy and free of defects. This study considers the two major defective conditions of broken impeller (B.I.) and seal failure (S.F.). The faults are introduced in the pump one after the other, and the vibration signals are obtained. The image processing approach converts these analog signals to 2D images.

Results

Later, the images are trained and tested using a deep convolution neural network (DCNN) classifier, and the fault accuracy is verified. The results show an accuracy of 99.07% after training and testing the image dataset.

Conclusion

The suggested DCNN architecture exhibits high and accurate fault diagnosis accuracy for the industrial mono-block centrifugal pump.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8.
Fig. 9
Fig. 10

Similar content being viewed by others

Data Availability

The datasets generated during and/or analyzed during the current study are not publicly available as the research work with few more faults are yet to be carried out in future but are available with the corresponding author on reasonable request.

References

  1. Azadeh A, Saberi M, Kazem A, Ebrahimipour V, Nourmohammadzadeh A, Saberi Z (2013) A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization. Appl Soft Comput 13(3):1478–1485. https://doi.org/10.1016/j.asoc.2012.06.020

    Article  Google Scholar 

  2. Muralidharan V, Sugumaran V, Indira V (2014) Fault diagnosis of mono-block centrifugal pump using SVM. Engineering Science and Technology, an International Journal 17(3):152–157. https://doi.org/10.1016/j.jestch.2014.04.005

    Article  Google Scholar 

  3. Muralidharan V, Sugumaran V (2013) Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugalpump. Measurement 46(1):353–359. https://doi.org/10.1016/j.measurement.2012.07.007

    Article  Google Scholar 

  4. Goyal D, Mongia C, Sehgal S (2021) Applications of digital signal processing in monitoring machining processes and rotary components: a review. IEEE Sens J 21(7):8780–8804. https://doi.org/10.1109/JSEN.2021.3050718

    Article  Google Scholar 

  5. Gao Z, Cecati C, Ding SX (2015) A survey of fault diagnosis and fault-tolerant techniques—Part I: Fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 62(6):3757–3767. https://doi.org/10.1109/TIE.2015.2417501

    Article  Google Scholar 

  6. Tian Y, Lu C, Wang ZL (2015) Approach for hydraulic pump fault diagnosis based on wpt-svd and svm. In Applied Mechanics and Materials. Trans Tech Publications Ltd 764:191–197. https://doi.org/10.4028/www.scientific.net/AMM.764-765.191

    Article  Google Scholar 

  7. Jiang W, Li Z, Zhang S, Wang T, Zhang S (2021) Hydraulic pump fault diagnosis method based on EWT decomposition denoising and deep learning on cloud platform. Shock Vib. https://doi.org/10.1155/2021/6674351

    Article  Google Scholar 

  8. Bin GF, Gao JJ, Li XJ, Dhillon BS (2012) Early fault diagnosis of rotating machinery based on wavelet packets—empirical mode decomposition feature extraction and neural network. Mech Syst Signal Process 27:696–711. https://doi.org/10.1016/j.ymssp.2011.08.002

    Article  Google Scholar 

  9. Tang S, Yuan S, Zhu Y (2019) Deep learning-based intelligent fault diagnosis methods toward rotating machinery. IEEE Access 8:9335–9346. https://doi.org/10.1109/ACCESS.2019.2963092

    Article  Google Scholar 

  10. LeCun Y, Bengio Y, Hinton G (2015) Deep learning nature 521(7553):436–444. https://doi.org/10.1038/nature14539

    Article  Google Scholar 

  11. Lu C, Wang ZY, Qin WL, Ma J (2017) Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process 130:377–388. https://doi.org/10.1016/j.sigpro.2016.07.028

    Article  Google Scholar 

  12. Ali JB, Fnaiech N, Saidi L, Chebel-Morello B, Fnaiech F (2015) Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl Acoust 89:16–27. https://doi.org/10.1016/j.apacoust.2014.08.016

    Article  Google Scholar 

  13. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507. https://doi.org/10.1126/science.1127647

    Article  MathSciNet  MATH  Google Scholar 

  14. Xie Y, Zhang T (2017) Fault diagnosis for rotating machinery based on convolutional neural network and empirical mode decomposition, 2017 Shock and Vibration. https://doi.org/10.1155/2017/3084197

  15. Kabla A, Mokrani K (2016) Bearing fault diagnosis using Hilbert-Huang transform (HHT) and support vector machine (SVM). Mech Ind 17(3):308. https://doi.org/10.1051/meca/2015067

    Article  Google Scholar 

  16. Yang B, Liu R, Chen X (2017) Fault diagnosis for a wind turbine generator bearing via sparse representation and shift-invariant K-SVD. IEEE Trans Ind Inf 13(3):1321–1331. https://doi.org/10.1109/TII.2017.2662215

    Article  Google Scholar 

  17. Feng H, Wang Y, Zhu J, Li D (2018) Multi-kernel learning based autonomous fault diagnosis for centrifugal pumps. 2018 International Conference on Control, Automation and Information Sciences (ICCAIS), 548–553. https://doi.org/10.1109/ICCAIS.2018.8570322

  18. Jiang W, Li Z, Li J, Zhu Y, Zhang P (2019) Study on a fault identification method of the hydraulic pump based on a combination of voiceprint characteristics and extreme learning machine. Processes 7(12):894. https://doi.org/10.3390/pr7120894

    Article  Google Scholar 

  19. Su X, Tomovic MM, Zhu D (2019) Diagnosis of gradual faults in high-speed gear pairs using machine learning. J Br Soc Mech Sci Eng 41(4):1–11. https://doi.org/10.1007/S40430-019-1701-3

    Article  Google Scholar 

  20. Yadav, P., Chauhan, S., Tiwari, P., Upadhyay, S.H. and Rakesh, P.K., (2020). Mode selection in variational mode decomposition and its application in fault diagnosis of rolling element bearing. Reliab Safety Hazard Assess Risk-Based Technol 663–670. https://doi.org/10.1007/978-981-13-9008-1_56

  21. Xin Y, Li S, Wang J, An Z, Zhang W (2020) Intelligent fault diagnosis method for rotating machinery based on vibration signal analysis and hybrid multi-object deep CNN. IET Sci Meas Technol 14(4):407–415. https://doi.org/10.1049/iet-smt.2018.5672

    Article  Google Scholar 

  22. Chacon JLF, Kappatos V, Balachandran W, Gan TH (2015) A novel approach for incipient defect detection in rolling bearings using acoustic emission technique. Appl Acoust 89:88–100. https://doi.org/10.1016/j.apacoust.2014.09.002

    Article  Google Scholar 

  23. Sharma A, Jigyasu R, Mathew L, Chatterji S (2018) Bearing fault diagnosis using weighted K-nearest neighbour. In: 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 1132–1137. https://doi.org/10.1109/ICOEI.2018.8553800

  24. Wang H, Yu Z, Guo L (2020) April. Real-time online fault diagnosis of rolling bearings based on KNN algorithm Journal of Physics: Conference Series 1486:032019. https://doi.org/10.1088/1742-6596/1486/3/032019

    Article  Google Scholar 

  25. Verma V, Ratol JS, Singh A (2019) Bearing fault detection using ensemble empirical mode decomposition integrated with Teager Kaiser energy method. Int J Technolol Comput 4:167–171

    Google Scholar 

  26. Wang D (2016) K-nearest neighbors based methods for identification of different gear crack levels under different motor speeds and loads: Revisited. Mech Syst Signal Process 70:201–208. https://doi.org/10.1016/j.ymssp.2015.10.007

    Article  Google Scholar 

  27. Sanchez RV, Lucero P, Macancela JC, Cerrada M, Cabrera D, Vasquez R (2019) Gear crack level classification by using KNN and time-domain features from acoustic emission signals under different motor speeds and loads. SDPC Proceedings-2018, International conference on sensing, diagnostics, prognostics, and control. https://doi.org/10.1109/SDPC.2018.8664979

  28. Gundewar SK, Kane PV (2021) Condition monitoring and fault diagnosis of induction motor. J Vib Eng Technol 9:643–674. https://doi.org/10.1007/s42417-020-00253-y

    Article  Google Scholar 

  29. H.S., R.C., Bharadwaj, S.C., Umashankar, S., Padmanaban, S., Dutta, N. and Holm-Nielsen, J.B., (2019), June. Electrical fault detection using machine learning algorithm for centrifugal water pumps. IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 1–6. https://doi.org/10.1109/EEEIC.2019.8783841

  30. Sinha GP, Kumar B (2021) Review on vibration analysis of functionally graded material structural components with cracks. J Vib Eng Technol 9(1):23–49. https://doi.org/10.1007/s42417-020-00208-3

    Article  Google Scholar 

  31. Ramteke DS, Pachori RB, Parey A (2021) Automated Gearbox Fault Diagnosis Using Entropy-Based Features in Flexible Analytic Wavelet Transform (FAWT) Domain. J Vib Eng Technol 9(7):1703–1713. https://doi.org/10.1007/s42417-021-00322-w

    Article  Google Scholar 

  32. Ramteke SM, Chelladurai H, Amarnath M (2021) Diagnosis and Classification of Diesel Engine Components Faults Using Time-Frequency and Machine Learning Approach. J Vib Eng Technol. https://doi.org/10.1007/s42417-021-00370-2

    Article  Google Scholar 

  33. Lobato TH, da Silva RR, da Costa ES, Mesquita AL (2020) An integrated approach to rotating machinery fault diagnosis using, EEMD, SVM, and augmented data. J Vib Eng Technol 8(3):403–408. https://doi.org/10.1007/s42417-019-00167-4

    Article  Google Scholar 

  34. Jena PC, Parhi DR, Pohit G (2019) Dynamic Investigation of FRP Cracked Beam Using Neural Network Technique. J Vib Eng Technol 7(6):647–661. https://doi.org/10.1007/s42417-019-00158-5

    Article  Google Scholar 

  35. Mahajan, A., & Chaudhary, S. (2019). Categorical Image Classification Based On Representational Deep Network (RESNET). 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA), 327–330. https://doi.org/10.1109/ICECA.2019.8822133

  36. Kai Zhang;Baoping Tang;Lei Deng;Xiaoli Liu; (2021). A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox. Measurement, Vol:179. https://doi.org/10.1016/j.measurement.2021.109491.

  37. Wen L, Li X, Gao L (2020) A transfer convolutional neural network for fault diagnosis based on ResNet-50. Neural Comput Appl 32:6111–6124. https://doi.org/10.1007/s00521-019-04097-w

    Article  Google Scholar 

  38. Pinedo-Sánchez LA, Mercado-Ravell DA, Carballo-Monsivais CA (2020) Vibration analysis in bearings for failure prevention using CNN. J Br Soc Mech Sci Eng 42:628. https://doi.org/10.1007/s40430-020-02711-w

  39. Lei X, Pan H, Huang X (2019) A dilated CNN model for image classification. IEEE Access 7 124087–124095. https://doi.org/10.1109/ACCESS.2019.2927169

  40. Hao Wu, Zhao J (2018) Deep convolutional neural network model based chemical process fault diagnosis. Comput Chem Eng 115:185–197. https://doi.org/10.1016/j.compchemeng.2018.04.009

    Article  Google Scholar 

  41. Malla C, Panigrahi I (2019) Review of condition monitoring of rolling element bearing using vibration analysis and other techniques. J Vib Eng Technol 7(4):407–414. https://doi.org/10.1007/s42417-019-00119-y

    Article  Google Scholar 

  42. Manikandan S, Duraivelu K (2021) Fault diagnosis of various rotating equipment using machine learning approaches–A review. Proc Inst Mech Eng Part E: J Process Mech Eng 235(2):629–642. https://doi.org/10.1177/0954408920971976

    Article  Google Scholar 

  43. Hasan MJ, Rai A, Ahmad Z, Kim J-M (2021) A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning. IEEE Access 9:58052–58066. https://doi.org/10.1109/ACCESS.2021.3072854

    Article  Google Scholar 

Download references

Funding

The authors did not receive support from any organization for the submitted work.

Author information

Authors and Affiliations

  1. SRM Institute of Science and Technology, Kattankulathur campus, Chennai, India

    S. Manikandan & K. Duraivelu

Authors
  1. S. Manikandan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Duraivelu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to S. Manikandan.

Ethics declarations

Conflict of interest

The author(s) declared no potential conflicts of interest concerning the research, authorship, and/or publication of this article.

Financial interests

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Manikandan, S., Duraivelu, K. Vibration-Based Fault Diagnosis of Broken Impeller and Mechanical Seal Failure in Industrial Mono-Block Centrifugal Pumps Using Deep Convolutional Neural Network. J. Vib. Eng. Technol. 11, 141–152 (2023). https://doi.org/10.1007/s42417-022-00566-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42417-022-00566-0

Keywords

Search

Navigation