Abstract
This paper proposes a fault diagnosis method for centrifugal pumps (CP) based on multi-filter processed scalograms (MFS) and convolutional neural networks (CNN). Deep learning (DL) based autonomous Health-sensitive features extraction from continuous wavelet transform (CWT) scalograms are popular adoption for the health diagnosis of centrifugal pumps. However, vibration signals (VS) acquired from the centrifugal pump consist of fault-related impulses and unwanted macrostructural noise which can affect the autonomous Health-sensitive features extraction capabilities of the deep learning models. To overcome this concern, novel multi-filter processed scalograms are introduced. The new multi-filter processed scalograms enhance the fault-related color intensity variations and remove the unwanted noise from the scalograms using Gaussian and Laplacian image filters. The proposed techniques identified the ongoing health condition of the centrifugal pump by extracting fault-related information from the multi-filter processed scalograms and classifying them into their respective classes using convolutional neural networks. The proposed method resulted in higher classification accuracy as compared to the existing method when it was applied to a real-world centrifugal pump vibration signals dataset.
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References
Ahmad, Z., Rai, A., Hasan, M.J., Kim, C.H., Kim, J.M.: A novel framework for centrifugal pump fault diagnosis by selecting fault characteristic coefficients of walsh transform and cosine linear discriminant analysis. IEEE Access 9, 150128–150141 (2021). https://doi.org/10.1109/ACCESS.2021.3124903
Ahmad, Z., Rai, A., Maliuk, A.S., Kim, J.M.: Discriminant feature extraction for centrifugal pump fault diagnosis. IEEE Access 8, 165512–165528 (2020). https://doi.org/10.1109/ACCESS.2020.3022770
Zhang, X., Zhao, B., Lin, Y.: Machine learning based bearing fault diagnosis using the case western reserve university data: a review. IEEE Access 9. Institute of Electrical and Electronics Engineers Inc., pp. 155598–155608, (2021). https://doi.org/10.1109/ACCESS.2021.3128669
Ahmad, S., Ahmad, Z., Kim, J.M.: A centrifugal pump fault diagnosis framework based on supervised contrastive learning. Sensors 22(17) (2022). https://doi.org/10.3390/s22176448
Chen, L., Wei, L., Wang, Y., Wang, J., Li, W.: Monitoring and predictive maintenance of centrifugal pumps based on smart sensors. Sensors 22(6) (2022) https://doi.org/10.3390/s22062106
Dong, L., Chen, Z., Hua, R., Hu, S., Fan, C., Xiao, X.: Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM. Nuclear Eng. Technol. 55(3), 827–838 (2023). https://doi.org/10.1016/j.net.2022.10.045
Ahmad, Z., Nguyen, T.K., Ahmad, S., Nguyen, C.D.,Kim, J.M.: Multistage centrifugal pump fault diagnosis using informative ratio principal component analysis. Sensors 22(1), (2022). https://doi.org/10.3390/s22010179
Rapuano, S., Harris, F.J.: IEEE instrumentation & measurement magazine an introduction to FFT and time domain windows part 11 in a series of tutorials in instrumentation and measurement (2007)
Hou, Y., Wu, P., Wu, D.: An operating condition information-guided iterative variational mode decomposition method based on Mahalanobis distance criterion for surge characteristic frequency extraction of the centrifugal compressor. Mech Syst Signal Process 186 (2023) https://doi.org/10.1016/j.ymssp.2022.109836
Dai, C., Hu, S., Zhang, Y., Chen, Z., Dong, L.: Cavitation state identification of centrifugal pump based on CEEMD-DRSN. Nucl. Eng. Technol. (2023). https://doi.org/10.1016/j.net.2023.01.009
Nguyen, T.K., Ahmad, Z., Kim, J.M.: A deep-learning-based health indicator constructor using kullback–leibler divergence for predicting the remaining useful life of concrete structures. Sensors 22(10) (2022) https://doi.org/10.3390/s22103687
Aguilera, J.J. et al.: A review of common faults in large-scale heat pumps. Renew. Sustain. Energ. Rev. 168. Elsevier Ltd, Oct. 01, 2022. https://doi.org/10.1016/j.rser.2022.112826
Yang, Y., Zheng, H., Li, Y., Xu, M., Chen, Y.: A fault diagnosis scheme for rotating machinery using hierarchical symbolic analysis and convolutional neural network. ISA Trans. 91, 235–252 (2019). https://doi.org/10.1016/j.isatra.2019.01.018
Gupta, S.B.: A hybrid image denoising method based on discrete wavelet transformation with pre-gaussian filtering. Indian J. Sci. Technol. 15(43), 2317–2324 (2022). https://doi.org/10.17485/IJST/v15i43.1570
Ullah, N., Ahmed, Z., Kim, J.M.: Pipeline leakage detection using acoustic emission and machine learning algorithms. Sensors 23(6) 2023. https://doi.org/10.3390/s23063226
Yafouz, A., Ahmed, A.N., Zaini, N., Sherif, M., Sefelnasr, A., El-Shafie, A.: Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms. Eng. Appl. Comput. Fluid Mech. 15(1), 902–933 (2021). https://doi.org/10.1080/19942060.2021.1926328
Gu, J., Peng, Y., Lu, H., Chang, X., Chen, G.: A novel fault diagnosis method of rotating machinery via VMD, CWT and improved CNN. Measurement (Lond) 200 (2022). https://doi.org/10.1016/j.measurement.2022.111635
Hasan, M.J., Rai, A., Ahmad, Z., Kim, J.M.: A fault diagnosis framework for centrifugal pumps by scalogram-based imaging and deep learning. IEEE Access 9, 58052–58066 (2021). https://doi.org/10.1109/ACCESS.2021.3072854
Ahmad, S., Ahmad, Z., Kim, C.H., Kim, J.M.: A method for pipeline leak detection based on acoustic imaging and deep learning. Sensors 22(4) (2022). https://doi.org/10.3390/s22041562
Nguyen, T.K., Ahmad, Z., Kim, J.M.: Leak localization on cylinder tank bottom using acoustic emission. Sensors 23(1) (2023). https://doi.org/10.3390/s23010027
Saeed, U., Lee, Y.D., Jan, S.U., Koo, I.: CAFD: Context-aware fault diagnostic scheme towards sensor faults utilizing machine learning. Sensors (Switzerland) 21(2), 1–15 (2021). https://doi.org/10.3390/s21020617
Li, G., Chen, L., Liu, J., Fang, X.: Comparative study on deep transfer learning strategies for cross-system and cross-operation-condition building energy systems fault diagnosis. Energy 263 (2023). https://doi.org/10.1016/j.energy.2022.125943
Siddique, M.F., Ahmad, Z., Kim, J.M.: Pipeline leak diagnosis based on leak-augmented scalograms and deep learning. Eng. Appl. Comput. Fluid Mech. 17(1) 2023. https://doi.org/10.1080/19942060.2023.2225577
Acknowledgements
This work was supported by the National IT Industry Promotion Agency (NIPA), grant funded by the Korean government Ministry of Science and ICT (MSIT), Grant No. S0721–23-1011, for development of a smart mixed reality technology for improving the pipe installation and inspection processes in the offshore structure fabrication. This work was also supported by the Technology Infrastructure Program funded by the Ministry of SMEs and Startups(MSS, Korea).
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Ahmad, Z., Siddique, M.F., Ullah, N., Kim, J., Kim, JM. (2024). Centrifugal Pump Health Condition Identification Based on Novel Multi-filter Processed Scalograms and CNN. In: Choi, B.J., Singh, D., Tiwary, U.S., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2023. Lecture Notes in Computer Science, vol 14532. Springer, Cham. https://doi.org/10.1007/978-3-031-53830-8_16
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