Abstract
A new framework for the fault diagnosis of centrifugal pumps (CP) is presented in this paper. Time domain (TD) features obtained from the vibration signal (VS) of the CP are vulnerable to severe faults and can affect the fault classification accuracy of the classifier. To address this issue, the proposed method selects a healthy reference signal (HRS) and extracts raw statistical features from this signal and the vibration signals of the CP obtained under different operating conditions in the time and frequency domain (FD). The Pearson correlation coefficient is calculated by cross-correlating the time and frequency domain features of the healthy reference signal with the time and frequency domain features extracted from the vibration signal of the CP under different operating conditions. The Pearson correlation coefficient results in a new feature vector, however, some of the coefficients may not be the best to identify the ongoing conditions of the centrifugal pump. To overcome this problem, the proposed method uses a new health sensitivity ratio for the selection of CP health-sensitive features. The health sensitivity ratio (HSR) assesses per-class feature compactness and between-class distance of the features. The selected health-sensitive features are provided to KNN for the identification of centrifugal pump health conditions. The proposed method has achieved a classification accuracy of 97.13%, surpassing that of the conventional methods for CP fault diagnosis.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ahmad, Z., Rai, A., Maliuk, A.S., Kim, J.M.: Discriminant feature extraction for centrifugal pump fault diagnosis. IEEE Access 8, 165512–165528 (2020)
Prosvirin, A.E., Ahmad, Z., Kim, J.M.: Global and local feature extraction using a convolutional autoencoder and neural networks for diagnosing centrifugal pump mechanical faults. IEEE Access 9, 65838–65854 (2021)
Nguyen, T.K., Ahmad, Z., Kim, J.M.: Leak localization on cylinder tank bottom using acoustic emission. Sensors 23(1), 27 (2023)
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)
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. Nucl. Eng. Technol. 55(3), 827–838 (2023)
Dai, C., Hu, S., Zhang, Y., Chen, Z., Dong, L.: Cavitation state identification of centrifugal pump based on CEEMD-DRSN. Nucl. Eng. Technol. 55, 1507–1517 (2023)
Chen, L., Wei, L., Wang, Y., Wang, J., Li, W.: Monitoring and predictive maintenance of centrifugal pumps based on smart sensors. Sensors 22(6), 2106 (2022)
Chen, H., Li, S.: Multi-sensor fusion by CWT-PARAFAC-IPSO-SVM for intelligent mechanical fault diagnosis. Sensors 22(10), 3647 (2022)
Ahmad, S., Ahmad, Z., Kim, J.M.: A centrifugal pump fault diagnosis framework based on supervised contrastive learning. Sensors 22(17), 6448 (2022)
Ahmad, Z., Prosvirin, A.E., Kim, J., Kim, J.M.: Multistage centrifugal pump fault diagnosis by selecting fault characteristic modes of vibration and using pearson linear discriminant analysis. IEEE Access 8, 223030–223040 (2020)
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)
Kumar, A., Tang, H., Vashishtha, G., Xiang, J.: Noise subtraction and marginal enhanced square envelope spectrum (MESES) for the identification of bearing defects in centrifugal and axial pump. Mech. Syst. Signal Process. 165, 108366 (2022)
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, 125943 (2023)
Rapur, J.S., Tiwari, R.: Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analyses. Measurement (Lond) 147, 106809 (2019)
Aguilera, J.J., et al.: A review of common faults in large-scale heat pumps. Renew. Sustain. Energy Rev. 168, 112826 (2022)
Chen, K., Lu, Y., Zhang, R., Wang, H.: The adaptive bearing fault diagnosis based on optimal regulation of generalized SR behaviors in fluctuating-damping induced harmonic oscillator. Mech. Syst. Signal Process. 189, 110078 (2023)
Vrachimis, S., et al.: WaterSafe: a water network benchmark for fault diagnosis research. In: IFAC-PapersOnLine, pp. 655–660. Elsevier B.V. (2022)
Saeed, U., Jan, S.U., Lee, Y.D., Koo, I.: Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliab. Eng. Syst. Saf. 205, 107284 (2021)
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)
Sakthivel, N.R., Nair, B.B., Elangovan, M., Sugumaran, V., Saravanmurugan, S.: Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals. Eng. Sci. Technol. Int. J. 17(1), 30–38 (2014)
Jin, X., Zhao, M., Chow, T.W.S., Pecht, M.: Motor bearing fault diagnosis using trace ratio linear discriminant analysis. IEEE Trans. Ind. Electron. 61(5), 2441–2451 (2014)
Dong, L., Xiao, Q., Jia, Y., Fang, T.: Review of research on intelligent diagnosis of oil transfer pump malfunction. Petroleum. KeAi Communications Co. (2022)
Acknowledgements
This research was funded by Ministry of Trade, Industry and Energy (MOTIE) and supported by Korea Evaluation Institute of Industrial Technology (KIET). [RS-2022–00142509, The development of simulation stage and digital twin for Land Based Test Site and hydrogen powered vessel with fuel cell]. This work was also supported by the Technology Infrastructure Program funded by the Ministry of SMEs and Startups (MSS, Korea).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Ahmad, Z., Ullah, N., Zaman, W., Siddique, M.F., Kim, J., Kim, JM. (2023). A Framework for Centrifugal Pump Diagnosis Using Health Sensitivity Ratio Based Feature Selection and KNN. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14407. Springer, Cham. https://doi.org/10.1007/978-3-031-47637-2_13
Download citation
DOI: https://doi.org/10.1007/978-3-031-47637-2_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-47636-5
Online ISBN: 978-3-031-47637-2
eBook Packages: Computer ScienceComputer Science (R0)