Fault Diagnosis of Centrifugal Pumps under Hydraulic Excitation and Flow Noise: A Critical Review of Vibration, Motor-Current, Acoustic-Emission, and Data-Driven Methods
محتوى المقالة الرئيسي
الملخص
Centrifugal-pump diagnosis is complicated by flow-induced excitation from pressure pulsation, recirculation, rotating stall, cavitation, and turbulence, which can produce periodic, modulated, impulsive, and broadband components that overlap the signatures associated with bearing, shaft, impeller, and seal faults. This review covers 103 traceable publications on vibration, motor-current, audible-sound, acoustic-emission, signal-processing, and data-driven methods. The included evidence is classified as direct centrifugal-pump evidence, transferable rotating-machinery evidence, or contextual review evidence. It shows that signal processing is not equally robust across all pump configurations and operating conditions, and that the frequency position alone is insufficient to support a diagnostic opinion. Harmonics, sidebands, envelope content, time-domain waveform behavior, speed, and flow context each provide important information. Methods such as envelope analysis, spectral kurtosis, mode decomposition, modulation signal bispectrum, motor current analysis, acoustic emission, and deep learning may provide meaningful results with varying reliability depending on the fault-transmission path, sensor location, operational range, and validation scheme. Higher-order spectral methods and data-driven approaches show promise but have not been iterated widely among pumps or different operating conditions. An example workflow is to perform healthy baselines covering the intended envelope, and diagnostic reports with uncertainty boundaries and evidence boundaries. Limitations include non-exhaustive literature discovery, heterogeneity in test rigs and labels, restricted access to some primary data, and reliance on transferable evidence where pump-specific validation is unavailable. The results therefore support condition-aware, multi-indicator diagnosis rather than universal thresholds or a single preferred algorithm.
##plugins.themes.bootstrap3.displayStats.downloads##
تفاصيل المقالة
القسم
كيفية الاقتباس
المراجع
Adaika, H., Tir, Z., Sahraoui, M., and Laadjal, K., 2025. PumpSpectra: An MCSA-based platform for fault detection in centrifugal pump systems. Sensors, 25(22), P. 6916. https://doi.org/10.3390/s25226916.
Ahmad, Z., Rai, A., Maliuk, A.S., and Kim, J.M., 2020. Discriminant feature extraction for centrifugal pump fault diagnosis. IEEE Access, 8, pp. 165512-165528. https://doi.org/10.1109/access.2020.3022770.
Al-Obaidi, A.R., 2019. Investigation of effect of pump rotational speed on performance and detection of cavitation within a centrifugal pump using vibration analysis. Heliyon, 5(6), P. e01910. https://doi.org/10.1016/j.heliyon.2019.e01910.
Al-Obaidi, A.R., 2020a. Detection of cavitation phenomenon within a centrifugal pump based on vibration analysis technique in both time and frequency domains. Experimental Techniques, 44(3), pp. 329-347. https://doi.org/10.1007/s40799-020-00362-z.
Al-Obaidi, A.R., 2020b. Experimental comparative investigations to evaluate cavitation conditions within a centrifugal pump based on vibration and acoustic analyses techniques. Archives of Acoustics, 45(3), pp. 541-556. https://doi.org/10.24425/aoa.2020.134070.
Albraik, A., Althobiani, F., Gu, F., and Ball, A., 2012. Diagnosis of centrifugal pump faults using vibration methods. Journal of Physics: Conference Series, 364, P. 012139. https://doi.org/10.1088/1742-6596/364/1/012139.
Alfayez, L., Mba, D., and Dyson, G., 2005. The application of acoustic emission for detecting incipient cavitation and the best efficiency point of a 60kW centrifugal pump: case study. NDT & E International, 38(5), pp. 354-358. https://doi.org/10.1016/j.ndteint.2004.10.002.
Alsalaet, J.K., Hajnayeb, A., and Bahedh, A.S., 2023. Bearing fault diagnosis using normalized diagnostic feature-gram and convolutional neural network. Measurement Science and Technology, 34(4), P. 045901. https://doi.org/10.1088/1361-6501/acad1f.
Antoni, J., 2006. The spectral kurtosis: a useful tool for characterising non-stationary signals. Mechanical Systems and Signal Processing, 20(2), pp. 282-307. https://doi.org/10.1016/j.ymssp.2004.09.001.
Antoni, J., 2007. Fast computation of the kurtogram for the detection of transient faults. Mechanical Systems and Signal Processing, 21(1), pp. 108-124. https://doi.org/10.1016/j.ymssp.2005.12.002.
Araste, Z., Sadighi, A., and Jamimoghaddam, M., 2022. Fault diagnosis of a centrifugal pump using electrical signature analysis and support vector machine. Journal of Vibration Engineering & Technologies, 11(5), pp. 2057-2067. https://doi.org/10.1007/s42417-022-00687-6.
Bagri, I., Tahiry, K., Hraiba, A., Touil, A., and Mousrij, A., 2024. Vibration signal analysis for intelligent rotating machinery diagnosis and prognosis: A comprehensive systematic literature review. Vibration, 7(4), pp. 1013-1062. https://doi.org/10.3390/vibration7040054.
Bai, L., Zhou, L., Jiang, X., Pang, Q., and Ye, D., 2019. Vibration in a multistage centrifugal pump under varied conditions. Shock and Vibration, 2019(1), P. 2057031. https://doi.org/10.1155/2019/2057031.
Barrio, R., Parrondo, J., and Blanco, E., 2010. Numerical analysis of the unsteady flow in the near-tongue region in a volute-type centrifugal pump for different operating points. Computers & Fluids, 39(5), pp. 859-870. https://doi.org/10.1016/j.compfluid.2010.01.001.
Barszcz, T., and Jabłoński, A., 2011. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mechanical Systems and Signal Processing, 25(1), pp. 431-451. https://doi.org/10.1016/j.ymssp.2010.05.018.
Borghesani, P., Pennacchi, P., Randall, R.B., Sawalhi, N., and Ricci, R., 2013. Application of cepstrum pre-whitening for the diagnosis of bearing faults under variable speed conditions. Mechanical Systems and Signal Processing, 36(2), pp. 370-384. https://doi.org/10.1016/j.ymssp.2012.11.001.
Bruinsma, S., Geertsma, R.D., Loendersloot, R., and Tinga, T., 2024. Motor current and vibration monitoring dataset for various faults in an E-motor-driven centrifugal pump. Data in Brief, 52, P. 109987. https://doi.org/10.1016/j.dib.2023.109987.
Chen, Z., Gryllias, K., and Li, W., 2020. Intelligent fault diagnosis for rotary machinery using transferable convolutional neural network. IEEE Transactions on Industrial Informatics, 16(1), pp. 339-349. https://doi.org/10.1109/tii.2019.2917233.
Chu, S., Dong, R., and Katz, J., 1995. Relationship between unsteady flow, pressure fluctuations, and noise in a centrifugal pump—part b: Effects of blade-tongue interactions. Journal of Fluids Engineering, 117(1), pp. 30-35. https://doi.org/10.1115/1.2816814.
Čudina, M., 2003. Detection of cavitation phenomenon in a centrifugal pump using audible sound. Mechanical Systems and Signal Processing, 17(6), pp. 1335-1347. https://doi.org/10.1006/mssp.2002.1514.
Cui, B., Zhang, Y., and Huang, Y., 2020. Analysis of the pressure pulsation and vibration in a low-specific-speed centrifugal pump. Journal of Fluids Engineering, 143(2), P. 021201. https://doi.org/10.1115/1.4048691.
Daraz, A., Alabied, S., Gu, F., and Ball, A.D., 2019. Modulation signal bispectrum analysis of acoustic signals for the impeller wear detection of centrifugal pumps. 2019 25th International Conference on Automation and Computing (ICAC), pp. 1-6. https://doi.org/10.23919/iconac.2019.8895023.
Dong, L., Wu, K., Zhu, J.C., Dai, C., Zhang, L.X., and Guo, J.N., 2019a. Cavitation detection in centrifugal pump based on interior flow‐borne noise using WPD‐PCA‐RBF. Shock and Vibration, 2019(1), P. 8768043. https://doi.org/10.1155/2019/8768043.
Dong, L., Zhao, Y., and Dai, C., 2019b. Detection of inception cavitation in centrifugal pump by fluid‐borne noise diagnostic. Shock and Vibration, 2019(1), P. 9641478. https://doi.org/10.1155/2019/9641478.
Dragomiretskiy, K., and Zosso, D., 2014. Variational mode decomposition. IEEE Transactions on Signal Processing, 62(3), pp. 531-544. https://doi.org/10.1109/tsp.2013.2288675.
Farokhzad, S., 2013. Vibration based fault detection of centrifugal pump by fast Fourier transform and adaptive neuro-fuzzy inference system. Journal of Mechanical Engineering and Technology, 1(3), pp. 82-87. https://doi.org/10.18005/jmet0103001.
Feng, Z., Liang, M., and Chu, F., 2013. Recent advances in time–frequency analysis methods for machinery fault diagnosis: A review with application examples. Mechanical Systems and Signal Processing, 38(1), pp. 165-205. https://doi.org/10.1016/j.ymssp.2013.01.017.
Gao, Q., Huang, T., Zhao, K., Shao, H., and Jin, B., 2024. Multi-source weighted source-free domain transfer method for rotating machinery fault diagnosis. Expert Systems with Applications, 237, P. 121585. https://doi.org/10.1016/j.eswa.2023.121585.
González, J., Fernández, J., Blanco, E., and Santolaria, C., 2002. Numerical simulation of the dynamic effects due to impeller-volute interaction in a centrifugal pump. Journal of Fluids Engineering, 124(2), pp. 348-355. https://doi.org/10.1115/1.1457452.
Gu, F., Shao, Y., Hu, N., Naid, A., and Ball, A.D., 2011. Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment. Mechanical Systems and Signal Processing, 25(1), pp. 360-372. https://doi.org/10.1016/j.ymssp.2010.07.004.
Guelich, J.F., and Bolleter, U., 1992. Pressure pulsations in centrifugal pumps. Journal of Vibration and Acoustics, 114(2), pp. 272-279. https://doi.org/10.1115/1.2930257.
Hamomd, O., Alabied, S., Xu, Y., Daraz, A., Gu, F., and Ball, A., 2017. Vibration based centrifugal pump fault diagnosis based on modulation signal bispectrum analysis. 2017 23rd International Conference on Automation and Computing (ICAC), pp. 1-5. https://doi.org/10.23919/iconac.2017.8082029.
Han, T., Liu, C., Yang, W., and Jiang, D., 2019. A novel adversarial learning framework in deep convolutional neural network for intelligent diagnosis of mechanical faults. Knowledge-Based Systems, 165, pp. 474-487. https://doi.org/10.1016/j.knosys.2018.12.019.
Hoang, D.T., and Kang, H.J., 2019. A survey on Deep Learning based bearing fault diagnosis. Neurocomputing, 335, pp. 327-335. https://doi.org/10.1016/j.neucom.2018.06.078.
Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.C., Tung, C.C., and Liu, H.H., 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 454(1971), pp. 903-995. https://doi.org/10.1098/rspa.1998.0193.
Jardine, A.K.S., Lin, D., and Banjevic, D., 2006. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20(7), pp. 1483-1510. https://doi.org/10.1016/j.ymssp.2005.09.012.
Jiang, W., Zheng, Z., Zhu, Y., and Li, Y., 2015. Demodulation for hydraulic pump fault signals based on local mean decomposition and improved adaptive multiscale morphology analysis. Mechanical Systems and Signal Processing, 58-59, pp. 179-205. https://doi.org/10.1016/j.ymssp.2014.10.017.
Jiao, J., Zhao, M., Lin, J., and Liang, K., 2020. A comprehensive review on convolutional neural network in machine fault diagnosis. Neurocomputing, 417, pp. 36-63. https://doi.org/10.1016/j.neucom.2020.07.088.
Khalid, S., Jo, S.H., Shah, S.Y., Jung, J.H., and Kim, H.S., 2024. Artificial intelligence-driven prognostics and health management for centrifugal pumps: A comprehensive review. Actuators, 13(12), P. 514. https://doi.org/10.3390/act13120514.
Kumar, A., and Kumar, R., 2018. Role of signal processing, modeling and decision making in the diagnosis of rolling element bearing defect: A review. Journal of Nondestructive Evaluation, 38(1), p. 5. https://doi.org/10.1007/s10921-018-0543-8.
Lei, Y., Yang, B., Jiang, X., Jia, F., Li, N., and Nandi, A.K., 2020. Applications of machine learning to machine fault diagnosis: A review and roadmap. Mechanical Systems and Signal Processing, 138, P. 106587. https://doi.org/10.1016/j.ymssp.2019.106587.
Li, X., Zhang, W., and Ding, Q., 2019. Understanding and improving deep learning-based rolling bearing fault diagnosis with attention mechanism. Signal Processing, 161, pp. 136-154. https://doi.org/10.1016/j.sigpro.2019.03.019.
Liu, H., Ding, J., Dai, H., and Tan, M., 2015. Effects of wear ring clearance on hydraulically generated vibration and noise of a centrifugal pump volute. Journal of Vibroengineering, 17(3), pp. 1142-1156. https://doi.org/10.21595/jve.2014.15356.
Liu, H., Xia, R., Wang, K., Jing, Y., and He, X., 2019. Experimental analysis on pressure fluctuation characteristics of a centrifugal pump with vaned-diffuser. Water, 12(1), p. 126. https://doi.org/10.3390/w12010126.
Liu, R., Wang, F., Yang, B., and Qin, S.J., 2020. Multiscale kernel based residual convolutional neural network for motor fault diagnosis under nonstationary conditions. IEEE Transactions on Industrial Informatics, 16(6), pp. 3797-3806. https://doi.org/10.1109/tii.2019.2941868.
McDonald, G.L., Zhao, Q., and Zuo, M.J., 2012. Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mechanical Systems and Signal Processing, 33, pp. 237-255. https://doi.org/10.1016/j.ymssp.2012.06.010.
McFadden, P.D., and Smith, J.D., 1984. Model for the vibration produced by a single point defect in a rolling element bearing. Journal of Sound and Vibration, 96(1), pp. 69-82. https://doi.org/10.1016/0022-460x(84)90595-9.
McNulty, P.J., and Pearsall, I.S., 1982. Cavitation inception in pumps. Journal of Fluids Engineering, 104(1), pp. 99-104. https://doi.org/10.1115/1.3240865.
Mele, J., Guzzomi, A., and Pan, J., 2014. Correlation of centrifugal pump vibration to unsteady flow under variable motor speed. Mechanics & Industry, 15(6), pp. 525-534. https://doi.org/10.1051/meca/2014051.
Miao, Y., Zhang, B., Li, C., Lin, J., and Zhang, D., 2023. Feature mode decomposition: New decomposition theory for rotating machinery fault diagnosis. IEEE Transactions on Industrial Electronics, 70(2), pp. 1949-1960. https://doi.org/10.1109/tie.2022.3156156.
Mohanty, A.R., Pradhan, P.K., Mahalik, N.P., and Dastidar, S.G., 2012. Fault detection in a centrifugal pump using vibration and motor current signature analysis. International Journal of Automation and Control, 6(3/4), P. 261. https://doi.org/10.1504/ijaac.2012.051884.
Mousmoulis, G., Karlsen-Davies, N., Aggidis, G., Anagnostopoulos, I., and Papantonis, D., 2019a. Experimental analysis of cavitation in a centrifugal pump using acoustic emission, vibration measurements and flow visualization. European Journal of Mechanics - B/Fluids, 75, pp. 300-311. https://doi.org/10.1016/j.euromechflu.2018.10.015.
Mousmoulis, G., Yiakopoulos, C., Kassanos, I., Antoniadis, I., and Anagnostopoulos, J., 2019b. Vibration and acoustic emission monitoring of a centrifugal pump under cavitating operating conditions. IOP Conference Series: Earth and Environmental Science, 405(1), P. 012003. https://doi.org/10.1088/1755-1315/405/1/012003.
Muralidharan, V., and Sugumaran, V., 2012. A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis. Applied Soft Computing, 12(8), pp. 2023-2029. https://doi.org/10.1016/j.asoc.2012.03.021.
Muralidharan, V., and Sugumaran, V., 2013a. Feature extraction using wavelets and classification through decision tree algorithm for fault diagnosis of mono-block centrifugal pump. Measurement, 46(1), pp. 353-359. https://doi.org/10.1016/j.measurement.2012.07.007.
Muralidharan, V., and Sugumaran, V., 2013b. Rough set based rule learning and fuzzy classification of wavelet features for fault diagnosis of monoblock centrifugal pump. Measurement, 46(9), pp. 3057-3063. https://doi.org/10.1016/j.measurement.2013.06.002.
Muralidharan, V., Sugumaran, V., and Indira, V., 2014. Fault diagnosis of monoblock centrifugal pump using SVM. Engineering Science and Technology, an International Journal, 17(3), pp. 152-157. https://doi.org/10.1016/j.jestch.2014.04.005.
Neill, G.D., Reuben, R.L., Sandford, P.M., Brown, E.R., and Steel, J.A., 1997. Detection of incipient cavitation in pumps using acoustic emission. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 211(4), pp. 267-277. https://doi.org/10.1243/0954408971529737.
Ni, Q., Ji, J.C., Feng, K., and Halkon, B., 2022. A fault information-guided variational mode decomposition (FIVMD) method for rolling element bearings diagnosis. Mechanical Systems and Signal Processing, 164, p. 108216. https://doi.org/10.1016/j.ymssp.2021.108216.
Pandarakone, S.E., Mizuno, Y., and Nakamura, H., 2017. Distinct fault analysis of induction motor bearing using frequency spectrum determination and support vector machine. IEEE Transactions on Industry Applications, 53(3), pp. 3049-3056. https://doi.org/10.1109/tia.2016.2639453.
Parrondo-Gayo, J.L., González-Pérez, J., and Fernández-Francos, J., 2002. The Effect of the operating point on the pressure fluctuations at the blade passage frequency in the volute of a centrifugal pump. Journal of Fluids Engineering, 124(3), pp. 784-790. https://doi.org/10.1115/1.1493814.
Pei, J., Yuan, S., Benra, F.K., and Dohmen, H.J., 2012. Numerical prediction of unsteady pressure field within the whole flow passage of a radial single-blade pump. Journal of Fluids Engineering, 134(10), p. 101103. https://doi.org/10.1115/1.4007382.
Randall, R.B., and Antoni, J., 2011. Rolling element bearing diagnostics—A tutorial. Mechanical Systems and Signal Processing, 25(2), pp. 485-520. https://doi.org/10.1016/j.ymssp.2010.07.017.
Ren, X., Fan, H., Xie, Z., and Liu, B., 2019. Stationary stall phenomenon and pressure fluctuation in a centrifugal pump at partial load condition. Heat and Mass Transfer, 55(8), pp. 2277-2288. https://doi.org/10.1007/s00231-019-02579-0.
Safizadeh, M.S., and Latifi, S.K., 2014. Using multi-sensor data fusion for vibration fault diagnosis of rolling element bearings by accelerometer and load cell. Information Fusion, 18, pp. 1-8. https://doi.org/10.1016/j.inffus.2013.10.002.
Sakthivel, N.R., Sugumaran, V., and Nair, B.B., 2010a. Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump. Mechanical Systems and Signal Processing, 24(6), pp. 1887-1906. https://doi.org/10.1016/j.ymssp.2010.01.008.
Sakthivel, N.R., Sugumaran, V., and Babudevasenapati, S., 2010b. Vibration based fault diagnosis of monoblock centrifugal pump using decision tree. Expert Systems with Applications, 37(6), pp. 4040-4049. https://doi.org/10.1016/j.eswa.2009.10.002.
Sakthivel, N.R., Nair, B.B., and Sugumaran, V., 2012. Soft computing approach to fault diagnosis of centrifugal pump. Applied Soft Computing, 12(5), pp. 1574-1581. https://doi.org/10.1016/j.asoc.2011.12.009.
Sakthivel, N.R., Nair, B.B., Elangovan, M., Sugumaran, V., and Saravanmurugan, S., 2014. Comparison of dimensionality reduction techniques for the fault diagnosis of mono block centrifugal pump using vibration signals. Engineering Science and Technology, an International Journal, 17(1), pp. 30-38. https://doi.org/10.1016/j.jestch.2014.02.005.
Shari, A., Ali, A.A., and Almudhaffer, M., 2019. Combination of FFT & ICA methods for faults analysis of rotating machine. Proceedings of the International Conference on Information and Communication Technology, pp. 196-202. https://doi.org/10.1145/3321289.3321307.
Sharma, A., Amarnath, M., and Kankar, P., 2014. Feature extraction and fault severity classification in ball bearings. Journal of Vibration and Control, 22(1), pp. 176-192. https://doi.org/10.1177/1077546314528021.
Sinha, J.K., and Rao, A.R., 2006. Vibration based diagnosis of a centrifugal pump. Structural Health Monitoring, 5(4), pp. 325-332. https://doi.org/10.1177/1475921706067760.
Smith, W.A., and Randall, R.B., 2015. Rolling element bearing diagnostics using the Case Western Reserve University data: A benchmark study. Mechanical Systems and Signal Processing, 64-65, pp. 100-131. https://doi.org/10.1016/j.ymssp.2015.04.021.
Song, M., Zhi, Y., An, M., Xu, W., Li, G., and Wang, X., 2024. Centrifugal pump cavitation fault diagnosis based on feature-level multi-source information fusion. Processes, 12(1), p. 196. https://doi.org/10.3390/pr12010196.
Spence, R., and Amaral-Teixeira, J., 2009. A CFD parametric study of geometrical variations on the pressure pulsations and performance characteristics of a centrifugal pump. Computers & Fluids, 38(6), pp. 1243-1257. https://doi.org/10.1016/j.compfluid.2008.11.013.
Stefatos, G., and Hamza, A.B., 2010. Dynamic independent component analysis approach for fault detection and diagnosis. Expert Systems with Applications, 37(12), pp. 8606-8617. https://doi.org/10.1016/j.eswa.2010.06.101.
Sunal, C.E., Dyo, V., and Velisavljevic, V., 2022. Review of machine learning based fault detection for centrifugal pump induction motors. IEEE Access, 10, pp. 71344-71355. https://doi.org/10.1109/access.2022.3187718.
Sunal, C.E., Velisavljevic, V., Dyo, V., Newton, B., and Newton, J., 2024. Centrifugal pump fault detection with convolutional neural network transfer learning. Sensors, 24(8), P. 2442. https://doi.org/10.3390/s24082442.
Tandon, N., and Choudhury, A., 1999. A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8), pp. 469-480. https://doi.org/10.1016/s0301-679x(99)00077-8.
Tang, Y., Wang, F., Wang, C., Ye, C., Qu, Q., and Xu, J., 2024. Investigation on the influence of seal clearance leakage on the rotating stall characteristics for a centrifugal pump. Physics of Fluids, 36(2), P. 025176. https://doi.org/10.1063/5.0197065.
Thomson, W.T., and Fenger, M., 2001. Current signature analysis to detect induction motor faults. IEEE Industry Applications Magazine, 7(4), pp. 26-34. https://doi.org/10.1109/2943.930988.
Tian, X., Gu, J.X., Rehab, I., Abdalla, G.M., Gu, F., and Ball, A.D., 2018. A robust detector for rolling element bearing condition monitoring based on the modulation signal bispectrum and its performance evaluation against the
Kurtogram. Mechanical Systems and Signal Processing, 100, pp. 167-187. https://doi.org/10.1016/j.ymssp.2017.07.037.
Ullah, N., Ahmad, Z., Siddique, M.F., Im, K., Shon, D.K., Yoon, T.H., Yoo, D.S., and Kim, J.M., 2023. An intelligent framework for fault diagnosis of centrifugal pump leveraging wavelet coherence analysis and deep learning. Sensors, 23(21), P. 8850. https://doi.org/10.3390/s23218850.
Wang, Y., and Liang, M., 2011. An adaptive SK technique and its application for fault detection of rolling element bearings. Mechanical Systems and Signal Processing, 25(5), pp. 1750-1764. https://doi.org/10.1016/j.ymssp.2010.12.008.
Wang, Y., Xiang, J., Markert, R., and Liang, M., 2016. Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications. Mechanical Systems and Signal Processing, 66-67, pp. 679-698. https://doi.org/10.1016/j.ymssp.2015.04.039.
Wang, C., Zhang, Y., Li, Z., Xu, A., Xu, C., and Shi, Z., 2018. Pressure fluctuation–vortex interaction in an ultra-low specific-speed centrifugal pump. Journal of Low Frequency Noise, Vibration and Active Control, 38(2), pp. 527-543. https://doi.org/10.1177/1461348418817697.
Wang, J., Zhang, L., Zheng, Y., and Wang, K., 2019. Adaptive prognosis of centrifugal pump under variable operating conditions. Mechanical Systems and Signal Processing, 131, pp. 576-591. https://doi.org/10.1016/j.ymssp.2019.06.008.
Wodecki, J., Michalak, A., and Zimroz, R., 2018. Optimal filter design with progressive genetic algorithm for local damage detection in rolling bearings. Mechanical Systems and Signal Processing, 102, pp. 102-116. https://doi.org/10.1016/j.ymssp.2017.09.008.
Wu, Z., and Huang, N.E., 2009. ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 01(01), pp. 1-41. https://doi.org/10.1142/s1793536909000047.
Wu, C., Yang, J., Yang, S., Wu, P., Huang, B., and Wu, D., 2023. A Review of fluid-induced excitations in centrifugal pumps. Mathematics, 11(4), P. 1026. https://doi.org/10.3390/math11041026.
Wu, J., Cabrera, D., Cerrada, M., Sánchez, R.V., Sancho, F., and Estupinan, E., 2025. Fault diagnosis generalization improvement through contrastive learning for a multistage centrifugal pump. IEEE Transactions on Reliability, 74(1), pp. 2373-2381. https://doi.org/10.1109/tr.2024.3381014.
Xu, Y., Zhen, D., Gu, J.X., Rabeyee, K., Chu, F., Gu, F., and Ball, A.D., 2021. Autocorrelated envelopes for early fault detection of rolling bearings. Mechanical Systems and Signal Processing, 146, P. 106990. https://doi.org/10.1016/j.ymssp.2020.106990.
Xu, Z., Wang, Z., Gao, C., Zhang, K., Lv, J., Wang, J., and Liu, L., 2024. A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks. Computers in Industry, 163, P. 104155. https://doi.org/10.1016/j.compind.2024.104155.
Xue, H., Li, Z., Wang, H., and Chen, P., 2014. Intelligent diagnosis method for centrifugal pump system using vibration signal and support vector machine. Shock and Vibration, 2014, pp. 1-14. https://doi.org/10.1155/2014/407570.
Yan, R., Gao, R.X., and Chen, X., 2014. Wavelets for fault diagnosis of rotary machines: A review with applications. Signal Processing, 96, pp. 1-15. https://doi.org/10.1016/j.sigpro.2013.04.015.
Zhang, N., Yang, M., Gao, B., and Li, Z., 2015. Vibration characteristics induced by cavitation in a centrifugal pump with slope volute. Shock and Vibration, 2015, pp. 1-10. https://doi.org/10.1155/2015/294980.
Zhang, M., Jiang, Z., and Feng, K., 2017. Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump. Mechanical Systems and Signal Processing, 93, pp. 460-493. https://doi.org/10.1016/j.ymssp.2017.02.013.
Zhang, W., Li, C., Peng, G., Chen, Y., and Zhang, Z., 2018. 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, 100, pp. 439-453. https://doi.org/10.1016/j.ymssp.2017.06.022.
Zhang, Y., Ren, Z., Feng, K., Yu, K., Beer, M., and Liu, Z., 2023. Universal source-free domain adaptation method for cross-domain fault diagnosis of machines. Mechanical Systems and Signal Processing, 191, p. 110159. https://doi.org/10.1016/j.ymssp.2023.110159.
Zhao, H., Sun, M., Deng, W., and Yang, X., 2016. A new feature extraction method based on EEMD and multi-scale fuzzy entropy for motor bearing. Entropy, 19(1), P. 14. https://doi.org/10.3390/e19010014.
Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., and Gao, R.X., 2019. Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, pp. 213-237. https://doi.org/10.1016/j.ymssp.2018.05.050.
Zhao, Z., Li, T., Wu, J., Sun, C., Wang, S., Yan, R., and Chen, X., 2020. Deep learning algorithms for rotating machinery intelligent diagnosis: An open source benchmark study. ISA Transactions, 107, pp. 224-255. https://doi.org/10.1016/j.isatra.2020.08.010.
Zheng, L., Chen, X., Qu, J., and Ma, X., 2023. a review of pressure fluctuations in centrifugal pumps without or with clearance flow. Processes, 11(3), P. 856. https://doi.org/10.3390/pr11030856.