Slurry pumps constitute critical equipment in mining and metallurgical industries, where impeller wear critically compromises operational efficiency and safety. To overcome the inadequacies in early fault detection and significant noise susceptibility in complex operating environments, this study proposes a novel diagnostic method integrating Theil-entropy-impact energy fusion with linear frequency modulation optimization. First, a fused Theil entropy-impact energy feature index is constructed, capturing impeller wear-induced cyclostationary patterns and transient impacts through frequency-domain synergy. Second, an optimized spectral estimation method based on the Chirp Z-Transform is designed, enabling high-precision fault frequency extraction with a limited number of sampling points. Finally, the effectiveness and accuracy of the proposed method were validated via simulated and industrial data, supplemented by a performance comparison of vibration and acoustic emission approaches. The proposed method provides a reference for monitoring the early wear state of slurry pump impellers.
WróbelJPietrusiakDRozmusR, et al. Failure analysis and guidelines for further exploitation of centrifugal slurry pumps used for copper flotation waste transport: a case study. Eng Fail Anal2025; 174: 109479.
2.
DuanALinZChenD, et al. A review on the hydraulic performance and erosion wear characteristic of the centrifugal slurry pump. Particuology2024; 95: 370–392.
3.
WangHTanZKuangS, et al. DDPM investigation on centrifugal slurry pump with inlet and sideline configuration retrofit. Powder Technol2025; 449: 120386.
4.
ShenZKongFLiuY, et al. Erosion analysis of static components in slurry pumps based on reverse modeling. Fluid Dyn Mater Process2025; 21(3): 589–603.
5.
GonçalvesJPSFruettFDalfré FilhoJG, et al. Faults detection and classification in a centrifugal pump from vibration data using Markos parameters. Mech Syst Signal Process2021; 158: 107694.
6.
ZhangMJiangZFengK. Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump. Mech Syst Signal Process2017; 93: 460–493.
7.
SunHLanQSiQ, et al. Cavitation detection via motor current signal analysis for a centrifugal pump in the pumped storage pump station. J Energy Storage2024; 81: 110417.
8.
XiaoCATangHSRenY, et al. Adaptive MOMEDA based on improved advance-retreat algorithm for fault features extraction of axial piston pump. ISA Trans2022; 128: 503–520.
9.
MoshrefzadehAFasanaA. The Autogram: an effective approach for selecting the optimal demodulation band in rolling element bearings diagnosis. Mech Syst Signal Process2018; 105: 294–318.
10.
LiYWangJFengD, et al. Bearing fault diagnosis method based on maximum noise ratio kurtosis product deconvolution with noise conditions. Measurement2023; 221: 113542.
11.
HashimSShakyaP. A spectral kurtosis based blind deconvolution approach for spur gear fault diagnosis. ISA Trans2023; 142: 492–500.
12.
PanHYinXChengJ, et al. Periodic component pursuit-based kurtosis deconvolution and its application in roller bearing compound fault diagnosis. Mech Mach Theory2023; 185: 105337.
13.
ZhangCLiuYWanF, et al. Adaptive filtering enhanced windowed correlated kurtosis for multiple faults diagnosis of locomotive bearings. ISA Trans2020; 101: 421–429.
14.
ZhouJGeKShenY, et al. The PSQSgram: a new adaptive multi-channel periodic pulse extraction method for bearing fault diagnosis. Expert Syst Appl2025; 277: 127162.
15.
WuCGaoYLiC, et al. Analytic Mode decomposition and windowed three-point interpolated Chirp-Z transform for voltage flicker components detection. Electr Power Syst Res2020; 186: 106382.
16.
GholamiAFarshadM. Fast hyperbolic Radon transform using chirp-z transform. Digit Signal Process2019; 87: 34–42.
17.
JiangLDuHBuY, et al. Deep learning-based multilabel compound-fault diagnosis in centrifugal pumps. Ocean Eng2024; 314: 119697.
18.
CaoSHuZLuoX, et al. Research on fault diagnosis technology of centrifugal pump blade crack based on PCA and GMM. Measurement2021; 173: 108558.
19.
HanYZouJGongB, et al. The use of model-based voltage and current analysis for torque oscillation detection and improved condition monitoring of centrifugal pumps. Mech Syst Signal Process2025; 222: 111781.
20.
FuQLiuYZhangR, et al. Intelligent condition monitoring for the vertical centrifugal pump using multimodal signals and hybrid models. Measurement2025; 242: 115813.
21.
XuZWangZGaoC, et al. A digital twin system for centrifugal pump fault diagnosis driven by transfer learning based on graph convolutional neural networks. Comput Ind2024; 163: 104155.
22.
PrasshanthCVNaveen VenkateshSMahantaTK, et al. Fault diagnosis of monoblock centrifugal pumps using pre-trained deep learning models and scalogram images. Eng Appl Artif Intell2024; 136: 109022.
23.
KumarDSingh RanawatNKumar KankarP, et al. InceptionV3 based blockage fault diagnosis of centrifugal pump. Adv Eng Inform2025; 65: 103181.
24.
CuiDZhouRLiH, et al. Rotor fault diagnosis of centrifugal pumps in nuclear power plants based on CWGAN-GP-CNN for imbalanced dataset. Prog Nucl Energy2025; 178: 105500.
25.
DongLChenZhuaR, et al. Research on diagnosis method of centrifugal pump rotor faults based on IPSO-VMD and RVM. Nucl Eng Technol2023; 55(3): 827–838.
26.
AltobiMASBevanGWallaceP, et al. Fault diagnosis of a centrifugal pump using MLP-GABP and SVM with CWT. Eng Sci Technol2019; 22(3): 854–861.
27.
RapurJSTiwariR. Experimental fault diagnosis for known and unseen operating conditions of centrifugal pumps using MSVM and WPT based analyses. Measurement2019; 147: 106809.
28.
KumarAKumarRXiangJ, et al. Digital twin-assisted AI framework based on domain adaptation for bearing defect diagnosis in the centrifugal pump. Measurement2024; 235: 115013.
29.
KumarDDewanganATiwariR, et al. Identification of inlet pipe blockage level in centrifugal pump over a range of speeds by deep learning algorithm using multi-source data. Measurement2021; 186: 110146.
30.
SakthivelNRSugumaranVNair BinoyB. Comparison of decision tree-fuzzy and rough set-fuzzy methods for fault categorization of mono-block centrifugal pump. Mech Syst Signal Process2010; 24(6): 1887–1906.
31.
DaiCHuSZhangY, et al. Cavitation state identification of centrifugal pump based on CEEMD-DRSN. Nucl Eng Technol2023; 55(4): 1507–1517.
32.
MiaoYZhaoMLinJ. Periodicity-impulsiveness spectrum based on singular value negentropy and its application for identification of optimal frequency band. IEEE Trans Ind Electron2018; 66(4): 3127–3138.
33.
BarszczTJabŁońskiA. A novel method for the optimal band selection for vibration signal demodulation and comparison with the Kurtogram. Mech Syst Signal Process2010; 25(1): 431–451.
