基于EWT-NPDLPP-LSSVM的水泵机组关键部件故障诊断方法

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关键词：水泵机组；故障诊断；经验小波变换降噪;NPDLPP特征约简；最小二乘支持向量机中图分类号：S277.9；TH212 文献标志码：A 文章编号：1674-8530（2026）01-0001-09DOI：10.3969/j.issn.1674-8530.24.0006

Abstract： To improve the efficiency and accuracy of fault diagnosis of key compo        nents in pump units， considering the operational environment of the pump system，a comprehensive diagnostic method integrating signal denoising，feature extraction，dimensionality reduction，and fault identification was proposed.Firstly，empirical wavelet transform （EWT） was employed to denoise the original signals， effectively mitigating the influence of environmental noise and improving data quality.Secondly，to comprehensively characterize the operational state of the pump unit，a multi-source fusion feature extraction method was designed，incorporating multi-channel signals （including vibration，pressure pulsation，electrical，and other signals）and multi-domain features （time domain，frequency domain，and time-frequency domain），based on the specific operating characteristics of the pump system. On this basis，an improved local preserving projections （LPP）method， termed nearby probability distance （NPP），was proposed to eliminate redundant information from the high-dimensional features.Further， least squares support vector machine （LSSVM） was applied to classify diferent fault types.The experimental results demonstrate that the proposed EWT-NPDLPP -LSSVM-based diagnostic method achieves a high diagnosticaccuracyof 99.44% and superior computational efficiency，which confirms the validity and engineering practical applicability in scenarios.

Key Words： pump unit;fault diagnosis ; empirical wavelet transform noise reduction; NPDLPP feature reduction；least squares support vector machine

水泵机组是实现水的输送和调节水压的关键设备，其安全平稳运行是保证水资源配置工程正常运行的关键.水泵机组关键部件发生故障可能导致整个机组损坏，影响泵站系统正常运行，进而造成巨大损失[1-4].同时，随着水泵机组容量的不断增大、系统和结构的复杂性不断增加，水泵机组安全、高效、稳定运行对水泵机组故障诊断的效率和精度要求进一步提高[5].因此，研究水泵机组关键部件故障诊断具有实际的工程意义。（剩余11585字）