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<p>Aiming at the problems of difficulty in extracting vibration signal features and low recognition accuracy of bearing fault, a new fault diagnosis method based on ISSA-VMD and IMSE was proposed. This method leverages an improved sparrow search algorithm (ISSA) to optimize the variational mode decomposition (VMD) method and employs an improved multiscale sample entropy (IMSE) for fault feature extraction. Initially, the ISSA algorithm optimizes two critical parameters of the VMD method: the number of modes <InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44147_2025_613_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="19" /> </InlineMediaObject> <EquationSource Format="TEX">\(K\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>K</mi> </math></EquationSource> </InlineEquation> and the penalty factor <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44147_2025_613_Article_IEq2.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(\alpha\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation>, to obtain the optimal parameter combination <InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44147_2025_613_Article_IEq3.gif" Format="GIF" Height="19" Rendition="HTML" Resolution="72" Type="Linedraw" Width="23" /> </InlineMediaObject> <EquationSource Format="TEX">\([K\)</EquationSource> <EquationSource Format="MATHML"><math> <mrow> <mo stretchy="false">[</mo> <mi>K</mi> </mrow> </math></EquationSource> </InlineEquation>, <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="44147_2025_613_Article_IEq4.gif" Format="GIF" Height="10" Rendition="HTML" Resolution="72" Type="Linedraw" Width="14" /> </InlineMediaObject> <EquationSource Format="TEX">\(\alpha\)</EquationSource> <EquationSource Format="MATHML"><math> <mi>α</mi> </math></EquationSource> </InlineEquation>]. The optimized VMD method is then used to decompose the bearing vibration signals for signal reconstruction analysis. Subsequently, the IMSE entropy algorithm is applied to the reconstructed signals to extract fault features, resulting in the required fault feature vector set. Finally, this extracted fault feature vector set is input into a multi-kernel extreme learning machine model for fault classification and diagnosis. The results show that the identification accuracy of the fault diagnosis of reciprocating compressor plain bearings and automobile rolling bearings is significantly improved, and the method has better fault feature extraction effect.</p>

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Research on bearing fault diagnosis based on ISA-VMD and IMSE

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  • 发表日期：

    2025年4月11日

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  • Feng Yan

摘要

Aiming at the problems of difficulty in extracting vibration signal features and low recognition accuracy of bearing fault, a new fault diagnosis method based on ISSA-VMD and IMSE was proposed. This method leverages an improved sparrow search algorithm (ISSA) to optimize the variational mode decomposition (VMD) method and employs an improved multiscale sample entropy (IMSE) for fault feature extraction. Initially, the ISSA algorithm optimizes two critical parameters of the VMD method: the number of modes \(K\) K and the penalty factor \(\alpha\) α , to obtain the optimal parameter combination \([K\) [ K , \(\alpha\) α ]. The optimized VMD method is then used to decompose the bearing vibration signals for signal reconstruction analysis. Subsequently, the IMSE entropy algorithm is applied to the reconstructed signals to extract fault features, resulting in the required fault feature vector set. Finally, this extracted fault feature vector set is input into a multi-kernel extreme learning machine model for fault classification and diagnosis. The results show that the identification accuracy of the fault diagnosis of reciprocating compressor plain bearings and automobile rolling bearings is significantly improved, and the method has better fault feature extraction effect.