<p>In practical conditions such as escalators, vibration signals collected from rolling bearings often exhibit strong non-stationarity and significant noise interference, making early compound fault feature extraction challenging for traditional methods. To address this, this paper proposes an intelligent compound fault diagnosis method integrating adaptive feature mode decomposition and improved multipoint optimal minimum entropy deconvolution adjusted (AFMD-IMOMEDA). First, a novel objective function, correlation ensemble kurtosis (CEK), is designed to comprehensively evaluate the impact intensity of modal components. Based on CEK, an improved crested porcupine optimizer (ICPO) algorithm is proposed and validated through six benchmark functions and comparison with five optimization algorithms. Subsequently, ICPO jointly optimizes the key parameters of feature mode decomposition (FMD) (i.e., the mode number <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$M$</EquationSource> <EquationSource Format="MATHML"><math> <mi>M</mi> </math></EquationSource> </InlineEquation> and filtering length <InlineEquation ID="IEq2"> <EquationSource Format="TEX">${L}_{1}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>L</mi> <mn>1</mn> </msub> </math></EquationSource> </InlineEquation>) to realize an adaptive decomposition strategy. After decomposition, the improved Gini index (IGI) is used to select effective modal components, which are then reconstructed to enhance fault-related transient features. To separate different fault components in the reconstructed compound fault signals, these signals are input to IMOMEDA, where a new fitness function, fault characteristic energy ratio (FCER), is introduced to assess the prominence of fault impact features. ICPO is used to jointly optimize MOMEDA parameters (i.e., the filter length <InlineEquation ID="IEq3"> <EquationSource Format="TEX">${L}_{2}$</EquationSource> <EquationSource Format="MATHML"><math> <msub> <mi>L</mi> <mn>2</mn> </msub> </math></EquationSource> </InlineEquation> and fault impact period <InlineEquation ID="IEq4"> <EquationSource Format="TEX">$T$</EquationSource> <EquationSource Format="MATHML"><math> <mi>T</mi> </math></EquationSource> </InlineEquation>), enabling parameter tuning and filtering for distinct fault types with characteristic impact periods, thus achieving precise separation of compound fault components. Finally, Hilbert envelope analysis extracts characteristic frequencies for accurate diagnosis of rolling bearing compound faults. Simulation results and experimental analyses based on the publicly available XJTU-SY bearing degradation dataset demonstrate that the proposed AFMD-IMOMEDA method significantly outperforms traditional FMD and maximum correlated kurtosis deconvolution (MCKD) in fault feature recognition accuracy and fault separation capability.</p>

Compound fault diagnosis of escalator bearings based on parameter-optimized feature mode decomposition and multipoint optimal minimum entropy deconvolution adjustment

期刊论文
发表日期：

2026年1月6日

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Fengfeng Fu,
Min Huang,
Xupeng Zhang,
Yu Feng,
Shihao Yang,
Yue Liu

摘要

In practical conditions such as escalators, vibration signals collected from rolling bearings often exhibit strong non-stationarity and significant noise interference, making early compound fault feature extraction challenging for traditional methods. To address this, this paper proposes an intelligent compound fault diagnosis method integrating adaptive feature mode decomposition and improved multipoint optimal minimum entropy deconvolution adjusted (AFMD-IMOMEDA). First, a novel objective function, correlation ensemble kurtosis (CEK), is designed to comprehensively evaluate the impact intensity of modal components. Based on CEK, an improved crested porcupine optimizer (ICPO) algorithm is proposed and validated through six benchmark functions and comparison with five optimization algorithms. Subsequently, ICPO jointly optimizes the key parameters of feature mode decomposition (FMD) (i.e., the mode number $M$ $M$ and filtering length ${L}_{1}$ $L_{1}$ ) to realize an adaptive decomposition strategy. After decomposition, the improved Gini index (IGI) is used to select effective modal components, which are then reconstructed to enhance fault-related transient features. To separate different fault components in the reconstructed compound fault signals, these signals are input to IMOMEDA, where a new fitness function, fault characteristic energy ratio (FCER), is introduced to assess the prominence of fault impact features. ICPO is used to jointly optimize MOMEDA parameters (i.e., the filter length ${L}_{2}$ $L_{2}$ and fault impact period $T$ $T$ ), enabling parameter tuning and filtering for distinct fault types with characteristic impact periods, thus achieving precise separation of compound fault components. Finally, Hilbert envelope analysis extracts characteristic frequencies for accurate diagnosis of rolling bearing compound faults. Simulation results and experimental analyses based on the publicly available XJTU-SY bearing degradation dataset demonstrate that the proposed AFMD-IMOMEDA method significantly outperforms traditional FMD and maximum correlated kurtosis deconvolution (MCKD) in fault feature recognition accuracy and fault separation capability.