Feature Extraction Based on Hierarchical Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis

Chen, Zhixiang; Yang, Yang; He, Chun Lei; Liu, Yongbin; Liu, Xianzeng; Cao, Zhan

doi:10.1109/tim.2023.3277938

Cited by 19 publications

(8 citation statements)

References 43 publications

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“…They are used to measure the probability of generating new patterns in a time series. If the probability of generating new patterns is higher, then the complexity of the sequence is higher [23][24][25]. Fuzzy entropy introduces a fuzzy affiliation function based on sample entropy to deal with the similarity measure of a time series.…”

Section: Arc Signal Feature Extractionmentioning

confidence: 99%

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

Wang,

Zhao

2024

Energies

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In low-voltage AC distribution systems, when a series arc fault occurs in a branch with multiple loads operating in parallel, it will be significantly more difficult to identify. Existing arc fault detection methods make it difficult to effectively detect faults occurring in the lower-level branch. This study introduces a novel series arc fault detection approach based on the improved northern goshawk optimization adaptive base class LogitBoost (INGO-ABCLogitBoost) algorithm. Considering the zero-rest, intermittent, and random fluctuation and high-frequency features of the arc current, the zero-rest coefficient, discrete coefficient, harmonic amplitude, and wavelet entropy are proposed to establish the high-dimensional feature matrix of the arc current. The ReliefF feature selection algorithm is used to optimize feature quality and decrease feature dimensionality. Subsequently, the ABCLogitBoost fault detection model is proposed, with the INGO algorithm applied to optimize the model parameters, thus enhancing the model’s diagnostic capabilities. The efficacy of the proposed diagnostic model is validated through the construction of a multi-load arc simulation system. The simulation results show that the overall fault diagnosis accuracy of the proposed method reaches 99.01% and can effectively identify the fault load types, which helps to locate the fault location.

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Section: Arc Signal Feature Extractionmentioning

confidence: 99%

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

Wang,

Zhao

2024

Energies

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“…A smaller entropy value indicates that the sequence contains more meaningful information and is smoother [37]. Among information entropy metrics, envelope spectral entropy (ESE) has the characteristics of simple calculation and fewer parameter inputs [38]. Therefore, in this paper, ESE was employed as the fitness function for the IPOA-VMD optimization model.…”

Section: Envelope Spectral Entropy (Ese)mentioning

confidence: 99%

A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA

Yi,

Cai,

Tang

et al. 2023

Sensors

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Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)–variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.

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“…1 The vibration signal during a bearing fault usually contains considerable noise and numerous frequency components, showcasing characteristics such as non-linearity, non-stationarity, weak periodicity, and a low signal-to-noise ratio. [2][3][4] Signals decompose algorithms, addressing non-linearity and nonstationarity, are a typical class of methods for fault feature extraction including local feature scale decomposition, 5,6 intrinsic time scale decomposition, 7 and local mean decomposition. Huang et al proposed the empirical mode decomposition (EMD) method, which offers stabilization of complex signals and decomposes vibration signals into multiple intrinsic mode functions (IMF).…”

Section: Introductionmentioning

confidence: 99%

“…Detail:Step 1: Initialize the number of populations of sparrow algorithm; maximum iteration parameter is [O, M] (O = 10, M = 10); determine the optimization range of VMD parameter [ K , α ], ( k ∈ [3, 7], α ∈ [100, 3000] 2 );Step 2: Initialize the position vector of sparrow within value range and subject original signals to VMD according to position vector; with minimum envelope entropy as the fitness function of sparrow algorithm, self-adaptively determine [ K , α ];Step 3: Substitute self-adaptively determined [ K , α ] into VMD and self-adaptively decompose vibration signals to get k IMFs;Step 4: Calculate self-correlation functions of IMFs and normalize each correlation function;Step 5: Calculate the RMS of correlation function after normalization;Step 6: Choose the IMFs as sensitive fault components, which correspond to 2 maximum RMS;Step 7: Blend 2 IMFs with the vector bispectrum;Step 8: Take frequency spectrum analysis of blended signals to extract fault feature frequencies of bearings.…”

Section: Algorithm Introductionmentioning

confidence: 99%

An identification method of compound faults of rolling bearings blending variational mode decomposition and vector bispectrum

Yu,

Wang,

et al. 2024

Noise & Vibration Worldwide

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To solve the difficulty in correctly identifying a compound fault of rolling bearing, a method combining variational mode decomposition (VMD) and harmonic fusion vector bispectrum (HFVB) is proposed. Firstly, to achieve adaptive decomposition of signals, the characteristic ability of envelope entropy to represent signal sparsity is utilized. By employing the minimum envelope entropy as the fitness function for the sparrow search algorithm (SSA), the decomposition levels and penalty factors of VMD are adaptively determined. Secondly, root-mean-square value is treated as fault feature index to self-adaptively choose from intrinsic mode function (IMF) which can embody fault features of bearings. Thirdly, to further highlight fault features, HFVB is used to blend chosen IMFs. Finally, faults of bearings were recognized with spectrum of signals. To verify the effectiveness of proposed method, an analysis was given to vibration signals in different situations, and SSA-VMD-HFVB was compared with classical method. The results demonstrate that the proposed SSA-VMD-HFVB method facilitates the adaptive decomposition of VMD, enabling the selection and effective integration of sensitive fault component signals. This approach enhances the accuracy of complex fault diagnosing in rolling bearings.

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Feature Extraction Based on Hierarchical Improved Envelope Spectrum Entropy for Rolling Bearing Fault Diagnosis

Cited by 19 publications

References 43 publications

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

Multi-Branch Line Fault Arc Detection Method Based on the Improved Northern Goshawk Optimization Adaptive Base Class LogitBoost Algorithm

A Rolling Bearing Fault Feature Extraction Algorithm Based on IPOA-VMD and MOMEDA

An identification method of compound faults of rolling bearings blending variational mode decomposition and vector bispectrum

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