Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods

Wang, Haibin; Long, Junbo; Liu, Zeliang; You, Fang

doi:10.1155/2020/8823389

Cited by 5 publications

(6 citation statements)

References 25 publications

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“…More feature information can be extracted from the GGCM of bispectrum analysis [25]. But if too many feature parameters are selected, this can lead to excessive computational effort and affect the accuracy of the fault identification.…”

Section: Texture Feature Extraction With Gray-gradient Co-occurrence ...mentioning

confidence: 99%

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

Wang

2023

Sensors

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An escalator is an essential large-scale public transport equipment; once it fails, this inevitably affects the operation of the escalator and even leads to safety concerns, or perhaps accidents. As an important structural part of the escalator, the foundation of the main engine can cause the operation of the escalator to become abnormal when its fixing bolts become loose. Aiming to reduce the difficulty of extracting the fault features of the footing bolt when it loosens, a fault feature extraction method is proposed in this paper based on empirical wavelet transform (EWT) and the gray-gradient co-occurrence matrix (GGCM). Firstly, the Teager energy operator and multi-scale peak determination are used to improve the spectral partitioning ability of EWT, and the improved EWT is used to decompose the original foundation vibration signal into a series of empirical mode functions (EMFs). Then, the gray-gradient co-occurrence matrix of each EMF is constructed, and six texture features of the gray-gradient co-occurrence matrix are calculated as the fault feature vectors of this EMF. Finally, the fault features of all EMFs are fused, and the degree of the loosening of the escalator foundation bolt is identified using the fused multi-scale feature vector and BiLSTM. The experimental results show that the proposed method based on EWT and GGCM feature extraction can diagnose the loosening degree of foundation bolts more effectively and has a certain engineering application value.

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Section: Texture Feature Extraction With Gray-gradient Co-occurrence ...mentioning

confidence: 99%

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

Wang

2023

Sensors

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“…MN represent the number of rows and columns of the gray matrix. 3 Texture feature extraction with gray-gradient co-occurrence matrix More feature information can be extracted from the GGCM of bispectrum analysis [25]. But if too many feature parameters are selected, this can lead to excessive computational effort and affect the accuracy of fault identification.…”

Section: Normalization Of Gray Matrix and Gradient Matrixmentioning

confidence: 99%

“…Bispectrum analysis is a very effective algorithm, which contains all the phase information of the processed signal and can completely suppress the effect of Gaussian noise [23].Liu et al [24] applied it to the detection of microcracks under mixed frequency excitation. Wang et al [25] used Fractional bispectrum analysis to identify Fault Characteristic. And the results showed that for small cracks bispectrum analysis can effectively extract the fault features.…”

Section: Introductionmentioning

confidence: 99%

Fault Feature Extraction for Anchor Bolt Loosening of Escalator Based on EWT and Bispectrum Analysis

Zheng¹

2021

converter

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Escalator is an essential large-scale public transportation equipment. Once the failure occurs, it will inevitably affect the operation and even cause safety accidents. As an important part of the structure of escalator, the loosening of the anchor bolt will lead to abnormal operation of escalator. Aiming at the current difficultyin extracting the fault features of anchor bolt loosening, a fault feature extraction method of escalator anchor loosening is constructed based on empirical wavelet transform (EWT) and bispectrum analysis. First, perform EWT decomposition of the original footing vibration acceleration signal to obtain a series of empirical mode functions(EMFs).Then, for each empirical mode function, the bispectrum was calculated by using bispectrum analysis method, and six texture features of the bispectrum were extracted as fault feature vectors by means of gray-gradient co-occurrence matrix. Finally, the extracted multi-scale fault feature vectors and bi-directional longshort-term memory (BI-LSTM) were used to classify and identify the four types of fault signals with different degrees of foot loosening, and the fault types of foot loosening were determined. The results show that the feature extraction method based on empirical wavelet decomposition and bispectrum analysis can more effectively identify the loosening level of anchor bolts.

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“…As instantaneous frequency reflects the dynamic state of mechanical equipment, so improving the estimation accuracy of IF becomes the core of TLOT. Furthermore, considering the structure of most mechanical parts has spatial symmetry, its fault vibration signal presents essentially cyclostationary in the angle domain, by using order tracking and synchronous averaging, we can convert the non-stationary signal in time domain into cyclostationary signal in angle domain again and establish order cyclostationarity analysis 10 ultimately which is convenient for processing mechanical varying non-stationary signal, such as order spectrum, 11 order cepstrum, 12 envelope order spectrum, 13 order bispectrum, 14 and high-order spectrum. 15 As for time-frequency analysis, considering only from the time domain or frequency domain, we can not obtain the instantaneous time-frequency property which is the core of non-stationary signal processing, 16 the time-frequency analysis provides the joint distribution information in the time domain and frequency domain which can effectively give attention to both time resolution and frequency resolution, it is very suitable for estimating instantaneous frequency and separating mechanical varying non-stationary signal.…”

Section: Introductionmentioning

confidence: 99%

Mechanical varying non-stationary signal separation method based on instantaneous frequency estimation

Yan

Qin-Xiao

Zhong

et al. 2023

Advances in Mechanical Engineering

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Mechanical equipment often works on variable speed condition, its corresponding vibration signal presents multi-component, modulation coupling with fast time-varying instantaneous frequency (IF), how to effectively compute IF and realize fasting varying non-stationary signal decoupling separation plays an important role in mechanical system fault diagnosis. In this paper, a sparse representation method called multi-scale chirp sparse representation (MSCSR) is introduced to identify, extract, and trend IF for achieving a highly concentrated time-frequency energy. Simulation demonstrates that the proposed method performs better than traditional IF estimation method. Furthermore, an adaptive time-varying filter is constructed using the extracted instantaneous frequency to decouple non-stationary fast signal. Ultimately, by rapid instantaneous frequency fluctuation experiment, the effectiveness of proposed method for fast strong time-varying signal is validated, it can effectively extract rapid oscillation instantaneous frequency, and the error is less than 10%.

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Fault Characteristic Extraction by Fractional Lower-Order Bispectrum Methods

Abstract: The generated signals generally contain a large amount of background noise when the mechanical bearing fails, and the fault signals present nonlinear and non-Gaussian feature, which have heavy tail and belong to α -stable distribution ( 1 < α < 2 … Show more

Cited by 5 publications

References 25 publications

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

Escalator Foundation Bolt Loosening Fault Recognition Based on Empirical Wavelet Transform and Multi-Scale Gray-Gradient Co-Occurrence Matrix

Fault Feature Extraction for Anchor Bolt Loosening of Escalator Based on EWT and Bispectrum Analysis

Mechanical varying non-stationary signal separation method based on instantaneous frequency estimation

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