An Adaptive Randomized Orthogonal Matching Pursuit Algorithm With Sliding Window for Rolling Bearing Fault Diagnosis

Li, Jimeng; Li, Ming; Yao, Xifeng; Wang, Hui

doi:10.1109/access.2018.2855732

Cited by 11 publications

(6 citation statements)

References 26 publications

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“…In order to effectively identify the feature information contained in the fault signal of rotating machinery and reveal its inherent characteristics, many fault feature extraction methods of rotating machinery have been proposed, such as empirical mode decomposition (EMD) [7,8], mathematical morphology filtering [9,10], wavelet decomposition [11,12], adaptive filtering [13,14], matching pursuit [15,16], cyclostationary signal analysis [17,18], Wiener filter [19], Kalman filter [20,21], and stochastic resonance [22,23] that are widely used in early fault diagnosis of rotating machinery. The EMD proposed by Huang et al [7] is a nonstationary signal analysis method, which can find the hidden characteristic information in the signal, and has been widely used in the extraction and noise reduction of the impact signal of rotating machinery.…”

Section: Introductionmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

International Journal of Rotating Machinery

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The vibration signal of rotating machinery compound faults acquired in actual fields has the characteristics of complex noise sources, the strong background noise, and the nonlinearity, causing the traditional blind source separation algorithm not be suitable for the blind separation of rotating machinery coupling fault. According to these problems, an extraction method of multisource fault signals based on wavelet packet analysis (WPA) and fast independent component analysis (FastICA) was proposed. Firstly, according to the characteristic of the vibration signal of rotating machinery, an effective denoising method of wavelet packet based on average threshold is presented and described to reduce the vibration signal noise. In the method, the thresholds of every node of the best wavelet packet basis are acquired and averaged, and then the average value is used as a global threshold to quantize the decomposition coefficient of every node. Secondly, the mixed signals were separated by using the improved FastICA algorithm. Finally, the results of simulations and real rotating machinery vibration signals analysis show that the method can extract the rotating machinery fault characteristics, verifying the effectiveness of the proposed algorithm.

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Section: Introductionmentioning

confidence: 99%

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Miao

Zhao

Jia

et al. 2021

International Journal of Rotating Machinery

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“…One of the ways to solve this inverse problem is to first establish the representation 'a' and then update the 'D', as used by sparse coding approaches for anomaly and target detection applications [1], sub-sampling with k-means classification in [2] and with spectral angle measurements in [3], to the popular generalized k-means or K-SVD [4] algorithm. The estimation of 'a' is either done through convex relaxation approaches minimizing for ℓ p norm (p > 0, most algorithms follow p = 2) [5,6], or through greedy methods like the popular Orthogonal Matching Pursuit (OMP) with some recent uses in [7,8,9,10].…”

Section: Introductionmentioning

confidence: 99%

Rapid Estimation of Orthogonal Matching Pursuit Representation

Chatterjee

Yuen

2020

IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

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Orthogonal Matching Pursuit (OMP) has proven itself to be a significant algorithm in image and signal processing domain in the last decade to estimate sparse representations in dictionary learning. Over the years, efforts to speed up the OMP algorithm for the same accuracy has been through variants like generalized OMP (g-OMP) and fast OMP (f-OMP). All of these algorithms solve OMP recursively for each signal sample among 'S' number of samples. The proposed rapid OMP (r-OMP) runs the loop for 'N' atoms, simultaneously estimating for all samples, and, in a real scene since N ≪ S, the proposed approach speeds up OMP by several orders of magnitude. Experiment on a real scene with a popular dictionary learning algorithm, K-SVD, show that the proposed rOMP completes K-SVD in ≈4% of the computational time compared to using OMP.

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“…The associate editor coordinating the review of this manuscript and approving it for publication was Youqing Wang. decomposition (EMD) [8]- [10], variational mode decomposition (VMD) [11]- [13], blind source separation [14]- [15], matching pursuit based methods [16]- [18], deep learning based methods [19]- [21]. However, these methods are unavailable for time-varying rotation speed operations.…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

Wang

Xiang

2019

IEEE Access

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Conventional order tracking, which relies on a reference signal, is a common tool for rotary machinery fault diagnosis under speed fluctuation working conditions. However, it is inconvenient to install a speed sensor under certain circumstances. In this paper, we present a two-stage method to detect variable speed rolling bearing faults without a tachometer. In the first stage, the spline-kernelled chirplet transform (SCT) is applied to calculate the time-frequency distribution and extract the instantaneous rotation frequency. In the second stage, angle synchronous averaging is employed to resample the raw signal and detect fault features. The comparison investigations between SCT and other time-frequency analysis methods are performed by a numerical signal with a nonlinear instantaneous frequency. The experimental studies of variable speed rolling bearings with the outer and inner race faults are further performed. The results demonstrate that the proposed method can well detect rolling bearing faults without a key-phase signal from strong background noise under varying speed conditions. INDEX TERMS General parameterized time-frequency analysis methods, spline-kernelled chirplet transform, angle synchronous averaging, variable speed, bearing fault detection.

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An Adaptive Randomized Orthogonal Matching Pursuit Algorithm With Sliding Window for Rolling Bearing Fault Diagnosis

Cited by 11 publications

References 26 publications

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Multisource Fault Signal Separation of Rotating Machinery Based on Wavelet Packet and Fast Independent Component Analysis

Rapid Estimation of Orthogonal Matching Pursuit Representation

A Two-Stage Method Using Spline-Kernelled Chirplet Transform and Angle Synchronous Averaging to Detect Faults at Variable Speed

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