Underdetermined Source Separation of Bearing Faults Based on Optimized Intrinsic Characteristic-Scale Decomposition and Local Non-Negative Matrix Factorization

Hao, Yansong; Song, Liuyang; Wang, Mengyang; Cui, Lingli

doi:10.1109/access.2019.2892559

Cited by 38 publications

(18 citation statements)

References 27 publications

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“…The three-dimensional geometric features-based SCA algorithm was used for compound faults diagnosis of roller bearing. A similar topic was discussed in [4] where NMF was used to extract error signals. The conducted experiments confirmed the effectiveness of these methods in extract the fault features and diagnosis for roller bearing.…”

Section: Introductionmentioning

confidence: 95%

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Lie Group Methods in Blind Signal Processing

Mika

Jóźwik

2020

Sensors

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This paper deals with the use of Lie group methods to solve optimization problems in blind signal processing (BSP), including Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). The paper presents the theoretical fundamentals of Lie groups and Lie algebra, the geometry of problems in BSP as well as the basic ideas of optimization techniques based on Lie groups. Optimization algorithms based on the properties of Lie groups are characterized by the fact that during optimization motion, they ensure permanent bonding with a search space. This property is extremely significant in terms of the stability and dynamics of optimization algorithms. The specific geometry of problems such as ICA and ISA along with the search space homogeneity enable the use of optimization techniques based on the properties of the Lie groups O ( n ) and S O ( n ) . An interesting idea is that of optimization motion in one-parameter commutative subalgebras and toral subalgebras that ensure low computational complexity and high-speed algorithms.

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Section: Introductionmentioning

confidence: 95%

“…+ algorithm SD on the Euclidean space with penalty function(3). o non-geodesic algorithm SD on Riemannian space(4).Sensors 2020, 20, x FOR PEER REVIEW 10 of . In Algorithm (3) we used the Lagrange multiplier method.…”

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confidence: 99%

Lie Group Methods in Blind Signal Processing

Mika

Jóźwik

2020

Sensors

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“…However, the sound measurement [32], temperature measurement [33], and magnetic field analysis [34] have been used less due to the inference of external factors. Among them, vibration signature analysis has become an effective tool in the field of condition monitoring and fault diagnosis of rotating machinery [35]. Also, the vibration signature of the motor under different loads is easy to measure and can provide rich dynamic information reflecting BRB health status.…”

Section: Introductionmentioning

confidence: 99%

An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors

Zhen

Wang

et al. 2019

Applied Sciences

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Induction motors (IMs) are widely used in many manufacturing processes and industrial applications. The harsh work environment, long-time enduring, and overloads mean that it is subjected to broken rotor bar (BRB) faults. The vibration signal of IMs with BRB faults consists of the reliable modulation information used for fault diagnosis. Cyclostationary analysis has been found to be effective in identifying and extracting fault feature. The estimators of cyclic modulation spectrum (CMS) and fast spectral correlation (FSC) based on the short-time fourier transform (STFT) have higher cyclic frequency resolution, which has proven efficient in demodulating second order cyclostationary (CS2) signals. However, these two estimators have limitations of processing the maximum cyclic frequency αmax that is smaller than Fs/2 (Fs is the sampling frequency) according to Nyquist’s Theorem. In addition, they have lower carrier frequency resolution due to the fixed window size used in STFT. In order to resolve the initial shortcomings of the CMS and FSC methods, in this paper, we extended the analysis of CMS algorithm based on the continuous wavelet transform (CWT), which enlarged the maximum cyclic frequency range to Fs/2 and provides higher carrier frequency resolution because the CWT has the advantage of multi-resolution analysis. The reliability and applicability of the proposed method for fault components localization were validated by CS2 simulation signals. Compared to CMS and FSC methods, the proposed approach shows better performance by analyzing vibration signals between healthy motor and faulty motor with one BRB fault under 0%, 20%, 40%, and 80% load conditions.

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“…In order to solve the problem issued above and improve the monitoring level of rolling bearings' running state, some methods have been proposed for compound faults diagnosis, such as demodulation algorithm, variational mode decomposition, clustering algorithm, and blind source separation technique [23][24][25][26]. Reference [27] proposed a method of combining wavelet analysis with blind source separation for roller bearing compound faults separation that needs multiple signal channels to analyze.…”

Section: Introductionmentioning

confidence: 99%

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

Cui

Yang

et al. 2019

Applied Sciences

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Vibration analysis is one of the main effective ways for rolling bearing fault diagnosis, and achallenge is how to accurately separate the inner and outer race fault features from noisy compoundfaults signals. Therefore, a novel compound fault separation algorithm based on parallel dual-Qfactorsand improved maximum correlation kurtosis deconvolution (IMCKD) is proposed. First, thecompound fault signal is sparse-decomposed by the parallel dual-Q-factor, and the low-resonancecomponents of the signal (compound fault impact component and small amount of noise) are obtained,but it can only highlight the impact of compound faults, and failed to separate the inner and outerrace compound fault signal. Then, the MCKD is improved (IMCKD) by optimizing the selection ofparameters (the shift order M and the filter length L) based on the iterative calculation method withthe Teager envelope spectral kurtosis (TEK) index. Finally, after the composite fault signal is filteredand de-noised by the proposed method, the inner and outer race fault signals are obtained respectively.The fault characteristic frequency is consistent with the theoretical calculation value. The results showthat the proposed method can efficiently separate the mixed fault information and avoid the mutualinterference between the components of the compound fault.

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Underdetermined Source Separation of Bearing Faults Based on Optimized Intrinsic Characteristic-Scale Decomposition and Local Non-Negative Matrix Factorization

Cited by 38 publications

References 27 publications

Lie Group Methods in Blind Signal Processing

Lie Group Methods in Blind Signal Processing

An Improved Cyclic Modulation Spectral Analysis Based on the CWT and Its Application on Broken Rotor Bar Fault Diagnosis for Induction Motors

Compound Faults Feature Extraction for Rolling Bearings Based on Parallel Dual-Q-Factors and the Improved Maximum Correlated Kurtosis Deconvolution

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