Novel morphological scale difference filter with application in localization diagnosis of outer raceway defect in rolling bearings

Huang, Jinfeng; Cui, Lingli; Zhang, Jianyu

doi:10.1016/j.mechmachtheory.2023.105288

Cited by 29 publications

(16 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are many denoising algorithms available, such as Wavelet, Morphological Filtering [ 33 ], and Matrix Completion [ 34 , 35 ]. To keep the spectral structure of the raw signal, we select NLmeans as the pre-treatment method.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

Yang

Zhou

et al. 2023

Entropy

View full text Add to dashboard Cite

As is well-known, ship-radiated noise (SN) signals, which contain a large number of ship operating characteristics and condition information, are widely used in ship recognition and classification. However, it is still a great challenge to extract weak operating characteristics from SN signals because of heavy noise and non-stationarity. Therefore, a new mono-component extraction method is proposed in this paper for taxonomic purposes. First, the non-local means algorithm (NLmeans) is proposed to denoise SN signals without destroying its time-frequency structure. Second, adaptive chirp mode decomposition (ACMD) is modified and applied on denoised signals to adaptively extract mono-component modes. Finally, sub-signals are selected based on spectral kurtosis (SK) and then analyzed for ship recognition and classification. A simulation experiment and two application cases are used to verify the effectiveness of the proposed method and the results show its outstanding performance.

show abstract

Section: The Proposed Methodsmentioning

confidence: 99%

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

Yang

Zhou

et al. 2023

Entropy

View full text Add to dashboard Cite

show abstract

“…Vibration signals from gearboxes are rich in state information, making vibration-based signal processing methods widely used in intelligent O&M and health management of mechanical systems [9][10][11][12][13][14]. The key to gearbox fault diagnosis is feature extraction.…”

Section: Introductionmentioning

confidence: 99%

Multivariate multi-scale cross-fuzzy entropy and SSA-SVM-based fault diagnosis method of gearbox

Hou,

Zheng,

Pan

et al. 2024

Meas. Sci. Technol.

View full text Add to dashboard Cite

Fuzzy entropy (FuzzyEn) is widely recognized as a powerful tool for analyzing nonlinear dynamics and measuring the complexity of time series data. It has been utilized as an effective indicator to capture nonlinear fault features in gearbox vibration signals. However, FuzzyEn only measures complexity at a single scale, ignoring the valuable information contained in large-scale features of the time series. Furthermore, FuzzyEn does not account for coupling characteristics between related or synchronized time series. To address these limitations, a novel entropy-based approach called multivariate multi-scale cross-fuzzy entropy (MvMCFE) is proposed in this paper for measuring the complexity and mutual predictability of two multivariate time series. Relying on the advantages of MvMCFE in nonlinear feature extraction, a new fault diagnosis method for gearboxes is proposed based on MvMCFE and an optimized support vector machine (SVM) using the salp swarm algorithm (SSA-SVM). Ultimately, the proposed gearbox diagnostic method is employed to analyze the gearbox experimental data and a comparison with existing fault diagnosis approaches is conducted. The comparison results indicate that the proposed method can effectively extract nonlinear fault features of gearboxes and achieve the highest recognition rate compared to the other methods.

show abstract

“…However, external noise interference and * Author to whom any correspondence should be addressed. internal structural complexity present significant challenges for diagnosing faults in actual working conditions [3,4], and effective fault diagnosis and identification methods need to be developed [5].…”

Section: Introductionmentioning

confidence: 99%

Non-negative wavelet matrix factorization-based bearing fault intelligent classification method

Dong

Zhao

Cui

2023

Meas. Sci. Technol.

Self Cite

View full text Add to dashboard Cite

With the increasing number of classifications in bearing fault diagnosis, it is difficult to achieve accurate results based on the original signal or time-frequency features calculated by the time-frequency analysis method, such as the continuous wavelet transform. Aiming at this issue, a time-frequency joint metric features extraction technique named non-negative wavelet matrix factorization (NWMF) is developed to apply in bearing fault intelligent classification. In the proposed technique, the NWMF is used to calculate bearing fault-related internal core information from the original time-frequency spectrum, as a result, the time-frequency characteristic dimension decrease, and the computational efficiency increase. In addition, a novel convolutional neural network model is developed to identify locations and sizes of fault bearing based on the calculated internal core information. For verifying the effectiveness of the proposed method, the types of bearing faults in the experiments are set up to fifteen, the results and comparative analysis reveal that the feasibility and superiority of the proposed method are much better than the other traditional machine learning methods and original deep learning methods, such as the support vector machine, random forest and residual neural network.

show abstract

Novel morphological scale difference filter with application in localization diagnosis of outer raceway defect in rolling bearings

Cited by 29 publications

References 33 publications

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

A Novel Underwater Acoustic Target Identification Method Based on Spectral Characteristic Extraction via Modified Adaptive Chirp Mode Decomposition

Multivariate multi-scale cross-fuzzy entropy and SSA-SVM-based fault diagnosis method of gearbox

Non-negative wavelet matrix factorization-based bearing fault intelligent classification method

Contact Info

Product

Resources

About