Multi-source feature extraction of rolling bearing compression measurement signal based on independent component analysis

Li, Jing; Meng, Zong; Yin, Na; Zhang, Pan; Cao, Lixiao; Fan, Fengjie

doi:10.1016/j.measurement.2020.108908

“…The extracted components of ICA are statistically independent, this property distinguishes ICA from other methods [19]. Recently, ICA feature extraction method gain attention as effective genes reduction technique of microarray data for NB classifier [20,21]. The embedded algorithm of the NB classifier based on conditional independence hypothesis, ICA technique resolved successfully this condition as the ICA extracted components (features) are statistically independent.…”

Section: Introductionmentioning

confidence: 99%

Application of nature inspired soft computing techniques for gene selection: a novel frame work for classification of cancer

Aziz

¹

2022

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A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is applied for cancer classification, that reduces the classifier's prediction errors and allows for faster convergence by selecting informative genes. Cuckoo search (CS) algorithm was used in the onlooker bee phase (exploitation phase)of ABC to boost performance by maintaining the balance between exploration and exploitation of ABC. Tuned the modified ABC algorithm by using Naïve Bayes (NB) classifiers to improve the further accuracy of the model. Independent Component Analysis (ICA) is used for dimensionality reduction. In the first step, the reduced dataset is optimized by using Modified ABC and after that, in the second step, the optimized dataset is used to train the NB classifier. Extensive experiments were performed for comprehensive comparative analysis of the proposed algorithm with well-known metaheuristic algorithms, namely Genetic Algorithm (GA) when used with the same framework for the classification of six high-dimensional cancer datasets. The comparison results showed that the proposed model with the CS algorithm achieves the highest performance as maximum classification accuracy with less count of selected genes. This shows the effectiveness of the proposed algorithm which is validated using ANOVA for cancer classification.

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“…The extracted components of ICA are statistically independent, this property distinguishes ICA from other methods [19]. Recently, ICA feature extraction method gain attention as effective genes reduction technique of microarray data for NB classifier [20,21]. The embedded algorithm of the NB classifier based on conditional independence hypothesis, ICA technique resolved successfully this condition as the ICA extracted components (features) are statistically independent.…”

Section: Introductionmentioning

confidence: 99%

Application of Nature Inspired Soft Computing Techniques for Gene Selection: A Novel Frame Work for Classification of Cancer.

Aziz

¹

2022

Preprint

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A modified Artificial Bee Colony (ABC) metaheuristics optimization technique is applied for cancer classification, that reduces the classifier's prediction errors and allows for faster convergence by selecting informative genes. Cuckoo search (CS) algorithm was used in the onlooker bee phase (exploitation phase)of ABC to boost performance by maintaining the balance between exploration and exploitation of ABC. Tuned the modified ABC algorithm by using Naïve Bayes (NB) classifiers to improve the further accuracy of the model. Independent Component Analysis (ICA) is used for dimensionality reduction. In the first step, the reduced dataset is optimized by using Modified ABC and after that, in the second step, the optimized dataset is used to train the NB classifier. Extensive experiments were performed for comprehensive comparative analysis of the proposed algorithm with well-known metaheuristic algorithms, namely Genetic Algorithm (GA) when used with the same framework for the classification of six high-dimensional cancer datasets. The comparison results showed that the proposed model with the CS algorithm achieves the highest performance as maximum classification accuracy with less count of selected genes. This shows the effectiveness of the proposed algorithm which is validated using ANOVA for cancer classification.

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“…e health state of rotating parts directly a ects the operation reliability of the whole mechanical system [1][2][3]. Once the rotating parts fail, it will cause serious accidents.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery

Li

¹

,

Riaz

²

,

Muhammad

³

et al. 2022

Shock and Vibration

0

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As the most basic component of rotating machinery, rolling bearing frequently works in harsh environments and complex working conditions, and its health status affects seriously the working efficiency. The health statuses of rolling bearing can not only reduce equipment maintenance costs but also contribute to reducing major accidents. Based on this, an adaptive diagnosis method that combines deep gated recurrent unit (DGRU) with wavelet packet decomposition (WPD) and extreme learning machine (ELM) is proposed for rolling bearing. Firstly, WPD is utilized to eliminate the noise of data. Secondly, DGRU is designed to extract the representative features of denoised data. Finally, ELM is utilized to output the diagnosis results. Massive results prove that the superiority and robustness of our approach outperform existing popular methods. Additionally, the proposed method can also achieve powerful antinoise ability.

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Multi-source feature extraction of rolling bearing compression measurement signal based on independent component analysis

Cited by 34 publications

References 35 publications

Application of nature inspired soft computing techniques for gene selection: a novel frame work for classification of cancer

Application of nature inspired soft computing techniques for gene selection: a novel frame work for classification of cancer

Application of Nature Inspired Soft Computing Techniques for Gene Selection: A Novel Frame Work for Classification of Cancer.

An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery

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