Locality structure preserving based feature selection for prognostics

Yu, Peng; Xu, Yong; Liu, Datong; Li, Junbao

doi:10.3233/ida-150737

Cited by 2 publications

(4 citation statements)

References 29 publications

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“…Feature extraction and selection processes affect the data structure. The change due to feature selection process has been controlled through preserving the local and global structure of the data in order to achieve effective feature selection (Peng, Xu, Liu, & Li, 2015;Wang, Zhang, Yin, & Liu, 2014). A feature selection method that aims to preserve the local and global structure has been presented in (Wang et al, 2014), and its application in prognostics has been discussed in (Peng et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The change due to feature selection process has been controlled through preserving the local and global structure of the data in order to achieve effective feature selection (Peng, Xu, Liu, & Li, 2015;Wang, Zhang, Yin, & Liu, 2014). A feature selection method that aims to preserve the local and global structure has been presented in (Wang et al, 2014), and its application in prognostics has been discussed in (Peng et al, 2015). A novel diagnostics feature extraction methodology was proposed in (Gelman, Patel, Persin, Murray, & Thomson, 2013) for bearing fault detection based on combination of frequency-domain based techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

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Segmentation Based Feature Evaluation and Fusion for Prognostics

Atamuradov

Camci

2020

IJPHM

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Quantification of feature goodness, called feature evaluation, is crucial in the identification of best features and achieving high accuracy in diagnostics and prognostics. Even though feature evaluation for diagnostics is a mature area, it is a developing research area for prognostics. The feature goodness for prognostics is measured by change in degradation. Most, if not all, of existing methods, analyze the feature change in the whole failure degradation. In other words, features collected throughout the failure degradation are analyzed to create a goodness value for the feature. In reality, the goodness of the features may change during the failure progression. A feature may be a good representative of failure progression in the initial phase but not in the final phases, or vice versa. This paper presents a methodology that divides the features into segments, each of which may have different goodness for prognostics. Thus, some part of the feature may be good, whereas the others not. The presented approach leads to extract more value from the features’ changing properties during the failure degradation. The method has been applied to simulated and real datasets obtained from Li-ion batteries aging tests. State of health (SoH) estimation accuracy is enhanced with the presented approach.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Segmentation Based Feature Evaluation and Fusion for Prognostics

Atamuradov

Camci

2020

IJPHM

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“…The structure of the data may change as a result of the feature selection due removed features. This change has been controlled through preserving the local and global structure of the data in order to achieve effective feature selection (Wang et al 2014) (Peng et al 2015). Feature selection Figure 1.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Feature selection Figure 1. Features with different failure progression method that aims to preserve the local and global structure has been presented in (Wang et al 2014), and its application in prognostics has been discussed in (Peng et al 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Features with Changing Effectiveness for Prognostics

Atamuradov

Camci

2016

PHM_CONF

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Feature evaluation is crucial to identify the best features and to achieve high accuracy in diagnostics and prognostics. Feature evaluation for prognostics is a developing research area with several publications in recent years. Most, if not all, of existing methods to evaluate features for prognostics base on the feature changes in the whole life of the system under observation. In other words, feature values collected throughout the failure degradation are analyzed to create a goodness value for the feature. In reality, the goodness of the features may change during the failure progression. A feature may be good representative of failure progression in the initial phase but not in the final phases, or vice versa. This paper presents dynamic nature of representation capabilities of features throughout the failure degradation and proposes a novel approach to evaluate the features considering their dynamic nature. Proposed approach involves feature segmentation based on their representation capabilities and feature fusion utilizing the segmented evaluations. The presented approach has been applied in simulated and real degradation datasets. Real degradation dataset were obtained from accelerated aging tests of Li-ion batteries in the lab environment. The results from both datasets show that dynamic feature evaluation improves SoH estimation accuracy.

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Locality structure preserving based feature selection for prognostics

Cited by 2 publications

References 29 publications

Segmentation Based Feature Evaluation and Fusion for Prognostics

Segmentation Based Feature Evaluation and Fusion for Prognostics

Evaluation of Features with Changing Effectiveness for Prognostics

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