Fault diagnosis for train plug door using weighted fractional wavelet packet decomposition energy entropy

Sun, Yongkui; Cao, Yuan

doi:10.1016/j.aap.2021.106549

Cited by 41 publications

(21 citation statements)

References 43 publications

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“…Similarly, the approximate coefficients cA1 of the first-level decomposition are further decomposed into cA2 and cD2. It is a recursive process until it reach the required decomposition level [13]- [14].…”

Section: Wavelet Transformmentioning

confidence: 99%

Fault diagnosis method of point machine based on vibration signal

Yan,

Zhang,

Sun

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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Section: Wavelet Transformmentioning

confidence: 99%

Fault diagnosis method of point machine based on vibration signal

Yan,

Zhang,

Sun

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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“…Finally, an addition noteworthy to this discussion on fractional derivatives and preprocessing is fractional measures of (signal) complexity, which can be used as a preprocessing step for machine learning applications. This is done in [82], in which the researchers employ a fractional entropy measure to improve the fault detection for train doors.…”

Section: Preprocessingmentioning

confidence: 99%

“…I.e., smallerscaled features, i.e., the data set itself, are not fed into the machine learning algorithm. Further, we recommend using fractional measures of complexity as another fractional calculus-based technique for feature extraction, [82].…”

Section: Renormalization Groupmentioning

confidence: 99%

Combining Fractional Derivatives and Supervised Machine Learning: A Review

Raubitzek¹,

Mallinger²,

Neubauer³

2022

Preprint

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Fractional calculus gained a lot of attention in the last couple of years. Researchers discovered that processes in various fields follow rather fractional dynamics than ordinary integer-ordered dynamics, meaning the corresponding differential equations feature non-integer valued derivatives. There are several arguments for why this is the case, one of them being that fractional derivatives’ inherit spatiotemporal memory and/or the ability to express complex naturally occurring phenomena. Another popular topic nowadays is machine learning, i.e., learning behavior and patterns from historical data. In our ever-changing world with ever-increasing amounts of data, machine learning is a powerful tool for data analysis, problem-solving, modeling, and prediction. It further provides many insights and discoveries in various scientific disciplines. As these two modern-day topics provide a lot of potential for combined approaches to describe complex dynamics, this article reviews combined approaches of fractional derivatives and machine learning from the past, puts them into context, and thus provides a list of possible combined approaches and the corresponding techniques. Note, however, that this article does not deal with neural networks, as there already is profound literature on neural networks and fractional calculus. We sorted past combined approaches from the literature into three categories, i.e., preprocessing, machine learning &amp; fractional dynamics, and optimization. The contributions of fractional derivatives to machine learning are manifold as they provide powerful preprocessing and feature augmentation techniques, can improve physically informed machine learning, and are capable of improving hyperparameter optimization. Thus, this article serves to motivate researchers dealing with data-based problems, to be specific machine learning practitioners, to adopt new tools and enhance their existing approaches.

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“…But as the noise intensifies, the accuracy and speed of the fastener detection algorithm are difficult to meet the detection requirements. Nowadays, deep learning has been widely used in various fields [10] [11]. Therefore, this paper develops a rail fastener detection method based on YOLOV5 which can effectively avoid noise interference and identify the vast majority of damaged rail fasteners.…”

Section: Introductionmentioning

confidence: 99%

Research on abnormal detection method of rail fastener based on YOLOV5

Huang,

Fan,

Sun

2023

Eighth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2023)

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Fault diagnosis for train plug door using weighted fractional wavelet packet decomposition energy entropy

Cited by 41 publications

References 43 publications

Fault diagnosis method of point machine based on vibration signal

Fault diagnosis method of point machine based on vibration signal

Combining Fractional Derivatives and Supervised Machine Learning: A Review

Research on abnormal detection method of rail fastener based on YOLOV5

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