A novel feature extraction method for bearing fault classification with one dimensional ternary patterns

Kuncan, Melih; Kaplan, Kaplan; Mi̇naz, Mehmet Recep; Kaya, Yılmaz; Ertunç, H. Metin

doi:10.1016/j.isatra.2019.11.006

Cited by 71 publications

(32 citation statements)

References 32 publications

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“…Kuncan et al [80] worked on the diagnosis of bearing fault for the rotary machine. They extracted features from the vibration signals of bearing by applying their proposed method called a one-dimensional ternary pattern (1D-TP).…”

Section: B Feature Extraction Methodsmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

613

210

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Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.

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Section: B Feature Extraction Methodsmentioning

confidence: 99%

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Zebari¹,

Abdulazeez²,

Zeebaree³

et al. 2020

JASTT

613

210

View full text Add to dashboard Cite

show abstract

“…Literatürde son dönemlerde arıza teşhisi için birçok yöntem kullanılmaktadır. Özellikle rulman arızalarında kullanılan bu yöntemler oldukça başarı göstermektedir [13]- [16]. Sonlu elemanlar yöntemi bir asenkron motorun mekanik arıza teşhisinde başarı göstermiştir.…”

Section: Arıza Teşhis Teknikleriunclassified

Sonlu Elemanlar Yöntemi ile Fırçasız Doğru Akım Motorunun (BLDC) Kısa Devre Arıza Analizi

Mi̇naz¹,

Yildiz²

2021

DÜMF Mühendislik Dergisi

Self Cite

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“…Zheng et al [44] proposed a fault diagnosis method based on feature extraction and bag-of-words. Kuncan et al [45] proposed a one-dimensional ternary pattern method to extract the features of rolling bearings. Li et al46] proposed a new feature extraction method using EWT and improved MCKD with grid search.…”

Section: Introductionmentioning

confidence: 99%

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

Cui

Guan

Chen³

2021

IEEE Access

137

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In order to improve the diagnosis accuracy and solve the weak fault signal of rolling element of rolling bearings due to long transmission path, a novel fault diagnosis method based on variational mode decomposition (VMD) and maximum correlation kurtosis deconvolution (MCKD), namely VMD-MCKD-FD is proposed for rolling elements of rolling bearings in this paper. In the proposed VMD-MCKD-FD, the vibration signal of rolling element of rolling bearings is decomposed into a series of Intrinsic Mode Functions (IMFs) by using VMD method. Then the number of modes with outstanding fault information is determined by Kurtosis criterion in order to calculate the deconvolution period T. The periodic fault component of reconstructed signal is enhanced by using sensitivity MCKD method. Finally, the power spectrum of the reconstructed signal is analyzed in detail in order to obtain the fault frequency and diagnose the rolling element fault of rolling bearings. The simulation signal and actual vibration signal are selected to verify the effectiveness of the VMD-MCKD-FD method. The experimental results show that the VMD-MCKD-FD method can effectively diagnose the rolling element fault of rolling bearings and obtain better fault accuracy.

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A novel feature extraction method for bearing fault classification with one dimensional ternary patterns

Cited by 71 publications

References 32 publications

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

Sonlu Elemanlar Yöntemi ile Fırçasız Doğru Akım Motorunun (BLDC) Kısa Devre Arıza Analizi

Rolling Element Fault Diagnosis Based on VMD and Sensitivity MCKD

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