An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

Kuncan, Melih

doi:10.1109/access.2020.3011980

Cited by 83 publications

(51 citation statements)

References 43 publications

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“…Different graph modeling-based approaches are also frequently proposed and implemented. Some worthy mentions are [7][8][9]20,[37][38][39][40][41].…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Fault Detection for Smart Manufacturing

Neupane¹,

Kim

Seok

et al. 2021

Applied Sciences

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A smart factory is a highly digitized and networked production facility based on smart manufacturing. A smart manufacturing plant is the result of intelligent systems deployed in the factory. Smart factories have higher production volumes and are prone to machine failures when operating in almost all applications on a daily basis. With the growing concept of smart manufacturing required for Industry 4.0, intelligent methods for detecting and classifying bearing faults have become a subject of scientific research and interest. In this paper, a deep learning-based 1-D convolutional neural network is proposed using the time-sequence bearing data from the Case Western Reserve University (CWRU) bearing database. Four different sets of data are used. The proposed method achieves state-of-the-art accuracy even with a small amount of training data. For the sensitivity analysis of the proposed method, metrics such as precision, recall, and f-measure are determined. Next, we compare the proposed method with a 2-D CNN that uses two-dimensional image illustrations of raw data as input. This method shows the effectiveness of using 1-D CNNs over 2-D CNNs for time-sequence data. The proposed method is computationally inexpensive and outperforms the most complex and computationally intensive algorithms used for bearing fault detection and diagnosis.

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“…Different graph modeling-based approaches are also frequently proposed and implemented. Some worthy mentions are [7][8][9]20,[37][38][39][40][41].…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Fault Detection for Smart Manufacturing

Neupane¹,

Kim

Seok

et al. 2021

Applied Sciences

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“…At present, many scholars use different methods to study the vibration signal of fault bearing [5][6][7][8][9][10][11]. In the field of fault diagnosis, some scholars study and apply VMD.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

Shen

Wang

et al. 2021

Mathematical Problems in Engineering

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Variational modal decomposition (VMD) has the end effect, which makes it difficult to efficiently obtain fault eigenvalues from rolling bearing fault signals. Inspired by the mirror extension, an improved VMD is proposed. This method combines VMD and mirror extension. The mirror extension is a basic algorithm to inhibit the end effect. A comparison is made with empirical mode decomposition (EMD) for fault diagnosis. Experiments show that the improved VMD outperforms EMD in extracting the fault eigenvalues. The performance of the new algorithm is proven to be effective in real-life mechanical fault diagnosis. Furthermore, in this article, combining with singular value decomposition (SVD), fault eigenvalues are extracted. In this way, fault classification is realized by K-nearest neighbor (KNN). Compared with EMD, the proposed approach has advantages in the recognition rate, which can accurately identify fault types.

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“…In a study conducted with the texture analysis method, it was observed that high success was achieved in the detection of bearing fault. [30]- [35]. In a wind turbine application, fault detection and classification were made by using the texture analysis method.…”

Section: Introductionmentioning

confidence: 99%

An Effective Method for Detection of Demagnetization Fault in Axial Flux Coreless PMSG With Texture-Based Analysis

Mi̇naz

Akcan

2021

IEEE Access

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Due to its high power densities and compact dimensions, the axial flux coreless permanent magnet synchronous generator (PMSG) is used in a wide range of areas such as wind turbines and electric vehicles. It is extremely important to detect magnetization faults that occur in these generators. The occurrence of such faults in these machines with a wide range of areas of use affects their operation negatively. In this study, an effective method has been proposed to detect the demagnetization fault occurring in axial flux coreless PMSGs. The relevant method proposes an effective texture analysis-based feature extraction method, which is an original method in contrast to conventional methods used in the literature. It has been revealed that it is a method that can be used instead of conventional methods such as time-frequency analysis, frequency spectrum analysis, and motor current signature analysis (MCSA) methods. Using the finite element method, current and voltage signals were taken from the healthy and axial flux coreless PMSG with 3% and 6% demagnetization fault. Besides, these signals were retaken at different speeds and loads. After the signals were converted into images, using the features obtained from the images with LBP, fault diagnosis processes were carried out with Knn. It was tested both at different fault rates and under different load and speed conditions to test whether the proposed method worked properly. The success rate of this method was observed as 97.16% and 100%. With the proposed method, it has been revealed that the demagnetization fault can be detected in axial flux coreless PMSGs.INDEX TERMS Image texture analysis, demagnetization, fault detection, permanent magnet machines.

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An Intelligent Approach for Bearing Fault Diagnosis: Combination of 1D-LBP and GRA

Cited by 83 publications

References 43 publications

CNN-Based Fault Detection for Smart Manufacturing

CNN-Based Fault Detection for Smart Manufacturing

Fault Diagnosis of Rolling Bearing Based on Improved VMD and KNN

An Effective Method for Detection of Demagnetization Fault in Axial Flux Coreless PMSG With Texture-Based Analysis

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