An Investigation of Fault Detection Techniques in Rolling Element Bearing

Sahu, Devendra; Dewangan, Ritesh Kumar; Matharu, Surendra Pal Singh

doi:10.1007/s42417-023-01202-1

Cited by 4 publications

(6 citation statements)

References 81 publications

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“…always beneficial to minimize the processing time in each and every time step [30]. Therefore, it is important to further reduce the processing time while maintaining high accuracy in data resampling for synchronous analysis.…”

Section: Basic Principles Of Synchronous Resamplingmentioning

confidence: 99%

“…In this example, we used the order range of [5,30], which approximately includes the first three orders of the outer race damage feature, excluding the influence of shaft order responses. The calculated results are listed in Table 4.…”

Section: Extraction Of Bearing Outer Race Spalling Damage Featuresmentioning

confidence: 99%

“…As shown in Table 4, the results based on synchronous sampling had higher 𝐸 value, which is consistent with the visual observations from the spectra as shown in Figure 10c,d. In this example, we used the order range of [5,30], which approximately includes the first three orders of the outer race damage feature, excluding the influence of shaft order responses. The calculated results are listed in Table 4.…”

Section: Extraction Of Bearing Outer Race Spalling Damage Featuresmentioning

confidence: 99%

“…Any uneconomical processing steps may prevent the application of the process to the entire time range. On the other hand, in quasi real-time analysis, it is always beneficial to minimize the processing time in each and every time step [30]. Therefore, it is important to further reduce the processing time while maintaining high accuracy in data resampling for synchronous analysis.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

Wang,

Huang,

Zhang

et al. 2024

Machines

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Synchronous analysis is one of the most effective and practical techniques in rotating machinery diagnostics, especially in cases with variable speed operations. A modern analog-to-digital convertor (ADC) usually digitizes an analog signal to an equal time interval data series. Synchronous resampling converts the data series from an equal time interval data series to an equal shaft rotation angle interval data series. This conversion is usually achieved in the digital domain with the aid of shaft speed information, through either direct measurement or identification from a measured vibration signal, which is a time-consuming process. In order to improve the computational efficiency as well as the data processing accuracy, in this paper, a fast synchronous time-point calculation method based on an inverse function interpolation procedure is proposed. By identifying the inverse function of the instantaneous phase with respect to time, the calculation process of synchronous time points is optimized, which results in improved calculation efficiency and accuracy. These advantages are demonstrated by numerical simulations as well as experimental verifications. The numerical simulation results show that the proposed method can improve calculation speed by about five times. The synchronous analysis based on the proposed method was applied to a bearing fault detection in a high-speed rail carriage, which demonstrated the advantages of the proposed algorithm in improving the signal-to-noise ratio (SNR) for bearing damage feature extraction.

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Section: Basic Principles Of Synchronous Resamplingmentioning

confidence: 99%

Section: Extraction Of Bearing Outer Race Spalling Damage Featuresmentioning

confidence: 99%

Section: Extraction Of Bearing Outer Race Spalling Damage Featuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

Wang,

Huang,

Zhang

et al. 2024

Machines

View full text Add to dashboard Cite

show abstract

“…* Author to whom any correspondence should be addressed. operation of production [1]. However, in the complex and variable processes of production, assembly, and transportation, bearing defects have become one of the core issues urgently needing resolution in the chemical manufacturing industry.…”

Section: Introductionmentioning

confidence: 99%

CAC-YOLOv8: real-time bearing defect detection based on channel attenuation and expanded receptive field strategy

Liu,

Zhao,

Chen

et al. 2024

Meas. Sci. Technol.

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Bearing defect detection plays a crucial role in the intelligent production of chemical transmission equipment, where timely identification and handling of defective bearings are essential. However, in practical large-scale industrial production, product surface defects are often complex, diverse, and exhibit significant variations in appearance, posing severe challenges to the discriminative ability and detection efficiency of bearing defect detection algorithms. This paper proposes a real-time bearing surface defect detection algorithm, CAC-YOLOv8, which designs the Channel Attenuation Network (CAN) and Compound Pooling Pyramid Spatial Pyramid Pooling Fast (CPPSPPF) structure. Specifically, the model introduces the Channel Attenuation Network to achieve parallel feature extraction, deep feature processing, and feature fusion under different channel numbers, capturing critical features related to bearing defects and thereby improving computational efficiency. Subsequently, based on the concept of overlapped receptive fields, a CPPSPPF structure is constructed, utilizing multiple iterations of max-pooling operations with smaller pooling kernel sizes to prevent information loss while expanding the receptive field, thereby strengthening the capturing ability of features at different scales. The experimental results indicate that the proposed CAC-YOLOv8 bearing surface defect detection algorithm, compared to the YOLOv8 model, achieved a 0.3% improvement in mAP@0.5, reduced model size by 14.4%, and enhanced model inference speed by 33.3%. This enables the CAC-YOLOv8 model to significantly improve the real-time performance of bearing defect detection while maintaining high-precision detection. The performance in practical industrial detection demonstrates that the proposed approach has achieved outstanding results in both speed and accuracy.

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A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

Hu,

Liu,

Zhao

et al. 2024

Meas. Sci. Technol.

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Recently, deep learning has received widespread attention in the field of bearing fault diagnosis due to its powerful feature learning capability. However, when the actual working conditions are complex and variable, the fault information in a single domain is limited, making it difficult to achieve high accuracy. To overcome these challenges, this paper proposes a bearing fault diagnosis method based on the Markov transition field, continuous wavelet transform (CWT), and dual-channel convolutional neural network (CNN). The method combines the descriptive ability of the Markov model for state transfer, the time-frequency analysis ability of CWT for signal, and the excellent performance of CNN with attention mechanism in feature extraction and classification. Specifically, we first propose a multi-channel Markov transition field method, which is combined with CWT to obtain two different representations of two-dimensional (2D) images. To comprehensively mine fault information, we further propose a dual-channel CNN with an attention mechanism. The design of this network structure aims to extract multi-level features from two types of 2D images. At the same time, we designed and embedded an attention mechanism to enable the network to focus more on extracting effective features, thereby improving the performance and accuracy of the network. To verify the effectiveness of the proposed method, three datasets were used for empirical research. The results show that this method exhibits superior performance in bearing fault diagnosis and has higher accuracy compared to traditional methods.

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An Investigation of Fault Detection Techniques in Rolling Element Bearing

Cited by 4 publications

References 81 publications

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

Improved Synchronous Sampling and Its Application in High-Speed Railway Bearing Damage Detection

CAC-YOLOv8: real-time bearing defect detection based on channel attenuation and expanded receptive field strategy

A new dual-channel convolutional neural network and its application in rolling bearing fault diagnosis

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