Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

Yu, Shengyong; Ma, Jing

doi:10.3390/pr10122532

Cited by 4 publications

(2 citation statements)

References 28 publications

(40 reference statements)

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“…depending on a scale a > 0 a ∈ R + and translocational value b(b ∈ R). Discrete wavelet transform (DWT) carries with it a similar idea, with the difference that parameters a and b are discrete: a = (a 0 ) n , b = kb 0 (7) Though CWT and DWT have much in common, they are usually used for different purposes. While DWT is a perfect instrument for such coding problems as image compression, CWT is mostly applied for signal analysis tasks.…”

Section: Continuous Wavelet Transformmentioning

confidence: 99%

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Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

Xie,

Tang,

Liu

et al. 2023

Processes

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Most disconnector fault diagnosis methods have high accuracy in model training. However, it is a challenging task to maintain high accuracy, a faster diagnosis speed, and less computation in practical situations. In this paper, we propose a multi-granularity contrastive learning (MG-CL) framework. First, the original disconnector current data are transformed into two different but related classes: strongly enhanced and weakly enhanced data, by using the strong and weak enhancement modules. Second, we propose the coarse-grained contrastive learning module to preliminarily judge the possibility of faults by learning the features of strongly/weakly enhanced data. Finally, in order to further judge the fault causes, we propose a fine-grained contrastive learning module. By comparing the differences in the data, the final fault type was judged. Our proposed MG-CL framework shows higher accuracy and speed compared with the previous model.

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Section: Continuous Wavelet Transformmentioning

confidence: 99%

“…The accuracy of feature extraction is a key step in fault diagnosis for directly affecting the recognition results. Therefore, it is very important to study how to achieve a clear and effective feature representation method with a small amount of labeled data [7].…”

Section: Introductionmentioning

confidence: 99%

Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

Xie,

Tang,

Liu

et al. 2023

Processes

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A multi-strategy enhanced northern goshawk optimization algorithm for global optimization and engineering design problems

Li¹,

Huang²,

Fu³

et al. 2023

Computer Methods in Applied Mechanics and Engineering

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Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

Ma,

Yu,

et al. 2024

Meas. Sci. Technol.

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The combined failure from rolling bearings is featured by weakness and complexity and hard to be recognized precisely. One-dimensional local binary pattern (1D-LBP) manifests failure information of rolling bearings from textural analysis. However, when signals are quantized with 1D-LBP, the periodic impact feature of fault signal itself will be excluded from consideration and consequently, the fault features will be hard to be dug sufficiently. Feature mode decomposition (FMD) is sensitive to impulse and periodicity of fault signals, but the number of decomposition modes and the length of filter determines accuracy of signal decomposition. To solve these problems, an adaptive local binarization feature mode decomposition (ALBFMD) method was proposed. ALBFMD method represents compound failure information of rolling bearings from textural feature extraction and inherent features of fault signal. Furthermore, with minimum permutation entropy as criterion, the number of decomposition modes and the length of filter of ALBFMD was adaptively determined. According to power spectrums of reconstructed signals, the type of combined faults could be precisely identified. The proposed method was compared with FMD and VMD method and analyzed in different situations. Its superiority in terms of feature extraction and combined failure identification of bearings has been verified.

show abstract

Adaptive Composite Fault Diagnosis of Rolling Bearings Based on the CLNGO Algorithm

Cited by 4 publications

References 28 publications

Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

Disconnector Fault Diagnosis Based on Multi-Granularity Contrast Learning

A multi-strategy enhanced northern goshawk optimization algorithm for global optimization and engineering design problems

Adaptive local binarization feature mode decomposition and its application in combined failure identification of rolling bearings

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