Fuzzy feature fusion and multimodal degradation prognosis for mechanical components

Li, Xuejiao; Ren, Yongmei; Tan, Xiaoyang

doi:10.3233/jifs-169531

Cited by 4 publications

(2 citation statements)

References 27 publications

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“…Health rolling bearings are essential to the safety, reliability, and effectiveness of wind turbine operation. The fault diagnosis of rolling bearings is a significant work in wind turbine maintenance [1,2]. Therefore, many approaches have been proposed and developed to identify bearing faults as accurately as possible [3].…”

Section: Introductionmentioning

confidence: 99%

Transfer Deep Learning Network for Rolling Bearing Fault Diagnosis of Wind Turbines

Cheng

Guo

et al. 2023

J. Phys.: Conf. Ser.

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Fault diagnosis of rolling bearings has become a critical measure to ensure the security, efficiency, and availability of wind turbine systems. In this work, an approach called a transfer deep learning network is reported to resolve the drawbacks of existing rolling bearing fault algorithms on the basis of deep learning, such as large training parameters, long training time, and insufficient training samples. The reported approach mainly consists of two steps: feature transfer using transfer component analysis and network transfer using the pre-trained convolutional neural network. One dataset of rolling bearings with four fault types is gathered to estimate the classification performance, and the peer model is employed for a comparison study. The derived research results show that the reported algorithm based on the feature transfer and the network transfer can yield outstanding results in the rolling bearing fault diagnosis.

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Section: Introductionmentioning

confidence: 99%

Transfer Deep Learning Network for Rolling Bearing Fault Diagnosis of Wind Turbines

Cheng

Guo

et al. 2023

J. Phys.: Conf. Ser.

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“…The contributions of Z. Chen and Z. Li [4]; Peña et al [5]; Sánchez et al [6]; Xie et al [7]; Jin et al [8], and Luo et al [9] deal with feature selection. Feature fusion is the main subject of the contributions of Jiang et al [10] and X. Li et al [11] In the paper of Jiang et al a deep belief network is exploited as a feature fusion method for bearings diagnosis. The work of X. Li et al reports the development of a fuzzy feature fusion and multimodal regression method for obtaining a degradation index for mechanical components.…”

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confidence: 99%

Advances in intelligent computing for diagnostics, prognostics, and system health management

Oliveira

2018

IFS

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The guest editors have accepted 41 papers with the special issue. By diagnosis, prognostics and system health management we mean a set of activities including: fault detection, fault classification, fault prognosis, and system modeling. Informally, fault detection refers to the real-time signal processing required to know whether or not a given system is in its healthy normal operating state. Fault classification refers to determination of the type of fault an unhealthy system is suffering from and is a pattern recognition task. Fault prognosis refers to the forecast of the remaining useful life of a system and is based on dynamic modeling. In general, these activities require a sequence of operations such as data acquisition and conditioning, feature extraction, feature selection, and a final

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An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

Guo

Zhang

Xia

et al. 2021

IFS

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The rolling bearing is the crucial component in the rotating machinery. The degradation process monitoring and remaining useful life prediction of the bearing are necessary for the condition-based maintenance. The commonly used deep learning methods use the raw or processed time domain data as the input. However, the feature extracted by these approaches is insufficient and incomprehensive. To tackle this problem, this paper proposed an improved Deep Convolution Neural Network with the dual-channel input from the time and frequency domain in parallel. The proposed methodology consists of two stages: the incipient failure identification and the degradation process fitting. To verify the effectiveness of the method, the IEEE PHM 2012 dataset is adopted to compare the proposed method and other commonly used approaches. The results show that the improved Deep Convolution Neural Network can effectively describe the degradation process for the rolling bearing.

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Fuzzy feature fusion and multimodal degradation prognosis for mechanical components

Cited by 4 publications

References 27 publications

Transfer Deep Learning Network for Rolling Bearing Fault Diagnosis of Wind Turbines

Transfer Deep Learning Network for Rolling Bearing Fault Diagnosis of Wind Turbines

Advances in intelligent computing for diagnostics, prognostics, and system health management

An improved deep convolution neural network for predicting the remaining useful life of rolling bearings

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