A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and CNN-SVM

Fan, Hongwei; Xue, Ceyi; Jiateng, Ma; Cao, Xiangang; Zhang, Xuhui

doi:10.1088/1361-6501/acad90

Cited by 29 publications

(18 citation statements)

References 30 publications

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“…The use of downsampling reduces the number of feature parameters after the convolution layer, thereby reducing the complexity of the network structure, improving the computational efficiency of the network by the computer, and avoiding insufficient generalization ability of the network caused by excessive feature parameters [32].…”

Section: Pooling Layermentioning

confidence: 99%

The research on fault diagnosis of rolling bearing based on current signal CNN-SVM

Wang,

Meng,

Wang

et al. 2023

Meas. Sci. Technol.

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This article proposes a novel approach to address the issues of low accuracy in fault diagnosis and the difficulty in installing sensors on rolling bearings in mechanical and electrical equipment systems. To accomplish fault diagnosis of rolling bearings, a network structure algorithm based on Convolutional Neural Network (CNN) and Support Vector Machine (SVM) is presented, which incorporates the electric motor current signal. Firstly, the collected electric motor current signal is subjected to a wavelet filter with a soft-hard threshold to eliminate the noise. Secondly, the processed data is fed as input to a one-dimensional Convolutional Neural Network (1D-CNN) to perform feature extraction and dimensionality reduction. Finally, the dimensionality-reduced features are processed by a Support Vector Machine to diagnose rolling bearing faults. The research results indicate that the proposed method significantly improves the accuracy of rolling bearing fault diagnosis compared to other approaches, with an accuracy of up to 99.01%. This study introduces an innovative approach that can be applied to the field of rolling bearing fault diagnosis, offering valuable insights for research and application in this domain.

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Section: Pooling Layermentioning

confidence: 99%

The research on fault diagnosis of rolling bearing based on current signal CNN-SVM

Wang,

Meng,

Wang

et al. 2023

Meas. Sci. Technol.

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show abstract

“…According to [26], the optimal solution of equation ( 10) is S * = 1 ρ ℑ σ (ρW − Λ), where ℑ σ (•) is a singular value threshold operator. Then, the auxiliary variable S is fixed to solve W, and equation ( 6) is equivalent to solve equation (11).…”

Section: Learning Algorithmmentioning

confidence: 99%

“…Currently, data-driven methods that combine sensor monitoring data with machine learning algorithms are a research hotspot in the field of roller bearing fault diagnosis, and many fault diagnosis algorithms based on data analysis and processing have been proposed and applied, such as decision tree [5,6], random forest (RF) [7,8], artificial neural network [9,10] and support vector machine (SVM) [11][12][13]. Among the aforementioned algorithms, the support vector machine (SVM) algorithm has a solid theoretical foundation and has been widely used in various fields.…”

Section: Introductionmentioning

confidence: 99%

A fault diagnosis approach for roller bearing based on boundary smooth support matrix machine

Shi,

Pan,

Cheng

et al. 2023

Meas. Sci. Technol.

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Support matrix machine (SMM), as a typical matrix classification method, is commonly used in the field of mechanical fault diagnosis due to its ability to fully utilize the strong correlation information between rows or columns in the matrix. However, the constraint terms of SMM have the property of local non-differentiability, which affects computational efficiency and accuracy. To address these limitations, a boundary smooth SMM (BSSMM) is proposed in this paper. In BSSMM, the squared hinge loss function is utilized to construct the loss term, which gives the model good generalization performance and robustness. Meanwhile, the square hinge loss function is smooth, which can achieve rapid convergence and avoid falling into the local optimal solution problem. Experimental verification is performed using vibration signals of two types of roller bearings, and the analysis results show that the proposed BSSMM method has superior classification performance compared to SMM and its improved methods.

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“…Support vector machine (SVM) is established on VC dimension theory and structural risk minimization principle based on machine learning methods [21]. It has better properties, especially in quickly setting the optimal alternative for current cases based on multi-attribute case history data [22]. As the SVM's input, the case history data sets frequently have redundant attributes [23].…”

Section: Related Workmentioning

confidence: 99%

RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

Zhang

et al. 2022

Data Intelligence

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Urban drainage pipe network is the backbone of urban drainage, flood control and water pollution prevention, and is also an essential symbol to measure the level of urban modernization. A large number of underground drainage pipe networks in aged urban areas have been laid for a long time and have reached or practically reached the service age. The repair of drainage pipe networks has attracted extensive attention from all walks of life. Since the Ministry of ecological environment and the national development and Reform Commission jointly issued the action plan for the Yangtze River Protection and restoration in 2019, various provinces in the Yangtze River Basin, such as Anhui, Jiangxi and Hunan, have extensively carried out PPP projects for urban pipeline restoration, in order to improve the quality and efficiency of sewage treatment. Based on the management practice of urban pipe network restoration project in Wuhu City, Anhui Province, this paper analyzes the problems of lengthy construction period and repeated operation caused by the mismatch between the design schedule of the restoration scheme and the construction schedule of the pipe network restoration in the existing project management mode, and proposes a model of urban drainage pipe network restoration scheme selection based on the improved support vector machine. The validity and feasibility of the model are analyzed and verified by collecting the data in the project practice. The research results show that the model has a favorable effect on the selection of urban drainage pipeline restoration schemes, and its accuracy can reach 90%. The research results can provide method guidance and technical support for the rapid decision-making of urban drainage pipeline restoration projects.

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A novel intelligent diagnosis method of rolling bearing and rotor composite faults based on vibration signal-to-image mapping and CNN-SVM

Cited by 29 publications

References 30 publications

The research on fault diagnosis of rolling bearing based on current signal CNN-SVM

The research on fault diagnosis of rolling bearing based on current signal CNN-SVM

A fault diagnosis approach for roller bearing based on boundary smooth support matrix machine

RS-SVM Machine Learning Approach Driven by Case Data for Selecting Urban Drainage Network Restoration Scheme

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