Comparison of Support Vector Machine‐Based Techniques for Detection of Bearing Faults

Wang, Lijun; Ji, Shengfei; Ji, Nanyang

doi:10.1155/2018/8174860

Cited by 7 publications

(2 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MLA based on experimental data sets seems to be very efficient in terms of accuracy [33], but it requires an enormous amount of historical operating data, which limits diagnostic reliability to devices with an extended operating history.…”

Section: Conventional Classificationmentioning

confidence: 99%

Digital twin-driven machine learning: ball bearings fault severity classification

Farhat¹,

Chiementin²,

Chaari³

et al. 2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

Machine learning algorithms (MLAs) are increasingly being used as effective techniques for processing vibration signals obtained from complex industrial machineries. Previous applications of automatic fault detection algorithms in the diagnosis of rotating machines were mainly based on historical operating data sets, limiting the diagnostic reliability to devices with an extended operating history. Moreover, physically collected data are often restricted by the conditions of acquisition and the specific elements for which they were recorded. Digital twin (DT) provides a powerful tool able to generate a huge amount of training data for MLAs. However, the DT model must be accurate enough to substitute the experiments. This work aims to escape the experience requirement by using a simulation-driven MLA based on the multifactorial analysis of fault indicators associated with a DT. To achieve this approach, a numerical model of a rotor-ball bearing system is developed. The latter is updated according to a parameter update scheme based on a comparison between the relevant features of the experimentally measured signals and the signals simulated by the model. These features are chosen as the selected input parameters of the MLA classifier. The results show that after updating, the developed DT has provided a reliable diagnostic with an adaptive degradation analysis, which makes the simulated data suitable for the construction of a machine learning predictive model. Two common MLAs, (multi-kernel support vector machine) and (k nearest neighbor’s algorithm), were trained using the simulated data and validated later against experimental datasets.

show abstract

Section: Conventional Classificationmentioning

confidence: 99%

Digital twin-driven machine learning: ball bearings fault severity classification

Farhat¹,

Chiementin²,

Chaari³

et al. 2021

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…As the working scenarios of industrial machines become more and more complex and diverse, their health condition monitoring is of increasing importance [1]. Single sensor signals cannot fulfill this task in diversified scenarios.…”

Section: Introductionmentioning

confidence: 99%

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

Feng,

Liu

2023

JST

View full text Add to dashboard Cite

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

show abstract

Detection of roller bearing faults at different rotational speeds using envelope analysis features and support vector machine

Najim,

Ali

2024

Transport, Ecology, Sustainable Development: Eko Varna 2023

View full text Add to dashboard Cite

Comparison of Support Vector Machine‐Based Techniques for Detection of Bearing Faults

Cited by 7 publications

References 8 publications

Digital twin-driven machine learning: ball bearings fault severity classification

Digital twin-driven machine learning: ball bearings fault severity classification

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

Detection of roller bearing faults at different rotational speeds using envelope analysis features and support vector machine

Contact Info

Product

Resources

About