The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network
In response to challenges associated with feature extraction and diagnostic models’ complexity in the early diagnosis of bearings’ faults, this paper presents an innovative approach for the early fault diagnosis of rolling bearings. This method combined concepts from frequency domain signal analysis...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
|
| Series: | Fractal and Fractional |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-3110/7/12/875 |
| _version_ | 1797380999572619264 |
|---|---|
| author | Fengyun Xie Gang Li Chengjie Song Minghua Song |
| author_facet | Fengyun Xie Gang Li Chengjie Song Minghua Song |
| author_sort | Fengyun Xie |
| collection | DOAJ |
| description | In response to challenges associated with feature extraction and diagnostic models’ complexity in the early diagnosis of bearings’ faults, this paper presents an innovative approach for the early fault diagnosis of rolling bearings. This method combined concepts from frequency domain signal analysis with lightweight neural networks. To begin, vibration signals from rolling bearings were collected using vibration sensors, and the mean square value was utilized as an indicator for accurate early fault signal extraction. Subsequently, employing the fractional Fourier transform, the time domain signal was converted into a frequency domain signal, which provided more detailed frequency feature information. The fusion process combined amplitude frequency and phase frequency information, and was visualized as a Gram angle field map. The lightweight neural network Xception was selected as the primary fault diagnosis tool. Xception, a convolutional neural network (CNN) variant, was chosen for its lightweight design, which maintains excellent performance while significantly reducing model parameters. The experimental results demonstrated that the Xception model excelled in rolling bearing fault diagnosis, particularly when utilizing fused information datasets. This outcome underscores the advantages of combining information fusion and the Xception model to enhance the accuracy of early rolling bearing fault diagnosis, and offers a viable solution for health monitoring and fault diagnosis in industrial settings. |
| first_indexed | 2024-03-08T20:46:01Z |
| format | Article |
| id | doaj.art-c0ee68d1f1da4d63988009af64fd1828 |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-03-08T20:46:01Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-c0ee68d1f1da4d63988009af64fd18282023-12-22T14:10:02ZengMDPI AGFractal and Fractional2504-31102023-12-0171287510.3390/fractalfract7120875The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural NetworkFengyun Xie0Gang Li1Chengjie Song2Minghua Song3School of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Mechanical Electrical and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaIn response to challenges associated with feature extraction and diagnostic models’ complexity in the early diagnosis of bearings’ faults, this paper presents an innovative approach for the early fault diagnosis of rolling bearings. This method combined concepts from frequency domain signal analysis with lightweight neural networks. To begin, vibration signals from rolling bearings were collected using vibration sensors, and the mean square value was utilized as an indicator for accurate early fault signal extraction. Subsequently, employing the fractional Fourier transform, the time domain signal was converted into a frequency domain signal, which provided more detailed frequency feature information. The fusion process combined amplitude frequency and phase frequency information, and was visualized as a Gram angle field map. The lightweight neural network Xception was selected as the primary fault diagnosis tool. Xception, a convolutional neural network (CNN) variant, was chosen for its lightweight design, which maintains excellent performance while significantly reducing model parameters. The experimental results demonstrated that the Xception model excelled in rolling bearing fault diagnosis, particularly when utilizing fused information datasets. This outcome underscores the advantages of combining information fusion and the Xception model to enhance the accuracy of early rolling bearing fault diagnosis, and offers a viable solution for health monitoring and fault diagnosis in industrial settings.https://www.mdpi.com/2504-3110/7/12/875early fault diagnosisfractional Fourier transforminformation fusionlightweight neural network |
| spellingShingle | Fengyun Xie Gang Li Chengjie Song Minghua Song The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network Fractal and Fractional early fault diagnosis fractional Fourier transform information fusion lightweight neural network |
| title | The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network |
| title_full | The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network |
| title_fullStr | The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network |
| title_full_unstemmed | The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network |
| title_short | The Early Diagnosis of Rolling Bearings’ Faults Using Fractional Fourier Transform Information Fusion and a Lightweight Neural Network |
| title_sort | early diagnosis of rolling bearings faults using fractional fourier transform information fusion and a lightweight neural network |
| topic | early fault diagnosis fractional Fourier transform information fusion lightweight neural network |
| url | https://www.mdpi.com/2504-3110/7/12/875 |
| work_keys_str_mv | AT fengyunxie theearlydiagnosisofrollingbearingsfaultsusingfractionalfouriertransforminformationfusionandalightweightneuralnetwork AT gangli theearlydiagnosisofrollingbearingsfaultsusingfractionalfouriertransforminformationfusionandalightweightneuralnetwork AT chengjiesong theearlydiagnosisofrollingbearingsfaultsusingfractionalfouriertransforminformationfusionandalightweightneuralnetwork AT minghuasong theearlydiagnosisofrollingbearingsfaultsusingfractionalfouriertransforminformationfusionandalightweightneuralnetwork AT fengyunxie earlydiagnosisofrollingbearingsfaultsusingfractionalfouriertransforminformationfusionandalightweightneuralnetwork AT gangli earlydiagnosisofrollingbearingsfaultsusingfractionalfouriertransforminformationfusionandalightweightneuralnetwork AT chengjiesong earlydiagnosisofrollingbearingsfaultsusingfractionalfouriertransforminformationfusionandalightweightneuralnetwork AT minghuasong earlydiagnosisofrollingbearingsfaultsusingfractionalfouriertransforminformationfusionandalightweightneuralnetwork |