A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis
The application of the existing complex network in fault diagnosis is usually modelled based on the time domain, resulting in the loss of sign frequency-domain features, and the extracted topology features of network are too macroscopic and insensitive to local changes within the network. This paper...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
|
| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2018/2913163 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850224427229249536 |
|---|---|
| author | Qi Zhang Tian Tian Guangrui Wen Zhifen Zhang |
| author_facet | Qi Zhang Tian Tian Guangrui Wen Zhifen Zhang |
| author_sort | Qi Zhang |
| collection | DOAJ |
| description | The application of the existing complex network in fault diagnosis is usually modelled based on the time domain, resulting in the loss of sign frequency-domain features, and the extracted topology features of network are too macroscopic and insensitive to local changes within the network. This paper proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition and builds a new complex network structure feature on this basis, namely, subnetwork average degree. The variation law of signals in frequency domain is obtained with the aid of the structural features of complex network. The local features that are sensitive to local changes of the network are applied to characterize the whole network, with flexible application and without limitation in mechanism. The average degree of subnetwork could be regarded as feature parameters for rolling bearing fault diagnosis and degradation state recognition. Analysis on the experimental data and bearing life cycle data shows that the method proposed in this paper is effective, revealing that the extracted features have effective separability and high accuracy in fault recognition and the degradation detection of the life cycle of rolling bearings combined with neural networks. Moreover, the proposed method has reference value for the processing and recognition of other nonstationary signals. |
| format | Article |
| id | doaj-art-4f54f3d7c71b4c79bcd34eeb38241118 |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-4f54f3d7c71b4c79bcd34eeb382411182025-08-20T02:05:38ZengWileyShock and Vibration1070-96221875-92032018-01-01201810.1155/2018/29131632913163A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault DiagnosisQi Zhang0Tian Tian1Guangrui Wen2Zhifen Zhang3School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, ChinaSchool of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, ChinaThe application of the existing complex network in fault diagnosis is usually modelled based on the time domain, resulting in the loss of sign frequency-domain features, and the extracted topology features of network are too macroscopic and insensitive to local changes within the network. This paper proposes a new method of local feature extraction based on frequency complex network (FCN) decomposition and builds a new complex network structure feature on this basis, namely, subnetwork average degree. The variation law of signals in frequency domain is obtained with the aid of the structural features of complex network. The local features that are sensitive to local changes of the network are applied to characterize the whole network, with flexible application and without limitation in mechanism. The average degree of subnetwork could be regarded as feature parameters for rolling bearing fault diagnosis and degradation state recognition. Analysis on the experimental data and bearing life cycle data shows that the method proposed in this paper is effective, revealing that the extracted features have effective separability and high accuracy in fault recognition and the degradation detection of the life cycle of rolling bearings combined with neural networks. Moreover, the proposed method has reference value for the processing and recognition of other nonstationary signals.http://dx.doi.org/10.1155/2018/2913163 |
| spellingShingle | Qi Zhang Tian Tian Guangrui Wen Zhifen Zhang A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis Shock and Vibration |
| title | A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis |
| title_full | A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis |
| title_fullStr | A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis |
| title_full_unstemmed | A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis |
| title_short | A New Modelling and Feature Extraction Method Based on Complex Network and Its Application in Machine Fault Diagnosis |
| title_sort | new modelling and feature extraction method based on complex network and its application in machine fault diagnosis |
| url | http://dx.doi.org/10.1155/2018/2913163 |
| work_keys_str_mv | AT qizhang anewmodellingandfeatureextractionmethodbasedoncomplexnetworkanditsapplicationinmachinefaultdiagnosis AT tiantian anewmodellingandfeatureextractionmethodbasedoncomplexnetworkanditsapplicationinmachinefaultdiagnosis AT guangruiwen anewmodellingandfeatureextractionmethodbasedoncomplexnetworkanditsapplicationinmachinefaultdiagnosis AT zhifenzhang anewmodellingandfeatureextractionmethodbasedoncomplexnetworkanditsapplicationinmachinefaultdiagnosis AT qizhang newmodellingandfeatureextractionmethodbasedoncomplexnetworkanditsapplicationinmachinefaultdiagnosis AT tiantian newmodellingandfeatureextractionmethodbasedoncomplexnetworkanditsapplicationinmachinefaultdiagnosis AT guangruiwen newmodellingandfeatureextractionmethodbasedoncomplexnetworkanditsapplicationinmachinefaultdiagnosis AT zhifenzhang newmodellingandfeatureextractionmethodbasedoncomplexnetworkanditsapplicationinmachinefaultdiagnosis |