A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features
Accurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for faul...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9203804/ |
| _version_ | 1819276451839475712 |
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| author | Ugochukwu Ejike Akpudo Hur Jang-Wook |
| author_facet | Ugochukwu Ejike Akpudo Hur Jang-Wook |
| author_sort | Ugochukwu Ejike Akpudo |
| collection | DOAJ |
| description | Accurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for fault isolation/classification of which the Binary Particle swarm optimization (BPSO) is superior to its counterparts. This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequency-domain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Compared with other filter and wrapper methods for supervised feature selection, a hybrid filter-wrapper (Pearson's correlation-BPSO (ρ-BPSO)) feature selection procedure is proposed for global search of optimal discriminative (uncorrelated) features for fault diagnosis with an RBF-kernel support vector machine (SVM*). Subsequently, a practical case study involving five VSC63A5 solenoid pumps at various operating/fault conditions is presented for validating the performance of the proposed approach. Results show the superior performance of the proposed hybrid filter-wrapper approach against filter-based and wrapper-based techniques for discriminative feature selection. Also, the proposed ρ-BPSO-SVM* diagnostics model performance was compared with other standard fault isolation/classification methods. |
| first_indexed | 2024-12-23T23:40:26Z |
| format | Article |
| id | doaj.art-7c2547c4abd146f6b661ec21e63d618d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-23T23:40:26Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-7c2547c4abd146f6b661ec21e63d618d2022-12-21T17:25:41ZengIEEEIEEE Access2169-35362020-01-01817502017503410.1109/ACCESS.2020.30259099203804A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative FeaturesUgochukwu Ejike Akpudo0https://orcid.org/0000-0003-4221-5192Hur Jang-Wook1https://orcid.org/0000-0002-4718-3540Department of Mechanical Systems Engineering, Kumoh National Institute of Technology, Gumi, South KoreaDepartment of Mechanical Systems Engineering, Kumoh National Institute of Technology, Gumi, South KoreaAccurate condition monitoring of industrial cyber-physical systems/components demands the use of reliable fault detection and isolation (FD&I) methodologies. Meta-heuristic algorithms for feature selection have good exploration capability for optimal discriminative feature selection for fault isolation/classification of which the Binary Particle swarm optimization (BPSO) is superior to its counterparts. This study presents a robust approach for vibration-based failure diagnostics of electromagnetic/solenoid pumps which employ a multi-domain feature extraction procedure (statistical time-domain and frequency-domain features, Mel frequency cepstral coefficients, and continuous wavelet coefficients) for capturing linear and nonlinear properties from the signals. Compared with other filter and wrapper methods for supervised feature selection, a hybrid filter-wrapper (Pearson's correlation-BPSO (ρ-BPSO)) feature selection procedure is proposed for global search of optimal discriminative (uncorrelated) features for fault diagnosis with an RBF-kernel support vector machine (SVM*). Subsequently, a practical case study involving five VSC63A5 solenoid pumps at various operating/fault conditions is presented for validating the performance of the proposed approach. Results show the superior performance of the proposed hybrid filter-wrapper approach against filter-based and wrapper-based techniques for discriminative feature selection. Also, the proposed ρ-BPSO-SVM* diagnostics model performance was compared with other standard fault isolation/classification methods.https://ieeexplore.ieee.org/document/9203804/Binary particle swarm optimizationcomprehensive feature extractioncontinuous wavelet transformMel frequency cepstral coefficientssolenoid pumpssupport vector machine |
| spellingShingle | Ugochukwu Ejike Akpudo Hur Jang-Wook A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features IEEE Access Binary particle swarm optimization comprehensive feature extraction continuous wavelet transform Mel frequency cepstral coefficients solenoid pumps support vector machine |
| title | A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features |
| title_full | A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features |
| title_fullStr | A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features |
| title_full_unstemmed | A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features |
| title_short | A Multi-Domain Diagnostics Approach for Solenoid Pumps Based on Discriminative Features |
| title_sort | multi domain diagnostics approach for solenoid pumps based on discriminative features |
| topic | Binary particle swarm optimization comprehensive feature extraction continuous wavelet transform Mel frequency cepstral coefficients solenoid pumps support vector machine |
| url | https://ieeexplore.ieee.org/document/9203804/ |
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