Canonical variate analysis, probability approach and support vector regression for fault identification and failure time prediction
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Li, X. | en |
| dc.contributor.author | Duan, Fang | en |
| dc.contributor.author | Bennett, Ian | en |
| dc.contributor.author | Mba, David | en |
| dc.date.acceptance | 2018-03-08 | en |
| dc.date.accessioned | 2018-03-20T13:16:26Z | |
| dc.date.available | 2018-03-20T13:16:26Z | |
| dc.date.issued | 2018-06-22 | |
| dc.description | The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link | en |
| dc.description.abstract | Reciprocating compressors are widely used in oil and gas industry for gas transport, lift and injection. Critical compressors that compress flammable gases and operate at high speeds are high priority equipment on maintenance improvement lists. Identifying the root causes of faults and estimating remaining usable time for reciprocating compressors could potentially reduce downtime and maintenance costs, and improve safety and availability. In this study, Canonical Variate Analysis (CVA), Cox Proportional Hazard (CPHM) and Support Vector Regression (SVR) models are employed to identify fault related variables and predict remaining usable time based on sensory data acquired from an operational industrial reciprocating compressor. 2-D contribution plots for CVA-based residual and state spaces were developed to identify variables that are closely related to compressor faults. Furthermore, a SVR model was used as a prognostic tool following training with failure rate vectors obtained from the CPHM and health indicators obtained from the CVA model. The trained SVR model was utilized to estimate the failure degradation rate and remaining useful life of the compressor. The results indicate that the proposed method can be effectively used in real industrial processes to perform fault diagnosis and prognosis. | en |
| dc.funder | N/A | en |
| dc.identifier.citation | Li, X., Duan, F., Bennett, I. and Mba, D. (2018) Canonical variate analysis, probability approach and support vector regression for fault identification and failure time prediction. Journal of Intelligent and Fuzzy Systems, | en |
| dc.identifier.doi | https://doi.org/10.3233/JIFS-169550 | |
| dc.identifier.issn | 1064-1246 | |
| dc.identifier.uri | http://hdl.handle.net/2086/15544 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.projectid | N/A | en |
| dc.publisher | IOS Press | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Condition monitoring | en |
| dc.subject | Canonical Variate Analysis | en |
| dc.subject | Proportional Hazard Model | en |
| dc.title | Canonical variate analysis, probability approach and support vector regression for fault identification and failure time prediction | en |
| dc.type | Article | en |