An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data
| dc.cclicence | CC-BY-NC | en |
| dc.contributor.author | Li, Xiaochuan | |
| dc.contributor.author | Yang, Xiaoyu | |
| dc.contributor.author | Yang, Yingjie | |
| dc.contributor.author | Bennett, Ian | |
| dc.contributor.author | Mba, David | |
| dc.date.acceptance | 2019-10 | |
| dc.date.accessioned | 2019-10-17T11:41:50Z | |
| dc.date.available | 2019-10-17T11:41:50Z | |
| dc.date.issued | 2019 | |
| 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 | In this work, a novel diagnostic and prognostic framework is proposed to detect faults and predict remaining service life of large-scale rotating machinery in the presence of scarce failure data. In the proposed framework, a canonical variate residuals–based diagnostic method is developed to facilitate remaining service life prediction by continuously implementing detection of the prediction start time. A novel two-step prognostic feature exploring approach that involves fault identification, feature extraction, feature selection and multi-feature fusion is put forward. Most existing prognostic methods lack a fault-identification module to automatically identify the fault root-cause variables required in the subsequent prognostic analysis and decision-making process. The proposed prognostic feature exploring method overcomes this challenge by introducing a canonical variate residuals–based fault-identification method. With this method, the most representative degradation features are extracted from only the fault root-cause variables, thereby facilitating machinery prognostics by ensuring accurate estimates. Its effectiveness is demonstrated for slow involving faults in two case studies of an operational industrial centrifugal pump. Moreover, an enhanced grey model approach is developed for remaining useful life prediction. In particular, the empirical Bayesian algorithm is employed to improve the traditional grey forecasting model in terms of quantifying the uncertainty of remaining service life in a probabilistic form and improving its prediction accuracy. To demonstrate the superiority of empirical Bayesian–grey model, existing prognostic methods such as grey model, particle filter–grey model and empirical Bayesian–exponential regression are also utilized to realize machinery remaining service life prediction, and the results are compared with that of the proposed method. The achieved predictive accuracy shows that the proposed approach outperforms its counterparts and is highly applicable in fault prognostics of industrial rotating machinery. The use of in-service data in a practical scenario shows that the proposed prognostic approach is a promising tool for online health monitoring. | en |
| dc.funder | No external funder | en |
| dc.identifier.citation | Li, X., Yang, X., Yang, Y., Bennett, I., Mba, D. (2019) An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data. Structural Health Monitoring, | en |
| dc.identifier.doi | https://doi.org/10.1177/1475921719884019 | |
| dc.identifier.issn | 1475-9217 | |
| dc.identifier.uri | https://dora.dmu.ac.uk/handle/2086/18637 | |
| dc.language.iso | en | en |
| dc.peerreviewed | Yes | en |
| dc.publisher | Sage | en |
| dc.researchinstitute | Institute of Artificial Intelligence (IAI) | en |
| dc.subject | Condition monitoring | en |
| dc.subject | fault detection | en |
| dc.subject | prognosis | en |
| dc.subject | canonical variable analysis | en |
| dc.subject | grey model | en |
| dc.title | An intelligent diagnostic and prognostic framework for large-scale rotating machinery in the presence of scarce failure data | en |
| dc.type | Article | en |