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DC Field | Value | Language |
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dc.contributor.author | Angadi, V | - |
dc.contributor.author | Mousavi, A | - |
dc.contributor.author | Bartolome, D | - |
dc.contributor.author | Tellarini, M | - |
dc.contributor.author | Fazziani, M | - |
dc.coverage.spatial | Cambridge,UK | - |
dc.date.accessioned | 2020-06-17T23:17:24Z | - |
dc.date.available | 2020-06-17T23:17:24Z | - |
dc.date.issued | 2020-12-18 | - |
dc.identifier.citation | Angadi, V.C. et al. (2020) 'Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process', IFAC-PapersOnLine, 53 (3), pp. 271 - 275. doi::10.1016/j.ifacol.2020.11.044. | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/21023 | - |
dc.description.abstract | Copyright © 2020 The Authors. A dynamic health indicator based on regressive event-tracker algorithm is proposed to accurately interpret the condition of critical components of machine tools in a production system and to predict their potential sudden breakdown based on future trends. Through sensors/actuators data acquisition, the algorithm predicts the causal links between various monitored parameters of the system and offers a diagnosis of the health state of the system. A safety and operational robustness regime determines the acceptable thresholds of the operational boundaries of the electro-mechanical components of the machines. The proposed model takes into account the possibilities of sensor values being a piecewise-linear models or a pair of exponential functions with restricted model parameters, which can predict the runs-to-failure or remaining useful life until a safety threshold. The events caused by sensors passing through sub levels of safety threshold are used as a re-enforcement learning for the models. Each remaining useful life estimation diagnosis and prognosis analysis can be conducted on individual or an interconnected network of components within a machine. The overall health indicator based on individual useful life estimation is calculated by deriving the weights from event-clustering algorithm. The work can be extended to a network of machines representing a process. The outcome of the continuously learning real-time condition monitoring modus-operandi is to accurately measure the remaining useful life of the network of critical components of a machine. | en_US |
dc.description.sponsorship | European Union’s Horizon 2020 Z-BRE4K and innovation program under grant agreement No. 768869. | en_US |
dc.format.extent | 271 - 275 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier on behalf of IFAC (International Federation of Automatic Control) | - |
dc.rights | Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control.. doi: https://doi.org/10.1016/j.ifacol.2020.11.044. | - |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | - |
dc.source | The 4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies 2020 | - |
dc.source | The 4th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies 2020 | - |
dc.subject | prediction Methods | en_US |
dc.subject | industry automation | en_US |
dc.subject | regression analysis | en_US |
dc.subject | discrete event dynamic system | en_US |
dc.subject | maintenance engineering | en_US |
dc.subject | trends | en_US |
dc.title | Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process | en_US |
dc.type | Conference Paper | en_US |
dc.identifier.doi | https://doi.org/10.1016/j.ifacol.2020.11.044 | - |
dc.relation.isPartOf | IFAC-PapersOnLine | - |
dc.relation.isPartOf | IFAC-PapersOnLine | - |
pubs.issue | 3 | - |
pubs.publication-status | Pulished | - |
pubs.start-date | 2020-09-07 | - |
pubs.start-date | 2020-09-07 | - |
pubs.volume | 53 | - |
dc.identifier.eissn | 2405-8963 | - |
dc.identifier.eissn | Electronic | - |
dc.rights.holder | The Authors | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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FullText.pdf | Copyright © 2020 The Authors. This is an open access article under the CC BY-NC-ND license. Peer review under responsibility of International Federation of Automatic Control.. doi: https://doi.org/10.1016/j.ifacol.2020.11.044. | 798.01 kB | Adobe PDF | View/Open |
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