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http://bura.brunel.ac.uk/handle/2438/21023
Title: | Causal Modelling for Predicting Machine Tools Degradation in High Speed Production Process |
Authors: | Angadi, V Mousavi, A Bartolome, D Tellarini, M Fazziani, M |
Keywords: | prediction Methods;industry automation;regression analysis;discrete event dynamic system;maintenance engineering;trends |
Issue Date: | 18-Dec-2020 |
Publisher: | Elsevier on behalf of IFAC (International Federation of Automatic Control) |
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. |
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. |
URI: | https://bura.brunel.ac.uk/handle/2438/21023 |
DOI: | https://doi.org/10.1016/j.ifacol.2020.11.044 |
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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