Prognostic modelling options for remaining useful life estimation by industry
Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approx...
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| Published in | Mechanical systems and signal processing Vol. 25; no. 5; pp. 1803 - 1836 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Kidlington
Elsevier Ltd
01.07.2011
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0888-3270 1096-1216 |
| DOI | 10.1016/j.ymssp.2010.11.018 |
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| Abstract | Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs.
This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models. |
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| AbstractList | Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models. Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of engineering assets. Implementations by industry have only had limited success. By design, models are subject to specific assumptions and approximations, some of which are mathematical, while others relate to practical implementation issues such as the amount of data required to validate and verify a proposed model. Therefore, appropriate model selection for successful practical implementation requires not only a mathematical understanding of each model type, but also an appreciation of how a particular business intends to utilise a model and its outputs. This paper discusses business issues that need to be considered when selecting an appropriate modelling approach for trial. It also presents classification tables and process flow diagrams to assist industry and research personnel select appropriate prognostic models for predicting the remaining useful life of engineering assets within their specific business environment. The paper then explores the strengths and weaknesses of the main prognostics model classes to establish what makes them better suited to certain applications than to others and summarises how each have been applied to engineering prognostics. Consequently, this paper should provide a starting point for young researchers first considering options for remaining useful life prediction. The models described in this paper are Knowledge-based (expert and fuzzy), Life expectancy (stochastic and statistical), Artificial Neural Networks, and Physical models. |
| Author | Hodkiewicz, M. Ma, L. Sikorska, J.Z. |
| Author_xml | – sequence: 1 givenname: J.Z. surname: Sikorska fullname: Sikorska, J.Z. email: jo@caswa.com organization: CASWA Pty Ltd., 24 Le Souef Drive, Perth, Kardinya, WA 6163, Australia – sequence: 2 givenname: M. surname: Hodkiewicz fullname: Hodkiewicz, M. organization: Department of Mechanical Engineering, University of Western Australia, Australia – sequence: 3 givenname: L. surname: Ma fullname: Ma, L. organization: School of Engineering Systems, CRC for Integrated Engineering Asset Management (CIEAM), Queensland University of Technology, Australia |
| BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=24105440$$DView record in Pascal Francis |
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| Snippet | Over recent years a significant amount of research has been undertaken to develop prognostic models that can be used to predict the remaining useful life of... |
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| SubjectTerms | Applied sciences Business Exact sciences and technology Fuzzy Industrial metrology. Testing Life prediction Maintenance Mathematical models Mechanical engineering. Machine design Mechanical systems Modelling Operational research and scientific management Operational research. Management science Prognostics Reliability Reliability theory. Replacement problems Remaining useful life (RUL) Tables |
| Title | Prognostic modelling options for remaining useful life estimation by industry |
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