Context awareness for maintenance decision making: A diagnosis and prognosis approach. Galar, D., Thaduri, A., Catelani, M., & Ciani, L. Measurement, 67:137–150, May, 2015. ![Context awareness for maintenance decision making: A diagnosis and prognosis approach [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex All assets necessarily suffer wear and tear during operation. Prognostics can assess the current health of a system and predict its remaining life based on features capturing the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area but has become an important part of Condition-based Maintenance (CBM) of systems. As there are many prognostic techniques, usage must be acceptable to particular applications. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more complete information can be gathered, leading to more accurate recognition of the fault state. In this context, it is also important to evaluate the consistency and the reliability of the measurement data obtained during laboratory testing activity and the prognostic/diagnostic monitoring of the system under examination. This approach is especially relevant in systems where the maintainer and operator know some of the failure mechanisms with sufficient amount of data, but the sheer complexity of the assets precludes the development of a complete model-based approach. This paper addresses the process of data aggregation into a contextual awareness hybrid model to get Residual Useful Life (RUL) values within logical confidence intervals so that the life cycle of assets can be managed and optimised.
@article{galar_context_2015,
title = {Context awareness for maintenance decision making: {A} diagnosis and prognosis approach},
volume = {67},
issn = {0263-2241},
shorttitle = {Context awareness for maintenance decision making},
url = {https://www.sciencedirect.com/science/article/pii/S0263224115000408},
doi = {10.1016/j.measurement.2015.01.015},
abstract = {All assets necessarily suffer wear and tear during operation. Prognostics can assess the current health of a system and predict its remaining life based on features capturing the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area but has become an important part of Condition-based Maintenance (CBM) of systems. As there are many prognostic techniques, usage must be acceptable to particular applications. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more complete information can be gathered, leading to more accurate recognition of the fault state. In this context, it is also important to evaluate the consistency and the reliability of the measurement data obtained during laboratory testing activity and the prognostic/diagnostic monitoring of the system under examination. This approach is especially relevant in systems where the maintainer and operator know some of the failure mechanisms with sufficient amount of data, but the sheer complexity of the assets precludes the development of a complete model-based approach. This paper addresses the process of data aggregation into a contextual awareness hybrid model to get Residual Useful Life (RUL) values within logical confidence intervals so that the life cycle of assets can be managed and optimised.},
language = {en},
urldate = {2022-03-05},
journal = {Measurement},
author = {Galar, Diego and Thaduri, Adithya and Catelani, Marcantonio and Ciani, Lorenzo},
month = may,
year = {2015},
keywords = {Condition based maintenance, Condition monitoring, Context-driven, Diagnosis, Prognosis, eMaintenance},
pages = {137--150},
}
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Prognosis is a relatively new area but has become an important part of Condition-based Maintenance (CBM) of systems. As there are many prognostic techniques, usage must be acceptable to particular applications. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more complete information can be gathered, leading to more accurate recognition of the fault state. In this context, it is also important to evaluate the consistency and the reliability of the measurement data obtained during laboratory testing activity and the prognostic/diagnostic monitoring of the system under examination. This approach is especially relevant in systems where the maintainer and operator know some of the failure mechanisms with sufficient amount of data, but the sheer complexity of the assets precludes the development of a complete model-based approach. 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Prognostics can assess the current health of a system and predict its remaining life based on features capturing the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area but has become an important part of Condition-based Maintenance (CBM) of systems. As there are many prognostic techniques, usage must be acceptable to particular applications. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more complete information can be gathered, leading to more accurate recognition of the fault state. 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