Rotating machinery prognostics: State of the art, challenges and opportunities. Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. Mechanical Systems and Signal Processing, 23(3):724 – 739, 2009. ![Rotating machinery prognostics: State of the art, challenges and opportunities [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Machinery prognosis is the forecast of the remaining operational life, future condition, or probability of reliable operation of an equipment based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce downtime, spares inventory, maintenance costs, and safety hazards. Given the significance of prognostics capabilities and the maturity of condition monitoring technology, there have been an increasing number of publications on rotating machinery prognostics in the past few years. These publications covered a wide spectrum of prognostics techniques. This review article first synthesises and places these individual pieces of information in context, while identifying their merits and weaknesses. It then discusses the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field. Current methods for predicting rotating machinery failures are summarised and classified as conventional reliability models, condition-based prognostics models and models integrating reliability and prognostics. Areas in need of development or improvement include the integration of condition monitoring and reliability, utilisation of incomplete trending data, consideration of effects from maintenance actions and variable operating conditions, derivation of the non-linear relationship between measured data and actual asset health, consideration of failure interactions, practicability of requirements and assumptions, as well as development of performance evaluation frameworks.
@article{heng_rotating_2009,
title = {Rotating machinery prognostics: {State} of the art, challenges and opportunities},
volume = {23},
issn = {0888-3270},
url = {http://www.sciencedirect.com/science/article/pii/S0888327008001489},
doi = {https://doi.org/10.1016/j.ymssp.2008.06.009},
abstract = {Machinery prognosis is the forecast of the remaining operational life, future condition, or probability of reliable operation of an equipment based on the acquired condition monitoring data. This approach to modern maintenance practice promises to reduce downtime, spares inventory, maintenance costs, and safety hazards. Given the significance of prognostics capabilities and the maturity of condition monitoring technology, there have been an increasing number of publications on rotating machinery prognostics in the past few years. These publications covered a wide spectrum of prognostics techniques. This review article first synthesises and places these individual pieces of information in context, while identifying their merits and weaknesses. It then discusses the identified challenges, and in doing so, alerts researchers to opportunities for conducting advanced research in the field. Current methods for predicting rotating machinery failures are summarised and classified as conventional reliability models, condition-based prognostics models and models integrating reliability and prognostics. Areas in need of development or improvement include the integration of condition monitoring and reliability, utilisation of incomplete trending data, consideration of effects from maintenance actions and variable operating conditions, derivation of the non-linear relationship between measured data and actual asset health, consideration of failure interactions, practicability of requirements and assumptions, as well as development of performance evaluation frameworks.},
number = {3},
journal = {Mechanical Systems and Signal Processing},
author = {Heng, Aiwina and Zhang, Sheng and Tan, Andy C. C. and Mathew, Joseph},
year = {2009},
keywords = {Condition monitoring, Condition-based maintenance, Prognostics, Reliability},
pages = {724 -- 739},
}
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