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A prognostic algorithm for machine performance assessment and its application
Abstract
This paper explores a method to assess assets performance and predict the remaining useful life, which would lead to proactive maintenance processes to minimize downtime of machinery and production in various industries, thus increasing efficiency of operations and manufacturing. At first, a performance model is established by taking advantage of logistic regression analysis with maximum-likelihood technique. Two kinds of application situations, with or without enough historical data, are discussed in detail. Then, real-time performance is evaluated by inputting features of online data to the logistic model. Finally, the remaining life is estimated using an ARMA model based on machine performance history; degradation predictions are also upgraded dynamically. The results such as current machine running condition and the remaining useful life, are output to the maintenance decision module to determine a window of appropriate maintenance before the machine fails. An application of the method on an elevator door motion system is demonstrated.
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Cited by
Rotating machinery prognostics: State of the art, challenges and opportunities
Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2008.06.009 · Cited by 1,207
A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008) · 2008 International Conference on Prognostics and Health Management · doi:10.1109/phm.2008.4711421 · Cited by 474
Self-citationPan, Y., Dikker, S., Goldstein, P., Zhu, Y., Yang, C., & Hu, Y. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116657 · Cited by 187
Prognostics: a literature review
Elattar, H. M., Elminir, H. K., & Riad, A. M. (2016) · Complex & Intelligent Systems · doi:10.1007/s40747-016-0019-3 · Cited by 181
Fault Prognostics for the Predictive Maintenance of Wind Turbines: State of the Art
Abid, K., Sayed Mouchaweh, M., & Cornez, L. (2019) · Communications in Computer and Information Science · doi:10.1007/978-3-030-14880-5_10 · Cited by 18
Metadata
- Authors
- Yan, J., Koç, M., & Lee, J.
- Venue
- Production Planning & Control
- Publisher
- Informa UK Limited
- Published
- 2004-12
- Volume
- 15
- Issue
- 8
- Pages
- 796-801
- ISSN
- 0953-7287 (ISSN-L)
- 1366-5871
- OpenAlex
- W2041598584
- Cited by
- 239
- References
- 0
- FWCI
- 15.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yan, J., Koç, M., & Lee, J. (2004). A prognostic algorithm for machine performance assessment and its application. Production Planning & Control. https://doi.org/10.1080/09537280412331309208
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
