Intelligent prognostics tools and e-maintenance

By Jay H. Lee, Jun Ni, Dragan Djurdjanovic, Hai Qiu, H. Liao. Computers in industry (Print), 2006

629 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Medical Laboratory Technology

View Paper: https://www.semanticscholar.org/paper/af8fe846d47abd7d22c86aaa62ed65778a90850f

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In Control and Systems Engineering

In Medical Laboratory Technology

Citations in the corpus

Recent advances and trends in predictive manufacturing systems in big data environment (2013, methodology): “The actual processing of big data into useful information is performed by predictive analytics such as the Watchdog Agent that has been developed by the Center for Intelligent Maintenance Systems (IMS) since 2001 [8,9].”
On condition based maintenance policy (2015, background): “[25] introduced the emerging field of emaintenance and its critical elements.”
Cloud-enabled prognosis for manufacturing (2015, background): “This is especially true in diagnosis and prognosis where sensor fusion is important in providing a holistic perspective of the state of a manufacturing system [217].”
Remaining useful life estimation - A review on the statistical data driven approaches (2011, background): “RUL estimation is one of the key factors in condition based maintenance (CBM) (Cui et al., 2004; Lee et al., 2006; Wang, 2007a,b; Wang and Zhang, 2008), and prognostics and health management (Dong and He, 2007a; Pecht, 2008; Pecht and Jaai, 2010; Gašperin et al., 2011).”
An artificial neural network method for remaining useful life prediction of equipment subject to condition monitoring (2009, methodology): “Lee et al. (2006) proposed to extract an overall health indicator based on the collected condition data, and predict future health indicator values using the autoregressive moving average (ARMA) method and Elman neural networks.”
PRONOSTIA : An experimental platform for bearings accelerated degradation tests. (2012, methodology): “In [9] the authors used an experimental setup related to drilling life tests to verify their method.”
An overview of time-based and condition-based maintenance in industrial application (2012, methodology): “For the others references regarding the RUL estimation, refer to Lee et al. (2006), Wang (2007), Wang and Zhang (2008a, 2008b), Pecht and Jaai (2010), Gašperin, Juričić, Baškoski, and Jožef (2011).”
Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction (2014, neutral): “Different regression models have been proposed in the literature to deal with data-driven RUL prediction problem such as the auto regressive model and the multivariate adaptive regression splines (Box and Jenkins 1976; Lewis 1992; Tsay 2000; Wu et al. 2007; Yan et al. 2004; Lee et al. 2006).”