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title = {Remaining useful life prediction for ion etching machine cooling system using deep recurrent neural network-based approaches},
journal = {Control Engineering Practice},
volume = {109},
pages = {104748},
year = {2021},
issn = {0967-0661},
doi = {https://doi.org/10.1016/j.conengprac.2021.104748},
url = {https://www.sciencedirect.com/science/article/pii/S0967066121000253},
author = {Shimeng Wu and Yuchen Jiang and Hao Luo and Shen Yin},
keywords = {Flowcool system, Remaining useful life (RUL) prediction, Long short-term memory (LSTM), Gated recurrent unit (GRU), Neural network},
abstract = {The cooling system called flowcool is an important part of the ion mill etching (IME) machine. In the case of the cooling system failure during operation, it will lead to significant impacts on the final quality of wafers. To address this problem, effective maintenance plans are made according to the predicted time to failure, i.e., the remaining useful life (RUL). However, the RUL prediction performances of existing prognostic approaches for the flowcool system are not satisfactory. In this work, multiple deep recurrent neural network-based approaches are used for RUL prediction. To improve reliability, the random forest-based early degradation warning approach is employed before the RUL prediction. The long short-term memory (LSTM) neural network, the gated recurrent unit (GRU) neural network, and the additional fully-connected layers (FCs) are used to predict the RUL of flowcool respectively. Results of the comparison study on the dataset of the 2018 PHM Data Challenge competition show that the proposed GRU and GRU-FCs based approaches integrated with sample screening, new feature construction, and degradation warning outperform the other reported approaches. The symmetric mean absolute percentage errors (SMAPE) of the RUL prediction in 3 failure modes achieved 14.13%, 14.44%, and 25.63% respectively.}
}

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@Article{data6010005,
AUTHOR = {Arias Chao, Manuel and Kulkarni, Chetan and Goebel, Kai and Fink, Olga},
TITLE = {Aircraft Engine Run-to-Failure Dataset under Real Flight Conditions for Prognostics and Diagnostics},
JOURNAL = {Data},
VOLUME = {6},
YEAR = {2021},
NUMBER = {1},
ARTICLE-NUMBER = {5},
URL = {https://www.mdpi.com/2306-5729/6/1/5},
ISSN = {2306-5729},
ABSTRACT = {A key enabler of intelligent maintenance systems is the ability to predict the remaining useful lifetime (RUL) of its components, i.e., prognostics. The development of data-driven prognostics models requires datasets with run-to-failure trajectories. However, large representative run-to-failure datasets are often unavailable in real applications because failures are rare in many safety-critical systems. To foster the development of prognostics methods, we develop a new realistic dataset of run-to-failure trajectories for a fleet of aircraft engines under real flight conditions. The dataset was generated with the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) model developed at NASA. The damage propagation modelling used in this dataset builds on the modelling strategy from previous work and incorporates two new levels of fidelity. First, it considers real flight conditions as recorded on board of a commercial jet. Second, it extends the degradation modelling by relating the degradation process to its operation history. This dataset also provides the health, respectively, fault class. Therefore, besides its applicability to prognostics problems, the dataset can be used for fault diagnostics.},
DOI = {10.3390/data6010005}
}







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