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FUCOM-optimization based predictive maintenance strategy using expert elicitation and Artificial Neural Network
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AI
DOI：10.1016/j.eswa.2023.121322.png)
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Abstract
En 中文
Necessity of predictive maintenance appears when production assets such as machines need either a repair or a regular check. When it comes to monitoring digital twins of objects, maintenance predictions in time play a significant role. This study targets an artificial intelligence-based prediction of time-series data integrated with fully consistent expert elicitation. It also allows the integration of expert knowledge into the construction and explanation of the predictor. Moreover, it is considered a hybrid approach using data and expert knowledge for time series prediction. Reliability of obtained prediction is examined through comparisons with basic dynamic and static forecasts. Decision-making analysis based on expert's opinions and optimization science coincides to prepare the data set for ANN prediction. Thus, the maintenance process will become predictable over time. Moreover, this prediction avoids sudden overhaul cost. Therefore, the entire production system is kept controlled. In this research, a FUll COnsistently Method (FUCOM) of optimization together with Multi Criteria Decision Making (MCDM) is used for obtaining optimal weights. Then, dynamic and static time-series forecasts are compared with ANN prediction. The obtained results prove ANN reliability according to expert's opinions and FUCOM optimization model. FUCOM-ANN prediction achieves nearly 10 times smaller RMSE (0.08) than the RMSE (0.83) of general ANN prediction on the test data set. Moreover, the 10-fold cross-validation strategy shows better RMSE (0.13) for FUCOM-ANN validation sets than the RMSE (0.78) of general ANN validation sets.
Keywords:
Maintenance cost
FUCOM optimization
Time-series
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W