Adaptive Framework for Maintenance Scheduling Based on Dynamic Preventive Intervals and Remaining Useful Life Estimation

Pedro Nunes (), Eugénio Rocha and José Santos
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Pedro Nunes: Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal
Eugénio Rocha: Department of Mathematics, University of Aveiro, 3810-193 Aveiro, Portugal
José Santos: Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal

Future Internet, 2024, vol. 16, issue 6, 1-17

Abstract: Data-based prognostic methods exploit sensor data to forecast the remaining useful life (RUL) of industrial settings to optimize the scheduling of maintenance actions. However, implementing sensors may not be cost-effective or practical for all components. Traditional preventive approaches are not based on sensor data; however, they schedule maintenance at equally spaced intervals, which is not a cost-effective approach since the distribution of the time between failures changes with the degradation state of other parts or changes in working conditions. This study introduces a novel framework comprising two maintenance scheduling strategies. In the absence of sensor data, we propose a novel dynamic preventive policy that adjusts intervention intervals based on the most recent failure data. When sensor data are available, a method for RUL prediction, designated k-LSTM-GFT, is enhanced to dynamically account for RUL prediction uncertainty. The results demonstrate that dynamic preventive maintenance can yield cost reductions of up to 51.8% compared to conventional approaches. The predictive approach optimizes the exploitation of RUL, achieving costs that are only 3–5% higher than the minimum cost achievable while ensuring the safety of critical systems since all of the failures are avoided.

Keywords: maintenance scheduling; predictive maintenance; preventive maintenance; prognostics; remaining useful life; reliability (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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