RESEARCH PAPER
A data-driven predictive maintenance strategy based on accurate failure prognostics
1
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Publication date: 2021-06-30
Eksploatacja i Niezawodność – Maintenance and Reliability 2021;23(2):387-394
HIGHLIGHTS
- Degradation feature selection module helps to lessen calculation burden.
- The prognostic model provides degradation trends online for failure prognostics.
- The perfect time for taking maintenance activities can be determined.
KEYWORDS
ABSTRACT
Maintenance is fundamental to ensure the safety, reliability and availability of engineering
systems, and predictive maintenance is the leading one in maintenance technology. This
paper aims to develop a novel data-driven predictive maintenance strategy that can make appropriate maintenance decisions for repairable complex engineering systems. The proposed
strategy includes degradation feature selection and degradation prognostic modeling modules to achieve accurate failure prognostics. For maintenance decision-making, the perfect
time for taking maintenance activities is determined by evaluating the maintenance cost online that has taken into account of the failure prognostic results of performance degradation.
The feasibility and effectiveness of the proposed strategy is confirmed using the NASA data
set of aero-engines. Results show that the proposed strategy outperforms the two benchmark
maintenance strategies: classical periodic maintenance and emerging dynamic predictive
maintenance.
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