Attention-based deep meta-transfer learning for few-shot fine-grained fault diagnosis
Deep learning-based fault diagnosis methods have made tremendous progress in recent
years; however, most of these methods are coarse grained and data demanding that cannot …
years; however, most of these methods are coarse grained and data demanding that cannot …
[HTML][HTML] Deep reinforcement learning for predictive aircraft maintenance using probabilistic remaining-useful-life prognostics
The increasing availability of sensor monitoring data has stimulated the development of
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
[HTML][HTML] Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics
The increasing availability of condition monitoring data for aircraft components has
incentivized the development of Remaining Useful Life (RUL) prognostics in the past years …
incentivized the development of Remaining Useful Life (RUL) prognostics in the past years …
Predictive maintenance in Industry 4.0: A systematic multi-sector map**
Industry 4.0 is strongly intertwined with big data streaming flows from intelligent sensors and
machinery installed in industrial facilities. Failures can disrupt production and lead the …
machinery installed in industrial facilities. Failures can disrupt production and lead the …
Hyperparameter-optimized multi-fidelity deep neural network model associated with subset simulation for structural reliability analysis
The present study proposes a two-stage Bi-Fidelity Deep Neural Network surrogate model to
quantify the uncertainty of structural analysis using low-fidelity data samples added to the …
quantify the uncertainty of structural analysis using low-fidelity data samples added to the …
Hierarchical Bayesian support vector regression with model parameter calibration for reliability modeling and prediction
S Haoyuan, M Yizhong, L Chenglong, Z Jian… - Reliability Engineering & …, 2023 - Elsevier
Support vector regression (SVR) has been widely used for reliability modeling and
prediction in various engineering practices. In order to improve the accuracy and robustness …
prediction in various engineering practices. In order to improve the accuracy and robustness …
Adaptive directed support vector machine method for the reliability evaluation of aeroengine structure
Abstract Machine learning methods have been widely applied to structural reliability
analysis, due to the excellent performance in modeling precision and efficiency. In this …
analysis, due to the excellent performance in modeling precision and efficiency. In this …
Inspection schedule for prognostics with uncertainty management
Condition monitoring data is an essential ingredient for prognostics and health
management. To minimize unnecessary inspections or measurements, it is crucial to …
management. To minimize unnecessary inspections or measurements, it is crucial to …
A dynamic predictive maintenance approach using probabilistic deep learning for a fleet of multi-component systems
J Zeng, Z Liang - Reliability Engineering & System Safety, 2023 - Elsevier
Empowered by the ubiquitous sensing infrastructure, predictive maintenance (PdM) has
received increasing attention. However, designing predictive maintenance for multiple …
received increasing attention. However, designing predictive maintenance for multiple …
Strategy selection for multi-objective redundancy allocation problem in a k-out-of-n system considering the mean time to failure
This paper presents a redundancy allocation problem (RAP) to maximize the mean time to
failure (MTTF) and minimize the costs for a k-out-of-n system with several subsystems. In this …
failure (MTTF) and minimize the costs for a k-out-of-n system with several subsystems. In this …