Jin et al., 2020 - Google Patents
An optimal maintenance strategy for multi-state deterioration systems based on a semi-Markov decision process coupled with simulation techniqueJin et al., 2020
- Document ID
- 6080516883882356497
- Author
- Jin H
- Han F
- Sang Y
- Publication year
- Publication venue
- Mechanical Systems and Signal Processing
External Links
Snippet
Maintenance optimization of multi-state systems is a research topic of practical significance for various manufacturing systems. Nevertheless, design of satisfactory maintenance strategies remains a challenging problem due to the complex industrial field and large …
- 238000000034 method 0 title abstract description 53
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Jin et al. | An optimal maintenance strategy for multi-state deterioration systems based on a semi-Markov decision process coupled with simulation technique | |
Du et al. | Research and application of a novel hybrid forecasting system based on multi-objective optimization for wind speed forecasting | |
Xue et al. | Partial connection based on channel attention for differentiable neural architecture search | |
Wang et al. | A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm | |
Choi et al. | Space–time reduced order model for large-scale linear dynamical systems with application to Boltzmann transport problems | |
Yin et al. | An effective secondary decomposition approach for wind power forecasting using extreme learning machine trained by crisscross optimization | |
Zhang et al. | Wind speed forecasting using a two-stage forecasting system with an error correcting and nonlinear ensemble strategy | |
Dong et al. | Wind power prediction based on recurrent neural network with long short-term memory units | |
Zhang et al. | Short-term load forecasting using recurrent neural networks with input attention mechanism and hidden connection mechanism | |
CN106372660A (en) | Spaceflight product assembly quality problem classification method based on big data analysis | |
Guan et al. | Ultra-short-term wind power prediction method combining financial technology feature engineering and XGBoost algorithm | |
Liu et al. | Super multi-step wind speed forecasting system with training set extension and horizontal–vertical integration neural network | |
CN110610019A (en) | A Dynamic Analysis Method for Markovian Jump Systems with Partially Unknown Transition Probabilities | |
Zhao et al. | Spatial correlation learning based on graph neural network for medium-term wind power forecasting | |
Xiao et al. | Short-term residential load forecasting with baseline-refinement profiles and bi-attention mechanism | |
Liu et al. | Hourly traffic flow forecasting using a new hybrid modelling method | |
CN104408531B (en) | A kind of uniform dynamic programming method of multidimensional multistage complicated decision-making problems | |
Sun et al. | A survey on spatio-temporal series prediction with deep learning: taxonomy, applications, and future directions | |
Lee et al. | Wind prediction based on general regression neural network | |
Xu et al. | Research on Load Forecasting Based on CNN-LSTM Hybrid Deep Learning Model | |
Yang et al. | A new multi-objective ensemble wind speed forecasting system: Mixed-frequency interval-valued modeling paradigm | |
CN101976840B (en) | Network topology analysis method of power system based on quasi-square of adjacency matrix | |
Tan et al. | Passenger Flow Prediction of Integrated Passenger Terminal Based on K‐Means–GRNN | |
Ceci et al. | Innovative power operating center management exploiting big data techniques | |
Wang et al. | The Application Research of Deep Learning Based Method for Short-term Wind Speed Forecasting |