A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
A comprehensive review of digital twin—part 2: roles of uncertainty quantification and optimization, a battery digital twin, and perspectives
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting
Wind energy is one of the most widely used and fastest-growing renewable energy. Wind
speed prediction is an efficient way to rationally dispatch wind power generation and ensure …
speed prediction is an efficient way to rationally dispatch wind power generation and ensure …
A review on AI for smart manufacturing: Deep learning challenges and solutions
Artificial intelligence (AI) has been successfully applied in industry for decades, ranging from
the emergence of expert systems in the 1960s to the wide popularity of deep learning today …
the emergence of expert systems in the 1960s to the wide popularity of deep learning today …
Remaining useful life prediction of bearings based on convolution attention mechanism and temporal convolution network
H Wang, J Yang, R Wang, L Shi - Ieee Access, 2023 - ieeexplore.ieee.org
The prediction of the remaining useful life (RUL) of bearings is of great significance for
reducing cost and increasing efficiency of mechanical equipment and ensuring healthy …
reducing cost and increasing efficiency of mechanical equipment and ensuring healthy …
Joint training of a predictor network and a generative adversarial network for time series forecasting: A case study of bearing prognostics
The lack of bearing run-to-failure data has been one of the challenges in developing and
practically implementing robust bearing prognostics models. This paper proposes a new …
practically implementing robust bearing prognostics models. This paper proposes a new …
Digital twins for the designs of systems: a perspective
The design and operation of systems are conventionally viewed as a sequential decision-
making process that is informed by data from physical experiments and simulations …
making process that is informed by data from physical experiments and simulations …
Bearing anomaly detection in an air compressor using an LSTM and RNN-based machine learning model
Smart systems such as data-driven machine health monitoring are emerging as powerful
technology for advanced manufacturing as a result of the availability of low-cost sensors …
technology for advanced manufacturing as a result of the availability of low-cost sensors …
Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon
This study aims to enhance diagnostic capabilities for optimising the performance of the
anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) …
anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) …
A decoupled network with variable graph convolution and temporal external attention for long-term multivariate time series forecasting
Y Liu, Z Huang, F Zhang, X Zhang - Expert Systems with Applications, 2025 - Elsevier
In recent years, long-term multivariate time series forecasting has become increasingly
important in domains such as energy, transportation, and healthcare. Existing research …
important in domains such as energy, transportation, and healthcare. Existing research …