Large scale foundation models for intelligent manufacturing applications: a survey

Journal of Intelligent Manufacturing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 4, 2025

Язык: Английский

Helicopter Turboshaft Engines’ Neural Network System for Monitoring Sensor Failures

Sensors, Год журнала: 2025, Номер 25(4), С. 990 - 990

Опубликована: Фев. 7, 2025

An effective neural network system for monitoring sensors in helicopter turboshaft engines has been developed based on a hybrid architecture combining LSTM and GRU. This enables sequential data processing while ensuring high accuracy anomaly detection. Using recurrent layers (LSTM/GRU) is critical dependencies among time series analysis identification, facilitating key information retention from previous states. Modules such as SensorFailClean SensorFailNorm implement adaptive discretization quantisation techniques, enhancing the input quality contributing to more accurate predictions. The demonstrated detection at 99.327% after 200 training epochs, with reduction loss 2.5 0.5%, indicating stability processing. A algorithm incorporating temporal regularization combined optimization method (SGD RMSProp) accelerated convergence, reducing 4 min 13 s achieving an of 0.993. Comparisons alternative methods indicate superior performance proposed approach across metrics, including 0.993 compared 0.981 0.982. Computational experiments confirmed presence highly correlated sensor method's effectiveness fault detection, highlighting system's capability minimize omissions.

Язык: Английский

Model-driven deep learning for joint control and decision-making in failure-prone circular multistage manufacturing systems

International Journal of Computer Integrated Manufacturing, Год журнала: 2025, Номер unknown, С. 1 - 22

Опубликована: Апрель 23, 2025

Язык: Английский