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Found 7 papers, 0 papers with code
Condition Monitoring with Incomplete Data: An Integrated Variational Autoencoder and Distance Metric Framework
Condition monitoring of industrial systems is crucial for ensuring safety and maintenance planning, yet notable challenges arise in real-world settings due to the limited or non-existent availability of fault samples.
Extended Reality for Enhanced Human-Robot Collaboration: a Human-in-the-Loop Approach
The rise of automation has provided an opportunity to achieve higher efficiency in manufacturing processes, yet it often compromises the flexibility required to promptly respond to evolving market needs and meet the demand for customization.
A mathematical model for simultaneous personnel shift planning and unrelated parallel machine scheduling
This paper addresses a production scheduling problem derived from an industrial use case, focusing on unrelated parallel machine scheduling with the personnel availability constraint.
Intelligent Condition Monitoring of Industrial Plants: An Overview of Methodologies and Uncertainty Management Strategies
This paper provides an overview of intelligent condition monitoring and fault detection and diagnosis methods for industrial plants with a focus on the open-source benchmark Tennessee Eastman Process (TEP).
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
This paper serves as a valuable resource for researchers to assess the current state of DRL-based machine scheduling and identify research gaps.
Anomaly Detection in Automated Fibre Placement: Learning with Data Limitations
The framework employs blob detection on this map to locate manufacturing defects.
Synthesizing Rolling Bearing Fault Samples in New Conditions: A framework based on a modified CGAN
The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions.
