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Found 4 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.
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.
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.
