Process Monitoring

Process monitoring aims to automatically detect anomalies and optimize industrial processes by analyzing data streams from various sources, enhancing efficiency and safety. Current research emphasizes the use of deep learning, including deep transfer learning and novel architectures like AttentionMixer, to handle the complexities of high-dimensional, real-time data from diverse sensors (e.g., vision systems, multiple cameras). These advancements are improving the accuracy and interpretability of process monitoring, with applications ranging from additive manufacturing to energy production and chemical processing, leading to more efficient and reliable industrial operations.