DeepFilter: A Transformer-Style Framework for Accurate and Efficient Process Monitoring

DeepFilter is a modified AI framework based on Transformers (a type of neural network architecture) designed to monitor industrial processes more accurately and efficiently. Standard Transformers use self-attention (a mechanism where the model weighs the importance of different parts of input data), but this approach struggles with process monitoring because it doesn't capture meaningful patterns in logs and requires a lot of computation. DeepFilter replaces the self-attention layer with an efficient filtering layer that better identifies long-term patterns while using less computing power.