The field of fault diagnosis and process management is moving towards leveraging large-scale models and reinforcement learning techniques to improve accuracy and adaptability. This shift enables interactive, interpretable, and actionable insights, enhancing industrial applicability. Notably, the incorporation of uncertain human guidance in reinforcement learning frameworks is showing promise in developing complex model transformations. The use of large-scale audio models and vibration signal alignment is also emerging as a effective approach in fault diagnosis. Some noteworthy papers include: AeroGPT, which proposes a novel framework for aero-engine bearing fault diagnosis using large-scale audio models, achieving exceptional performance with 98.94% accuracy on the DIRG dataset. GymPN, a software library that supports optimal decision-making in business processes using Deep Reinforcement Learning, allowing for easy modeling of desired problems and learning optimal decision policies.