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Deep reinforcement learning holds significant potential for application in industrial decision-making, offering a promising alternative to traditional physical models. However, its black-box learning approach presents challenges for real-world and safety-critical systems, as it lacks interpretability and explanations for the derived actions. Moreover, a key research question in deep reinforcement learning is how to focus policy learning on critical decisions within sparse domains. This paper introduces a novel approach that combines probabilistic modeling and reinforcement learning, providing interpretability and addressing these challenges in the context of safety-critical predictive maintenance. The methodology is activated in specific situations identified through the input–output hidden Markov model, such as critical conditions or near-failure scenarios. To mitigate the challenges associated with deep reinforcement learning in safety-critical predictive maintenance, the approach is initialized with a baseline policy using behavioral cloning, requiring minimal interactions with the environment. The effectiveness of this framework is demonstrated through a case study on predictive maintenance for turbofan engines, outperforming previous approaches and baselines, while also providing the added benefit of interpretability. Importantly, while the framework is applied to a specific use case, this paper aims to present a general methodology that can be applied to diverse predictive maintenance applications. • Segmentation of the state–space through probabilistic modeling. • Initializing baseline policy for guided exploration using Behavioral Cloning (BC). • Identifying segmented abnormal states with probabilistic modeling and DRL. • Interpreting operating conditions using human feedback, probabilistic model, and DRL. • Enhanced cross-validation methods for predictive maintenance benchmarking. [ABSTRACT FROM AUTHOR]

Online output prediction is an indispensable part of any model predictive control implementation. For several application scenarios, operating conditions may change quite often, while designing the data collection process may not be an option. To this end, this study introduces a supervisory output prediction scheme, tailored specifically for input–output stable bilinear systems, that intends on automating the process of selecting the most appropriate prediction model during runtime. The selection process is based upon a reinforcement‐learning scheme, where prediction models are selected according to their prior prediction performance. An additional selection process is concerned with appropriately partitioning the control‐inputs' domain also to allow for switched‐system approximations of the original bilinear dynamics. The authors show analytically that the proposed scheme converges (in probability) to the best model and partition. They also demonstrate these properties through simulations of temperature prediction in residential buildings. [ABSTRACT FROM AUTHOR]

AbstractIn complex industrial and chemical process control rooms, effective decision-making is crucial for safety and efficiency. The experiments in this paper evaluate the impact and applications of an AI-based decision support system integrated into an improved human-machine interface, using dynamic influence diagrams, a hidden Markov model, and deep reinforcement learning. The enhanced support system aims to reduce operator workload, improve situational awareness, and provide different intervention strategies to the operator adapted to the current state of both the system and human performance. Such a system can be particularly useful in cases of information overload when many alarms and inputs are presented all within the same time window, or for junior operators during training. A comprehensive cross-data analysis was conducted, involving 47 participants and a diverse range of data sources such as smartwatch metrics, eye-tracking data, process logs, and responses from questionnaires. The results indicate interesting insights regarding the effectiveness of the approach in aiding decision-making, decreasing perceived workload, and increasing situational awareness for the scenarios considered. Additionally, the results provide insights to compare differences between styles of information gathering when using the system by individual participants. These findings are particularly relevant when predicting the overall performance of the individual participant and their capacity to successfully handle a plant upset and the alarms connected to it using process and human-machine interaction logs in real-time which resulted in a 95.8% prediction accuracy using hidden Markov model. These predictions enable the development of more effective intervention strategies. [ABSTRACT FROM AUTHOR]