In industrial production, it is of great importance to have high availability in its production equipment. Well-functioning maintenance is a significant factor for a high level of availability. This can be achieved by minimizing the number of reactive maintenance stops and optimizing scheduled maintenance. New methods for predictive maintenance provide a good opportunity for this, but most technologies that are available today are designed for individual sub-systems and they are rarely designed for a complex, interconnected machine. In the process industry, raw materials are rocessed into a finished product in a continuous flow through several subsystems and if one subsystem stops, the entire process flow stops. For these processes, it is more important to optimize the maintenance efforts for subsystems so maintenance can take place synchronized. This paper describes a method of supervised control that includes maintenance aspects; health parameters indicating deterioration are included in a MIMO controller. The method is verified in a simulation of a rolling mill with three rollers. The results show that it is possible to optimize the whole complex process including several subprocesses by using a health parameter as a control parameter and broadening the controllability of the system by dividing the workload in a way that all the subsystems reach the desired degradation level for maintenance in a desired optimum time. 

Efficient production planning hinges on reducing costs and maintaining output quality, with machine degradation management as a key factor. The traditional approaches to control this degradation face two main challenges: high costs associated with physical modeling and a lack of physical interpretability in machine learning methods. Addressing these issues, our study presents an innovative solution focused on controlling the degradation, a common cause of machine failure. We propose a method that integrates machine degradation as a virtual state within the system model, utilizing relevance vector machine-based identification designed in a way that offers physical interpretability. This integration maximizes the machine’s operational lifespan. Our approach merges a physical machine model with a physically interpretable data-driven degradation model, effectively tackling the challenges in physical degradation modeling and accessibility to the system disturbance model. By embedding degradation into the system’s state-space model, we simplify implementation and address stability issues. The results demonstrate that our method effectively controls degradation and significantly increases the machine’s mean time to failure. This represents a significant advancement in production planning, offering a cost-effective and interpretable method for managing machine degradation.

Controlling machine degradation enhances the accuracy of the remaining-useful-life estimation and offers the ability to control failure type and time. In order to achieve optimal degradation control, the system controller must be cognizant of the consequences of its actions by considering the degradation each action imposes on the system. This article presents a method for designing cost-aware controllers for linear systems, to increase system reliability and availability through degradation control. The proposed framework enables learning independent of the system's physical structure and working conditions, enabling controllers to choose actions that reduce system degradation while increasing system lifetime. To this end, the cost of each controller's action is calculated based on its effect on the state of health. A mathematical structure is proposed, to incorporate these costs into the cost function of the linear-quadratic controller, allowing for optimal feedback for degradation control. A simulation validates the proposed method, demonstrating that the optimal-control method based on the proposed cost function outperforms the linear-quadratic regulator in several ways.

Controlling the machine’s state of health (SoH) increases the accuracy of the remaining useful life estimation and enables the control of the failure time by keeping the system operational until the desired maintenance time is reached. To achieve system reliability through SoH control, the system controller must consider the impact of its actions on other parameters, such as degradation. This article proposes a structure for designing degradation-aware controllers for systems with available physical models. A system using this approach can learn autonomously, irrespective of the system’s physical structure and degradation model, and opt for control actions that enhance the system’s reliability and availability. To this end, first, a method is proposed to compute the cost associated with the actions taken by the controller. Second, a new cost function is introduced that incorporates the costs associated with degradation into the cost function utilized in model predictive control. In the third step, dynamic programming and deterministic scheduling are used to calculate the optimal action based on the defined cost function. Finally, the proposed control method is validated through simulation, demonstrating its ability to effectively manage machine degradation and achieve optimal performance according to production and maintenance plans.

This study introduces a comprehensive framework designed to enhance production efficiency by integrating maintenance strategies, energy costs, and production specifications. This integration is achieved through a novel empirical method for estimating state–action costs, suitable for both machines with measurable and non-measurable states-of-health. We address the challenge of under-determination in state–action cost optimization by employing a k-means clustering approach, ensuring robustness and applicability. Utilizing an adapted SARSA algorithm, our framework optimally controls shop-floor machinery to minimize the global cost function. The efficacy of the state–action cost estimation method is validated using NASA’s C-MAPSS dataset. Additionally, the optimization strategy is further corroborated through its successful implementation in an autonomous mining cart model on the shop floor. Our results highlight the framework’s ability to optimize machine lifetime and production processes effectively, providing tailored solutions that adapt to varying operational conditions without depending on predefined machine degradation models and costs.