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 aims to investigate the deployment and maintenance of Digital Twin (DT) in an industrial ring rolling manufacturing process. Three parameters were analyzed (i) data cleaning and processing, (ii) DT development and updating, and (iii) secure communication. A process-based DT model was developed using iba-ag and Python, prioritizing security throughout the process. The study underscores the significance of secure DT system, with particular attention to the challenges of real-time updates affected by process operations and environmental factors. This emphasizes the adaptability of the DT while acknowledging the expertise of process operators. The findings promote a deeper understanding of DT implementation, particularly for process developers, maintenance personnel, and operators. The research highlights the importance of security in DT systems, especially when dealing with industrial data influenced by production and environmental factors. Therefore, by focusing on security and deployment in an open-source environment, this study contributes to the practical use of DT systems for predictive maintenance and process optimization within the industrial settings.

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.

Construction and manufacturing workers undertake physically laborious activities which put them at risk of developing serious musculoskeletal disorders (MSDs). In the EU, millions of workers are being affected by workplace-related MSDs, inflicting huge financial implications on the European economy. Besides that, increased health problems and financial losses, severe shortages of skilled labor also emerge. The work aims to create awareness and accelerate the adoption of exoskeletons among SMEs and construction workers to reduce MSDs. Large-scale manufacturers and automobile assemblers are more open to adopt exoskeletons, however, the use of exoskeletons in small and medium enterprises (SMEs) is still not recognized. This paper presents an experimental study demonstrating the advantages of different exoskeletons while performing workers’ tasks. The study illustrates how the use of certain upper and lower body exoskeletons can reduce muscle effort. The muscle activity of the participants was measured using EMG sensors and was compared while performing designated tasks. It was found that up to 60% reduction in human effort can be achieved while performing the same tasks using exoskeletons. This can also help ill workers in rehabilitation and putting them back to work. The study concludes with pragmatic recommendations for future exoskeletons.

The capability of estimating future maintenance needs in advance and in a timely manner is a prerequisite for reliable manufacturing with high availability in a production unit. Additionally, conducting planned maintenance efforts regularly and prematurely increases the service lifetimes and utilization rates of parts, which leads to more sustainable production. The benefits of predictive maintenance are obvious, but introducing it into a facility poses various challenges. In this study, digital twins of well-functioning machines are used for predictive maintenance. The discrepancies between each physical unit and its digital twin are used to detect the maintenance needs. A thorough evaluation of the method over a period of 18 months by comparing digital twin detection results with maintenance and control system logs shows promising results. The method is successful in detecting discrepancies, and the paper describes the techniques that are used. However, not all discrepancies are related to the maintenance needs, and the evaluation identifies and discusses the most common sources of error. These are often the results of human interaction, such as parameter changes, maintenance activities and component replacement. 

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.

Communication systems are a key element for the industry 4.0 revolution, where the remote access to the machinery is a fundamental part for the automation of tasks related to monitoring and control of the different industrial processes. There is an increasing interest in performing such communications using a wireless medium, as they offer several advantages as a lower cost, greater flexibility or the ability to operate in moving elements. However, existing works have showed that the achievable performance in the sub-6 GHz frequency bands is insufficient to cope with all the requirements, which motivated the analysis of the millimeter wave spectrum for these use cases. Industrial environments present a harsh condition for electromagnetic wave propagation, where the abundance of reflective surfaces can present difficulties to properly exchange information. Thus, a thorough analysis and characterization of the propagation through this kind of environment is necessary to develop protocols and standards accordingly. This work provides the results of measurements carried out in two industrial facilities, which are a university workshop and a pit oven building from a steel company. Metrics of the results are computed and discussed as well, where a significantly larger losses can be seen for the pit oven measurements compared to other industrial scenarios. 

Predicting the failure and estimating the machine’s state of health is information that supports the production planning and maintenance management systems to increase productivity and reduce maintenance and downtime costs. However, controlling the degradation in the machines will improve the system’s reliability and resilience and make high-level decisions more accurate and reliable. To control the degradation in the machines, time should be included in the cost function as a variable, which alters the markovian properties of the system dynamic. In this article, we include the degradation cost in the quadratic cost function of the infinite horizon controller and calculate the optimal feedback according to the dynamics of the degradation using dynamic programming. It will be shown that the infinite horizon control will convert to the finite horizon, and the controller will be able to control the degradation according to the desired degradation at the desired time. In the end, with the help of simulation, we show that the degradation controller can control the degradation in the MIMO systems.

Predicting the failure and estimating the machine's state of health is information that supports the production planning and maintenance management systems to increase productivity and reduce maintenance and downtime costs. However, controlling the degradation in the machines will improve the system's reliability and resilience and make high-level decisions more accurate and reliable. To control the degradation in the machines, time should be included in the cost function as a variable, which alters the markovian properties of the system dynamic. In this article, we include the degradation cost in the quadratic cost function of the infinite horizon controller and calculate the optimal feedback according to the dynamics of the degradation using dynamic programming. It will be shown that the infinite horizon control will convert to the finite horizon, and the controller will be able to control the degradation according to the desired degradation at the desired time. In the end, with the help of simulation, we show that the degradation controller can control the degradation in the MIMO systems.