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This paper proposes a generic algorithm for industries with degrading and/or failing equipment with significant consequences. Based on the specifications and the real-time status of the production line, the algorithm provides decision support to machinery operators and manufacturers about the appropriate lifetime extension strategies to apply, the optimal time-frame for the implementation of each and the relevant machine components. The relevant recommendations of the algorithm are selected by comparing smartly chosen alternatives after simulation-based life cycle evaluation of Key Performance Indicators (KPIs), considering the short-term and long-term impact of decisions on these economic and environmental KPIs. This algorithm requires various inputs, some of which may be calculated by third-party algorithms, so it may be viewed as the ultimate algorithm of an overall Decision Support Framework (DSF). Thus, it is called “DSF Core”. The algorithm was applied successfully to three heterogeneous industrial pilots. The results indicate that compared to the lightest possible corrective strategy application policy, following the optimal preventive strategy application policy proposed by this algorithm can reduce the KPI penalties due to stops (i.e., failures and strategies) and production inefficiency by 30–40%.

This paper introduces a novel approach to anomaly-based failure forecasting that jointly optimizes both the ensemble function and the anomaly threshold used for decision making. Unlike conventional methods that apply fixed or classifier-defined thresholds, the proposed framework simultaneously tunes the threshold of the failure probability or anomaly score and the parameters of an ensemble function that integrates multiple machine learning models—specifically, Random Forest and Isolation Forest classifiers trained under diverse preprocessing configurations. The distinctive contribution of this work lies in introducing a weighted mean ensemble function, whose coefficients are co-optimized with the anomaly threshold using a global optimization algorithm, enabling adaptive, data-driven decision boundaries. The method is designed for predictive maintenance applications and validated using sensor data from three industrial domains: aluminum anode production, plastic injection molding, and automotive manufacturing. The experimental results demonstrate that the proposed combined optimization significantly enhances forecasting reliability, improving the Matthews Correlation Coefficient by up to 6.5 percentage units compared to previous approaches. Beyond its empirical gains, this work establishes a scalable and computationally efficient framework for integrating threshold and ensemble optimization in real-world, cross-industry predictive maintenance systems.

Energy demand and generation are common variables that need to be forecast in recent years, due to the necessity for energy self-consumption via storage and Demand Side Management. This work studies multi-step time series forecasting models for energy with confidence intervals for each time point, accompanied by a demand optimization algorithm, for energy management in partly or completely isolated islands. Particularly, the forecasting is performed via numerous traditional and contemporary machine learning regression models, which receive as input past energy data and weather forecasts. During pre-processing, the historical data are grouped into sets of months and days of week based on clustering models, and a separate regression model is automatically selected for each of them, as well as for each forecasting horizon. Furthermore, the multi-criteria optimization algorithm is implemented for demand scheduling with load shifting, assuming that, at each time point, demand is within its confidence interval resulting from the forecasting algorithm. Both clustering and multiple model training proved to be beneficial to forecasting compared to traditional training. The Normalized Root Mean Square Error of the forecasting models ranged approximately from 0.17 to 0.71, depending on the forecasting difficulty. It also appeared that the optimization algorithm can simultaneously increase renewable penetration and achieve load peak shaving, while also saving consumption cost in one of the tested islands. The global improvement estimation of the optimization algorithm ranged approximately from 5% to 38%, depending on the flexibility of the demand patterns.

This paper explores the energy-intensive cement industry, focusing on a plant in Greece and its mill and kiln unit. The data utilized include manipulated, non-manipulated, and uncontrolled variables. The non-manipulated variables are computed based on the machine learning (ML) models and selected by the minimum value of the normalized root mean square error (NRMSE) across nine (9) methods. In case the distribution of the data displayed in the user interface changes, the user should trigger the retrain of the AI models to ensure their accuracy and robustness. To form the objective function, the expert user should define the desired weight for each manipulated or non-manipulated variable through the user interface (UI), along with its corresponding constraints or target value. The user selects the variables involved in the objective function based on the optimization strategy, and the evaluation is based on the comparison of the optimized and the active value of the objective function. The differential evolution (DE) method optimizes the objective function that is formed by the linear combination of the selected variables. The results indicate that using DE improves the operation of both the cement mill and kiln, yielding a lower objective function value compared to the current values.

This paper concerns an application of recurrent neural networks to text synthesis in the word level, with the help of keywords. First, a Parts Of Speech tagging library is employed to extract verbs and nouns from the texts used in our work, a part of which are then considered, after automatic eliminations, as the aforementioned keywords. Our ultimate aim is to train a recurrent neural network to map the keyword sequence of a text to the entire text. Successive reformulations of the keyword and full-text word sequences are performed, so that they can serve as the input and target of the network as efficiently as possible. The predicted texts are understandable enough, and the model performance depends on the problem difficulty, determined by the percentage of full-text words that are considered as keywords, that ranges from 1/3 to 1/2 approximately, the training memory cost, mainly affected by the network architecture, as well as the similarity between different texts, which determines the best architecture.

Sustainable manufacturing is of great importance in today’s world. In manufacturing, keep industrial equipment well-functioning is important because failure of equipment leads to significant financial and production losses. In addition, disposal of such failed equipment is both costly and environmentally unfriendly and does not recover any residual value. This raises the need to adopt methods and means that help extending the life of equipment and reduce waste of material. This paper presents a digital toolkit of cost model to estimate and understand the costs to be incurred when applying life extension strategy for industrial equipment. It is meant to be integrated with other tools and methodologies to enable end-users to perform optimal decision-making regarding which life extension strategy (e.g., remanufacturing, refurbishment, repair) to implement for large industrial equipment that is towards its end-of-life or needs maintenance, taking into account criteria such as cost, machine performance, and energy consumption. The cost model developed integrates a combination of parametric costing and activity-based costing methods to per form cost estimation. It has been implemented in an Excel-based Macro platform. A case study with application scenarios has been conducted to demonstrate the application of the cost model to optimize life extension strategies for industrial equipment. Finally, conclusions on the developed cost model have been reported.