## 4 Advancements in Industry 4.0 and Pandemic Management
### Predictive Maintenance and Prognosis  
Predictive maintenance [24; 62; 63; 64; 65] and prognosis play a crucial role in the context of Industry 4.0, facilitating the transition from scheduled-based processes to smart, reactive ones. This review explores several scholarly papers that focus on predictive maintenance methodologies and their applications [66; 67; 68; 69; 49] in the context of Industry 4.0. [PERSON] et al. [70] present an Industry-4.0-based approach for the health assessment of critical assets in the stainless steel industry. The authors propose a predictive model based on a Bayesian filter from the machine learning field to estimate and predict the gradual degradation of machinery, specifically the drums within the heating coilers of Steckel mills. By fusing expert knowledge with real-time information, the model enables informed decisions regarding maintenance operations, contributing to the Industry 4.0 era. [PERSON] et al. [71] delve into emerging trends and strategies for Industry 4.0 during and beyond the COVID-19 pandemic. The book provides multidisciplinary research and insights on advancing technologies and new strategies in business and administrative settings. The authors reflect upon the challenges and opportunities brought about by the pandemic, and how it has accelerated the adoption of Industry 4.0 technologies. [PERSON] et al. [72] propose a generic end-to-end predictive maintenance methodology for estimating the time-to-failure of industrial machines. The methodology includes feature extraction based on various sensor data types, feature transformation and selection techniques, and the training of multiple machine learning classification models. The methodology is evaluated through a real-world case study, demonstrating its effectiveness for time-to-failure prediction. [PERSON] et al. [73] discuss access control strategies for Industry 4.0 manufacturing systems, emphasizing the need for fine-grained access control policies to mitigate emerging cybersecurity threats. The authors evaluate several access control strategies in a simulation experiment, considering various attack scenarios, and outline a method for automatic policy generation based on engineering data. [PERSON] et al. [74] propose PMMI 4.0, a predictive maintenance model for Industry 4.0, which utilizes a data-driven LSTM model for remaining useful life (RUL) estimation. The model is integrated with a solution called PMS4 MMC to support an optimized maintenance schedule plan for multiple machine components. The effectiveness of the proposed solution is demonstrated through a real-world industrial case. In their paper, [PERSON] et al. [75] present a flexible architecture for predictive maintenance systems, incorporating expert knowledge and matrix-profile anomaly detection for advanced failure prediction. The system aims to enable timely corrective actions and prevent material and environmental damages in the context of Industry 4.0. The integration of continuous monitoring and healthiness checks allows for accurate predictions of equipment failures and facilitates proactive decision making by the maintenance team. The proposed conceptual model showcases the potential of combining expert knowledge with anomaly detection techniques, yielding promising results for the implementation of predictive maintenance in various industrial sectors. [PERSON] et al. [76] discuss the application of machine learning and IIoT (industrial Internet of things) for predictive maintenance. The paper explores the potential of ML techniques to analyze data from IIoT tools, such as smart sensors, to predict equipment performance and health. A case study on data from a heat exchanger showcases the advantages of deep learning LSTM models in time series forecasting for predictive maintenance. In conclusion, the papers in this group highlight the importance of predictive maintenance and prognosis in Industry 4.0.