## 2 Review of Current Maintenance Strategies
In addition, the above-mentioned calculations did not take into account the additional profit resulting from the increase in the value of system security and the company's image. In addition to purely economic benefits, a predictive maintenance strategy supports companies in implementing the environmental and social dimensions of sustainable production [32; 33; 34; 35; 2]. In the context of new challenges of intelligent production, PdM is becoming an attractive paradigm of modern management of production equipment, enabling early detection of failures using predictive methods and data from the entire lifetime of the monitored devices. Hence,  
Figure 1: The structure of an artificial neural network (ANN). it is starting to be used more and more often in various industry sectors, including the automotive and furniture industries [20], aircraft sectors [37], and marine industries [38]. The evolution of the approach from preventive maintenance to predictive maintenance (PdM) is progressing thanks to the introduction of new technologies: Digital Twin (DT), Internet of Things (IoT) and Smart Manufacturing (IM). Nevertheless, any organization seeking to establish a PdM system should follow a plan that provides detailed guidance on the steps to be taken to effectively achieve the goal. This plan is a roadmap that is defined as a tool for interdisciplinary teams to help determine the sequence of planned activities in a specific application [39] considering short, medium, and long-term strategies to foster the implementation of PdM [40]. The existing literature offers research in the comprehensive optimization of maintenance transformation and building a predictive approach [41; 42; 43; 44; 45]. [PERSON] et al. [41] developed PdM framework and proposed an implementation roadmap. The proposed framework for the development of PdM starts with the knowledge gained while studying the literature and analyzing similar industrial projects. In order to effectively implement a PdM system based on small data sets or big data analytics, the authors propose a road map based on fourteen steps. [PERSON] et al. [46] proposed six-step PdM implementation roadmap: (1) Identify asset(s) for PdM; (2) Establish data sources; (3) Analyze the failure mode (e.g., effects); (4) Select and implement monitoring technologies; (5) Develop predictive algorithms and key metrics (e.g., time to failure) and (6) Generation and implementation of a predictive maintenance schedule. In addition, the authors have defined the requirements for each step and enablers, and they have also indicated the requirements that the IT system should meet. This system must be able to: collect and store required data, process and use the data and generate practical results. [PERSON] and [PERSON] [42] proposed a structured approach to implementing PdM strategies in the industry so as to result in reduced maintenance and resource costs. [PERSON] et al. [29] presents four stages of methodology for PdM applications dedicated to industrial companies (MEDADEK-PdM) by combining domain knowledge with advanced data-driven techniques. Regardless of which PdM delivery route an enterprise chooses, in order to predict upcoming failures, downtimes or other critical events for maintaining production, it is necessary to analyze previously collected data. It also requires continuous monitoring of the current state by collecting real-time data. Data should be gathered to the beginning of the road. It is necessary to analyze them. Literature data indicate ML, Industrial Edge Computing, Algorithmic Programming and AI as the most suitable to achieve this goal [47; 48; 49; 50; 51]. A lot of research has been conducted in the last decade and many attempts have been made to apply artificial intelligence (AI) techniques to modeling and managing maintenance [52; 53; 54]. AI is changing the technologies underlying the solution to many problems related to modeling and controlling machines within the paradigm of Industry 4.0.