# A Semantic Model in the Context of Maintenance: A Predictive Maintenance Case Study
## 1 Introduction  
The sustainability and efficiency of manufacturing systems depend on the utilization of technologies that facilitate the smart, sustainable digital factories of the future [1]. In particular, the design and deployment of predictive, prescriptive maintenance strategies that achieve increased reliability in manufacturing systems are of significant importance in fostering competitive advantage among manufacturing companies [2; 3]. Manufacturing companies now face immense pressures to continuously improve the performance and reliability of their assets; at the same time, they are required to lower costs and ensure safety [4]. Furthermore, the emergence and advancement of modern, Industry 4.0 technologies--such as artificial intelligence (AI) and the industrial internet of things (IoT)--offer companies the possibility to accurately predict machines' statuses and thus optimize maintenance activities, achieving far more than conventional reactive maintenance [5]. The future of maintenance, operations, and asset management requires manufacturing companies to move toward more proactive and optimized maintenance [6]. Further, researchers and practitioners should increase their focus on the synergies between key business processes and maintenance activities across the lifecycle of key manufacturing assets. They also need to leverage the opportunities provided by advanced maintenance solutions to optimize the cost efficiency and performance of these valuable assets [7]. The most basic and inefficient approach to maintenance is reactive maintenance, which runs assets to failure and only reacts when the equipment breaks down. Hence, this approach is often implemented with manufacturing assets that have minimal replacementor repair costs. Such assets are not critical and do not have any significant or immediate impact on plant availability or safety [8]. The second level of maintenance is preventive maintenance (PM), which follows a fixed maintenance schedule with predefined time intervals coupled with best practices and the recommendations of manufacturers [9]. Condition-based maintenance monitors the physical condition of manufacturing equipment and operates when key and measurable performance metrics indicate critical issues before their occurrence [10]. Finally, the predictive maintenance (PdM) strategy studied in this research applies to more critical, complex assets and provides a data-driven analysis to predict a piece of equipment's operating profile during different process conditions [11]. PdM thus uses data records and Industry 4.0 technologies and methods to determine the underlying issues that affect the reliability and performance of manufacturing assets [12]. To improve scheduled PM, [PERSON] et al. designed a sequential \"predict, then optimize\" approach to bridge PM with a predictive approach [13]. In the same context, a novel hybrid approach is implemented to assess and test real-world and synthesized information through a combination of statistical and symbolic AI to lay the foundations for Industry 4.0 PdM [14]. The PdM functions by accessing data relevant to the status and condition of machinery and focuses on the knowledge to be gained from the data [15; 16]. A PdM mechanism hence aims to improve the reliability of production assets by identifying potential problems and providing early warning of issues and critical events before the occurrence of catastrophic failures in the production run [17; 18]. Advanced technologies in modern industry collect massive volumes of data from a plethora of sources, such as processes, machines, components, and documents [19]. This also applies to the case of PdM. Hence, to provide access to all these data in a standard and structured way, researchers and practitioners must design and develop a semantic model of maintenance entities to build a reference ontology for maintenance [20]. Semantic enrichment can be achieved by exploring and filtering cross-sectoral information related to maintenance [21].