## 1 Introduction  
Today, we live in a world that could only be described as science fiction. With the rise of the Internet of Things (IoT), everything has become smarter and connected in a way that resembles the industrial revolution of the western world 35 years ago [1]. The modern industry must adapt and maintain maximum efficiency while facing new challenges such as high reliability, low environmental risk, and human safety. This has led to the emergence of the digital economy, also known as Industry 4.0, which focusses on the logic that governs the entire manufacturing process rather than just technological advancements in manufacturing processes. The introduction of cyber-physical systems (CPS) has made each production resource (e.g., machines, equipment, and operators) a part of the \"cyber production system\" connected to the IoT. However, this new production scenario also presents challenges such as the need to build and standardise CPSs and the implementation of new communication standards among CPSs [2]. On the other hand, this new industrial paradigm also offers numerous opportunities. A network of CPSs can help achieve goals such as product customisation, a reduction in production lead times, a more efficient control of manufacturing processes, and an increased product quality and reliability. In this CPS configuration, which aligns with the Industry 4.0 paradigm, machines and production equipment are connected at a higher level of awareness, allowing them to become aware of their process capabilities. One area of focus in auxiliary processes is maintenance activity, which \"combines various methods, tools, and techniques to reduce maintenance costs while increasing equipment reliability, availability, and safety\" [3]. In fact, maintenance stops, whether expected or unexpected, account for a significant portion of operating expenses in various industry sectors, with nearly 30% of maintenance costs being attributed to inefficient policies (e.g., unscheduled downtime due to sudden failures) [4]. Industry 4.0 technologies enable the development of new maintenance strategies, such as \"smart maintenance\". Since 1950, various maintenance logics have been developed, including time-based maintenance (TBM). TBM involves regularly scheduled preventive maintenance in order to slow down the deterioration process of components. The TBM maintenance strategy assumes that the system's failure distribution is known either experimentally or statistically [5]. However, the real evolution of maintenance has occurred in the past three decades with the emergence of condition-based maintenance (CBM), which is a \"decision-making strategy that enables real-time diagnosis of failure and prognosis of future equipment health\" [6]. By integrating sensors into operating machines, the system's health state can be quantified in a proactive manner. It is desirable for the CBM policy to evolve from a diagnostic approach based on fault detection to a prognostic approach based on degradation and fault prediction. The \"prognostic process\" allows for the prediction and anticipation of machine failure, resulting in numerous benefits in the fields of safety, economics, and resource management [3]. Prognostics and health management (PHM) \"refers specifically to the phase involved with predicting future behaviour, including remaining useful life (RUL), in terms of current operating state and the scheduling of required maintenance actions to maintain system health\" [7]. There have been many algorithms developed for predicting the health state of a machine within the context of PHM [6; 8; 9]. However, to the best of our knowledge, only a limited number of approaches are currently available for estimating the probability that a monitored machine will successfully complete a predefined workload by integrating data about its historical behaviour and current health state. While the number of these algorithms has significantly increased in recent years, most of them are only suitable for machines operating under constant workload conditions.