## 1 Introduction  
The Fourth Industrial Revolution (Industry 4.0) represents the integration of digital technologies and automation into industrial processes. Industry 4.0 has a significant impact on maintenance management practices, revolutionizing traditional approaches and introducing new opportunities for more efficient and proactive maintenance strategies [1,2]. The key trigger for the big data approach in asset management has been the Internet of Things (IoT) [3]. Industry 4.0 technologies and features affect maintenance management functions, especially condition-based and predictive maintenance programs [4]. The total productive maintenance (TPM) program has also been improved by Industry 4.0 technologies, which have shown a positive impact on production rates, the overall quality of the manufactured items, and the satisfaction of internal and external customers [5]. On the other hand, Industry 4.0 technologies generate massive data that are dynamic and need a highly complex decision-making process [6]. In addition, uncertainty arises in the process of forming emerging technologies [7]. Reliability-centered maintenance (RCM) and risk-based maintenance (RBM) programs are among the most well-known maintenance concepts for critical asset-dependent sectors, for example, oil and gas, nuclear, and aviation [8, 9, 10]. Industry 4.0 technologies have also affected RCM and RBM [11, 12, 13, 14, 15]. [PERSON] et al. [11] also explored how risk-based maintenance and predictive maintenance can be integrated. [PERSON] [12] and [PERSON] [16]both explored the main updates, due to Industry 4.0 technologies, to be considered in the well-known maintenance base study (which is for the risk-based maintenance program). A reference architecture for an intelligent maintenance management system has been developed to comply with Industry 4.0 visions and requirements [13]. [PERSON] [14] found that with equipment degradation, the reliability of the equipment is often overestimated under the condition-based maintenance (CBM) strategy and delays maintenance work, while the risk-based predictive maintenance strategy provides better economic performance than CBM. [PERSON] and [PERSON] [15] proposed a framework for adopting prescriptive analytics into risk-based maintenance of naval vessel. The advent of Industry 4.0 technologies, such as digitalization and automation, significantly influences the adoption of new practices that enable the collection and analysis of large amounts of data, facilitating smart decision making and resource efficiency [3]. From a risk-based maintenance point of view, the collection of failure data has been revolutionized in the Industry 4.0 era, affecting the consequences and likelihoods used in risk-based maintenance programs [11]. In addition, probabilistic risk assessment techniques, such as fault tree analysis (FTA), event tree analysis (ETA), bow tie analysis, or Bayesian networks (BNs), all rely heavily on historical data, failure data, and probabilities [17]. Thus, the impact of Industry 4.0 technologies on reliability-centered maintenance (RCM) and risk-based maintenance (RBM) programs needs to be further clarified [4; 5]. During the last 10 years, several case studies have been performed in the industry where existing maintenance systems were analyzed. Several emerging practices are observed. For example, [PERSON] [18], [PERSON] and [PERSON] [19], and [PERSON] [20] studied the potential of condition-based maintenance concept in the risk-based maintenance program for safety valves. [PERSON] [21] and [PERSON] [22] highlighted that adaptation of the PdM concept to the RBM program requires the design of PdM-ready equipment, which means that a major change is required in the work process in the project phase. [PERSON] [23] studied the cost effectiveness of PdM retrofitted equipment against PdM-ready equipment. [PERSON] [24] highlighted that predictive maintenance analytics shall be enabled by designing PdM-ready equipment.