## 1 Introduction
[PERSON] [25] explored the potential of applying the prescriptive maintenance concept for the crude oil pump system. [PERSON] [26] and [PERSON] [27] studied the impact of condition-based maintenance on maintenance personnel and corrective maintenance workload. [PERSON] [28] studied the impact of condition-based maintenance on the scheduled inspection workload. [PERSON] [29] studied how Industry 4.0 technologies enable remote maintenance of unmanned platforms. [PERSON] [30] explored how redundancy can ensure the annual unmanned mode for unmanned platforms. [PERSON] [31] explored the need for predictive analytics to gain value from several datasets and plan maintenance of a hydraulic power unit in a drilling rig. [PERSON] [32] used model-based predictive maintenance to maintain drilling sheaves. [PERSON] [33] explored how condition-based maintenance can be embedded into reliability-centered maintenance. [PERSON] [34], [PERSON] [35], [PERSON] [36], [PERSON] [37], and [PERSON] [38] have studied the need, requirements, and benefits of applied model-based maintenance engineering and built parametric upgradable models instead of rigid documents. [PERSON] [39] indicated the need to digitize the master data for offshore rental tools to build a risk-based maintenance program, as currently there is a lack of collected information on the likelihood and consequences of failures compared to the top-side equipment supported by OREDA. [PERSON] and [PERSON] [40], [PERSON] [41], and [PERSON] [42] have studied spare parts in the context of Industry 4.0, their inventory, supply, sharing, and additive manufacturing. [PERSON] [43] has explored condition-based and predictive maintenance as new concepts required for the RBM program for wind turbine systems. [PERSON] [44] explored the use of argument reality for safety and maintenance training. [PERSON] [45] used simulation modeling to find the most optimal maintenance logistics strategy for an offshore wind farm that applied the RBM program. [PERSON] [46] explored how drones can be adapted within an RBM program to inspect wind turbine blade erosion. [PERSON] [47] studied the condition-based maintenance for high voltage direct current. Most of these case studies are confidential master thesis projects that cover various emerging practices in the industry and need to be analyzed. The purpose of this study is to identify the impact of Industry 4.0 on risk-based maintenance and explore emerging practices in terms of maintenance functions, likelihood, consequences, and uncertainties. There are several well-known frameworks to identify existing practices in a company [48]. However, the Cynefin framework also helps categorize existing practices into best, good, emerging, and innovative practices [49; 50]. In complex fields, such as Industry 4.0, where a single case study may not provide comprehensive information, a multi-case study analysis is capable of identifying common and unique practices [51]. The type of industrial assets and their criticality might affect the emergence of practices. Thus, multi-case studies are purposefully selected to cover different asset types (rotating, static, safety-related) in different contexts (onshore, offshore topside, offshore subsea) to study the effect of the asset type. The scope of the study is illustrated in Figure 1, where two sets of relationships are planned to be explored. First, the relations clarify the impact of mindset, technology, value, and asset type on emerging practices. Second, the relations that clarify the impact of emerging practices on the RBM program (management, engineering analysis, risk evaluation, maintenance planning, scheduling, and task prioritization). In the following section, the literature, research methodology, and case studies are presented to provide the theoretical background and data input for this study. Then in Section 3, the results of the case studies are presented and discussed.