# How to Make Augmented Reality a Tool for Railway Maintenance Operations: Operator 4.0 Perspective
## 3 Methods
### Architectural Requirements  
The proposed architecture, apart from bringing together information from different digital databases, combines five major features by exploiting AR: (1) capturing the knowledge of the operator, (2) providing maintenance support, (3) performing failure analysis, (4) providing problem-solving strategy, and (5) providing learning capabilities. The adaptive architectural framework has been designed simultaneously while performing case studies. The studies are proving the value of the framework. The framework is based on the methodological decision support system and its main requirements include:  
* Provide the augmented operator with real-time feedback and augmented reality content on tasks/procedures execution. Operators are guided by the supplying of visual and audible instructions to give tangible feedback. * Based on expertise, experience, external factors, current conditions of the component, it is required to ensure the operator has a personal tailored digital knowledgeable assistant to interact with. Depending on the operator's ability, noncritical information can be supplied using subtle instructions in different visible frequencies. * By capturing the knowledge of the operator and procedural steps, the system can learn from previous maintenance procedures. The time and sequence of steps used to perform a maintenance task can be captured and reported. This can indicate how much time is needed in the future to perform the task. Moreover, failure rates can be compared to this information, providing insight on the most sustainable procedure. Maintenance planning and schedules can be adapted to these accurate findings. Therefore, the efficiency of operation support will be increased. Based on the architectural requirements, a functional analysis is presented to structure and identify potential solutions that exist for the adaptive tool. In Figure 3, the most viable solutions are proposed. The analysis explores potential solutions for a given function, the solutions are variable. Based on the functional requirements from Figure 3, the architecture in Figure 4 has been designed and implemented in the maintenance process analysis of Figure 2.  
* _Step 1_: Select equipment. The goal to be achieved is formulated. The operator will be guided by visual and audible instructions which also give tangible feedback. * _Step 2_: Determine functions. The task that needs to be selected to reach the goal is stated. Besides this, the adaptive AR tool provides all failure causes and digital information on all potential solutions. It will let the operator be aware of the context to gather relevant information and/or services, relevancy depends on the operator's tasks [37]. Using context awareness systems, such as AR, accurate access to maintenance information is provided such that the operator's performance efficiency can be increased. * _Step 3_: Describe failures. Initiation and evaluating the operator's expertise is currently based on the operator's or manager's perspective. The level of expertise varies from having no clue what is going on up to being an expert and able to train others. In this framework, initiating the level of expertise is performed manually but can become an automized process in the future. Operators can be equipped with sensors to activate psychomotor and cognitive responses that are beyond what operators can verbalize. Capturing gestures of experts can improve interactions with AR and ensures future knowledge capturing [33]. * _Step 4_: Describe failure modes. Dynamic behavior capturing is required to perform a successful fault diagnosis [33]. Based on time and process tracking, the operator should know what initiated the fault, what the current situation is, what is needed to solve the issue, and what time is required to solve the task. Varying business demands changesin work routines, resource availability, and environmental conditions. Depending on the complexity and nature of the maintenance task, the operator adapts his/her maintenance concept.