# Remote Technologies as Common Practice in Industrial Maintenance: What Do Experts Say?
## 2 Related Work  
The intersection between digitisation and maintenance, especially predictive maintenance, in production systems is well investigated recently [11]. Emergence of disruptive technologies for digitisation such as robotic, cloud computing, big data analytic, cyber physical systems, digital twins and machine learning makes it possible to monitor and collect data from production systems and analyse and utilise them for more precisely planning of maintenance. Plenty amount of technical reports from successful application of cyber physical systems for monitoring of production systems in different domains are available. An example of digitisation of agriculture is reported by [PERSON] et al. [12]. They apply Unmanned Aerial Vehicle, big data analytics and neural networks to monitor oil palms. More related studies are reported by [PERSON] et al. [13]. Their systematic literature review proposes an overview on application of machine learning for failure prediction in industrial maintenance. However, most of studies and technical reports discuss the benefits of application of digitisation in different domains using individual case studies (e.g., using remote monitoring for integration of digitisation to total production maintenance concept [14]). Despite the existing research on predictive maintenance, there is no systematic examination that addresses the challenges of the topic of maintenance in digital manufacturing systems. It is only described that maintenance should be digitised and implemented with the support of the new Industry 4.0 technologies, but not exactly what this implementation should look like. Another issues which is not covered in the literature is the increasing complexity of production facilities which leads to increased demands on the qualifications of employees. Technical assistance systems of Industry 4.0 for maintenance can be used in particular to support employees in machine monitoring and troubleshooting, so that they can better meet the increasing requirements through continuous further qualification [15]. To date, augmented reality (AR) has been described as a promising technology for assisting employees in industrial settings. However, many of these systems are not developed beyond prototype status [16]. [PERSON] et al. [17] see one possibility for solving this problem in the connection of experts who convey instructions to unqualified machine operators with the help of AR. The latest solutions from the field of AR are considered to be particularly promising for use in maintenance in order to provide targeted support, e.g., where to look for sources of malfunction or what correlations exist [4, 6, 18, 19]. There are also barriers to the widespread adoption of AR in various and complex industrial processes, e.g., the lack of adaptable and scalable AR work instructions [20] and low number of applications for user-oriented collaboration [21]. Furthermore, combination of Digital Twin and other technologies, e.g., AR are discussed in [22]. Our previous work [23] elaborates the need for remote technologies in industrial maintenance, especially AR, and summarises the challenges on the human, machine and organisational levels that need to be overcome for practical application in regard to literature. In this paper, we primarily address the often neglected human factor, as people are still the deciding factor for the success of maintenance processes despite all automation and digitisation. In addition, we also look at technical and organisational aspects of maintenance. We address the lack of overview on technical challenges and technological aspects for performing maintenance in digitised production systems. As it is still not known which Industry 4.0 technologies are applicable for which application scenario in maintenance and how an effective implementation of the related technology can proceed.