PurposeThis paper presents a concept for digitalised maintenance (DM), maps the conceptualised DM to maintenance problems in industries and highlights challenges that might be faced when realizing this concept.Design/methodology/approachFirst, maintenance problems that are faced by the industry are presented, followed by a conceptualisation of DM. Next, a typical operational scenario is used as an exemplification to show system dynamics. The characteristics of this conceptualised DM are then mapped to the identified maintenance problems of industry. Then, interesting initiatives in this domain are highlighted, and finally, the challenges to realize this approach are discussed.FindingsThis paper identified a set of problems related to maintenance in industry. In order to solve current industrial problems, exploit emerging digital technologies and elevate future industries, it will be necessary to develop new maintenance approaches. The mapping between the criteria of DM and maintenance problems shows the potential of this concept and gives a reason to examine it empirically in future work.Originality/valueThis paper aims to help maintenance professionals from both academia and industry to understand and reflect on the problems related to maintenance, as well as to comprehend the requirements of a digitalised maintenance and challenges that may arise.

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With the recent digitalization trends in the industry, wireless sensors are, in particular, gaining a growing interest. This is due to the possibility of being installed in inaccessible locations for wired sensors. Although great success has already been achieved in this area, energy limitation remains a major obstacle for further advances. As such, it is important to optimize the sampling with a sufficient rate to catch important information without excessive energy consumption, and one way to achieve sufficient sampling is using adaptive sampling for sensors. As software plays an important role in the techniques of adaptive sampling, a reference framework for software architecture is important in order to facilitate their design, modeling, and implementation. This study proposes a software architecture, named Rainbow, as the reference architecture, also, it develops an algorithm for adaptive sampling. The algorithm was implemented in the Rainbow architecture and tested using two datasets; the results show the proper operation of the architecture as well as the algorithm. In conclusion, the Rainbow software architecture has the potential to be used as a framework for adaptive sampling algorithms, and the developed algorithm allows adaptive sampling based on the changes in the signal.

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The key driving factors in using humans and robots in collaborative applications for assembly processes are to reduce assembly time, cost and to improve the human working environment from an ergonomic viewpoint. Currently, there are limited automated procedures in assembly operations in house construction because the traditional type of assembly process depends entirely on manpower. This is common in the assembly process in different industries since assembly is one of the most demanding and intense manufacturing processes, and it is difficult to automate. This paper presents a case study on the implementation of human-robot collaboration for window assembly by way of an offline robot programming simulation. A self-adaptive software architecture that runs on a real-time target machine is also proposed for robotic window assembly. The window assembly method that will be used in this study is called “Click-In” and is manufactured by Fixture System Sweden AB. Apart from robot simulations, detailed suggestions are given for building a pilot cell for robot window assembly. The case study presented in this paper has both economical and ergonomic goals. The economic goal is to reduce the assembly time which will lead to an increase in window production. By introducing human-robot collaboration, operators do not need to perform uncomfortable assembly operations—rather the robot will perform these un-ergonomic operations. The feasibility of both goals is verified with offline robot programming simulation.