Purpose – The purpose of this paper is to explore the main ontologies related to eMaintenance solutions and to study their application area. The advantages of using these ontologies to improve and control data quality will be investigated.

Design/methodology/approach – A literature study has been done to explore the eMaintenance ontologies in the different areas. These ontologies are mainly related to content structure and communication interface. Then, ontologies will be linked to each step of the data production process in maintenance.

Findings – The findings suggest that eMaintenance ontologies can help to produce a high quality data in maintenance. The suggested maintenance data production process may help to control data quality. Using these ontologies in every step of the process may help to provide management tools to provide high quality data.

Research limitations/implications – Based on this study, it can be concluded that further research could broaden the investigation to identify more eMaintenance ontologies. Moreover, studying these ontologies in more technical details may help to increase the understandability and the use of these standards.

Practical implications – It has been concluded in this study that applying eMaintenance ontologies by companies needs additional cost and time. Also the lack or the ineffective use of eMaintenance tools in many enterprises is one of the limitations for using these ontologies.

Originality/value – Investigating eMaintenance ontologies and connecting them to maintenance data production is important to control and manage the data quality in maintenance.

The Iron Ore Line (Malmbanan) is a 473 km long track section located in northern Sweden and has been in operation since 1903. This track section stretches through two countries, namely Sweden and Norway, and the main part of the track runs on the Swedish side, where the owner is the Swedish Government and the infrastructure manager is Trafikverket (the Swedish Transport Administration). The ore trains are owned and managed by the freight operator and mining company LKAB. Due to the high axle load exerted by transportation of the iron ore, 30 tonnes, and the high demand for a constant flow of ore and pellets, the track and wagons must be monitored and maintained on a regular basis. The condition of the wagon wheel is one of the most important aspects in this connection, and here the wheel profile plays an important role. For this reason an automatic laser-based wheel profile monitoring system (WPMS) has been installed on this line using a system lifecycle approach that is based on the reliability, availability, maintainability and safety (RAMS) approach for railways. The system was prepared and installed and is being operated in a collaborative project between the freight operator and infrastructure manager. The measurements are used to diagnose the condition of the wheels, and to further optimize their maintenance. This paper presents a study of the concepts and ideas of the WPMS, and the selection, installation and validation of the equipment using a system lifecycle approach that is based on RAMS for railways. Results from the profile measurements and validation are shown. The system’s reliability during performance in extreme climate conditions, with severe cold and large quantities of snow, is presented. Then the benefits, perceived challenges and acquired knowledge of the system are discussed, and an improved V-model for the lifecycle approach is presented.

Asset performance measurement is an integral part of business process designed to support infrastructure managers in decision-making. Managing infrastructure performance throughout an asset’s life cycle is a challenge; assessment is a complex issue involving various inputs and outputs, as well as conflicting requirements of different stakeholders. For railway infrastructure managers, the most critical issue is to reduce maintenance possession time, to minimise train delays and meet passengers’ and societal needs.

In this study, we collect and analyse work order and train delay data for one section of the iron ore railway line in Sweden. The aim is to present the overall performance of an asset for its end users, considering both the asset context and the user context, to ensure effective planning and decision-making. The case study results can also be used for internal and external benchmarking, and to identify performance problems of the infrastructure. 

The demands on the integrity and interoperability of data in maintenance are constantly increasing. For many enterprises,achieving these demands in their operation and maintenance processes is challenging. In order to enable informationexchange between systems and their actors, using effectively andefficiently usable way, there are two essential issues which need to be addressed: i) content structure - which addresses with the description of the content’s inherent elements and also the relationship between these elements; and ii) communication interface – which addresses the interface through which each specific content will be exposed.

There are a lot of eMaintenance ontologies which may help to solve these issues.The aim of this paper is to: i) explore the main ontologies related to eMaintenance solutions and to inspect how Data Quality (DQ) aspect is considered within these ontologies; ii) propose a process called “Maintenance Data Production” and to find the relation between ontologies and their role during data production stages.

iMaintenance stands for integrated, intelligent and immediate maintenance. It integrates various maintenance functions and connects these to all devices, using advanced communication technologies. The main challenge is to integrate the disparate systems and capabilities developed under current eMaintenance models and to make them immediately accessible through intelligent computing technologies. iMaintenance systems are computer-based, able to evolve with the system that they monitor and control, and they can be embedded in the system’s components, providing the ability to integrate new functionality with no downtime. This article will show how iMaintenance systems can provide decision-making support, thereby going beyond merely connecting various maintenance systems.

As technological development progresses in society, there are several new possibilities to make the maintenance process in industry more efficient. Today, eMaintenance solutions facilitate data management in maintenance activities; data can easily be integrated and shared between sources in heterogeneous environments. This enables systems used in maintenance, such as Computerized Maintenance Management Systems (CMMS), to base decisions on various data, e.g. data produced in other processes. These systems often fulfil the technical requirements (e.g. data consistency control) required to support activities in the maintenance process (e.g. management, support planning, and assessment), but the human interaction with the systems is still essential to the quality of the work performed.

Since many maintenance activities require manual input of data the interaction between user, e.g. technician, and system, e.g. CMMS, has impact on the quality of the data; in order for the data to be right and relevant, the technician may need supporting directives from the CMMS. Hence, the system usability must be considered when assuring the quality of the manually inputted data. 

The focus of this paper is to investigate CMMS limitation issues due to usability aspects. Furthermore, the paper discusses the role of context awareness within user interfaces managing data obtained through eMaintenance solutions and presents ideas for future research on context awareness in eMaintenance solutions. Data and conclusions have been conducted through literature studies and case studies within the area.