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.

Enterprise resource planning systems and maintenance management systems are commonly used by organisations for handling of maintenance records, through a graphical user interface. A maintenance record consists of a number of data fields, such as drop-down lists, list boxes, check boxes and text entry fields. In contrast to the other data fields, the operator has the freedom to type in any text in the text entry fields, to complement and make the maintenance record as complete as possible. Accordingly, the text entry fields of maintenance records can contain any words, in any number.Data quality is crucial in statistical analysis of maintenance records, and therefore manual analysis of maintenance records’ text entry fields is often necessary before any decision making. However, this may be a very tedious and resource consuming process.In this article, natural language processing is applied to text entry fields of maintenance records in a case study, to show how it can bring further value in the assessment of technical assets’ performance.Keywords: Maintenance records, Natural language processing, Structured and unstructured data, Data quality, Rail infrastructure.

Increased environmental awareness in the industry combined with the globalized market economy makes increasing demands for sustainable and efficient resource utilization. In this context, maintenance plays a critical role by linking business objectives to the strategic and operational activities aimed at retaining the system’s availability performance, cost-efficiency and sustainability. Performing maintenance effectively and efficiently requires corresponding infrastructure for decision-support provided through eMaintenance solutions. A proper eMaintenance solution needs to provide services for data acquisition, data processing, data aggregation, data analysis, data visualization, context-sensing etc. To en Quality of Service (QoS) in eMaintenance solutions, the performance of both system-of-interest, enabling systems and related processes have to be measured and managed. However, the QoS has to be considered on all aggregation levels and encompass the aspects of Content Quality (CQ), Data Quality (DQ) and Information Quality (IQ). Hence, the purpose of this paper is to study and describe some aspects of DQ in eMaintenance related to process industry in northern Europe

Reliability, availability and maintainability analysis is one of the most important tools for measuring system performance. The performance of a traction power supply system (TPSS) can be measured using the data collected from frequency converters, as these converters constitute the main part of the TPSS. The quality of the collected data should be good enough to provide the correct and complete information necessary for assessment of frequency converter performance. Many methods can be used to assess the performance of converters such as neural networks, fuzzy logic and standards. The IEEE 762 Standard offers a methodology that can provide key performance indicators for power generation units. This standard has been chosen for its widespread acceptance and applicability. To be able to evaluate a converter’s performance, IEEE 762 indexes should be calculated using data such as the downtime, reserve shutdown hours and service hours. Therefore, the purpose of this study is to assess the performance of the Swedish TPSS frequency converters using IEEE 762, and to assess the quality of data by inspecting their compatibility with this standard. In this study, an application has been developed to generate the missing information and to calculate the indexes provided by the standard, in order to evaluate the power converters’ performance. A case with sample data is also discussed in this paper.

Data quality (DQ) in maintenance has become an increasinglyimportant aspect to many firms as most of the maintenanceplanning and implementations are based on data analysis. PoorDQ has adverse effects at the operational, tactical, and strategiclevels of any organization. Respectively, poor DQ reducescustomer satisfaction, leading to poor decision making, and hasnegative impacts on strategy execution. To improve DQ as well asto evaluate the current status, DQ need to be measured followingthe fact that only what can be measured can be improved. Ameasure for DQ could be an important support for decisionmakers. In order to assess DQ, related attributes should bedefined. These attributes could be related to the data itself, to themetadata, or to the data representation schemes. After definingthese attributes, an assessment model should be used to evaluatethese attributes. The purpose of this paper is to propose a modelfor DQ assessment. Therefore, a study of DQ attributes and thepossible metrics that could be used to measure these attributes wasundertaken. The proposed model will be applied on datasetprovided by the Swedish Transport Administration (Trafikverket)for validation and to find an estimation measure of the DQ.

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.

Effective and efficient maintenance requires a proper information logistics, which can be delivered through eMaintenance solutions. Development of eMaintenance solutions faces extensive challenges. One of these challenges is how to ensure the quality of data used in different eMaintenance solutions. Data Quality (DQ) concerns all phases of the maintenance process.

The purpose of this paper is to answer the research question: How should DQ be considered and managed when developing eMaintenance solutions?

To deal with such challenges a case study was conducted at a mining company. Empirical data has been collected through interviews, observations, archival records and workshops.

The data analysis has been based on an empirical framework that supports the identification of required information services. Conditions that support the DQ and the information logistics, along with that, support the maintenance process have been presented. These aspects have also been related to the phases of a generic maintenance process.