Phillip Tretten

Enhancing the usability of maintenance data management systems

Purpose – The purpose of this paper is to explore and study the aspects of usability related to eMaintenance solutions. The study aims to expand the domain of eMaintenance by increasing the usefuln ...

eMaintenance solution through online data analysis for railway maintenance decision-making

Purpose – The purpose of this paper is to demonstrate how research within the railway sector is developing eMaintenance solutions using the cloud and web-based applications for improved condition monitoring, better maintenance and increased uptime. This eMaintenance solution is based on the on-line data acquisition, integration and analysis leading to effective maintenance decision making. Design/methodology/approach – In the proposed methodology, data are acquired from railway measurement stations to the eMaintenance cloud, where they are filtered, fused, integrated and analysed to assist maintenance decisions. Extensive consultation with stakeholders has resulted in the analysis of railway data. Findings – The paper provides a concept for a web-based eMaintenance solution for railway maintenance stakeholders for making fact-based decisions and develops more efficient and economically sound maintenance policies. Train wheels reaching their maintenance and safety limits are visualised in grids and graphs ...

eMaintenance ontologies for data quality support

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 eMainten...

Metadata-based data quality assessment

High quality data and data quality assessment can support the decision-makingprocess. In the literature, discussions of the assessment process are mainly focused on theoretical approaches to conten ...

Identifying Significance of Human Cognition in Future Maintenance Operations

Industrial maintenance in future will operate heavily with intelligent systems. Advanced sensor networks on machines will enable them communicate and learn about failure types, predict consequences and share solutions. Humans on the other hand are equipped with intuitive cognition that facilitates acquisition of knowledge about unique characteristics of individual machines, and use this knowledge in maintenance problem solving. In this article, we identify two major opportunities to collaborate human intuitive cognition with intelligent systems for future maintenance solutions.

Quality of Streaming Data in Condition Monitoring Using ISO 8000

The purpose of this paper is to propose a Data Quality Measurement Model based on ISO 8000 standard. This paper deals about the concepts implied in the measurement process, not about the measures themselves. Poor quality information causes customer dissatisfaction, lost revenue and higher costs associated with additional time to reconcile information. An understanding of the characteristics of the data that determine its quality, and an ability to measure, manage and report on data quality is required. Measurement is a major activity in data quality management. In literature, there are many proposals contributing somehow to the measurement of data quality. However, these measurement methods lack the unification. ISO 8000 provides a framework for improving data quality that can be used independently or in conjunction with quality management systems. ISO 8000 defines characteristics that can be tested by any organization in the data supply chain to objectively determine conformance of the data to ISO 8000.