Mustafa Al-Jumaili

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 ...

Aspects of Data Quality in eMaintenance: A Case Study of Process Industry in Northern Europe

Increased environmental awareness in industry combined with the globalized market economy has created an increase in demand 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 system 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. For 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 must 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 the process industry in northern Europe.

Assessment of railway frequency converter performance and data quality using the IEEE 762 Standard

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.

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.