Oloke, D; Edwards, D J; Wright, B and Love, P E D (2006) A web-based information management system for predicting the breakdown of off-highway plant. Journal of Engineering, Design and Technology, 4(1), pp. 29-45. ISSN 1726-0531
Abstract
Effective management and utilisation of plant history data can considerably improve plant and equipment performance. This rationale underpins statistical and mathematical models for exploiting plant management data more efficiently, but industry has been slow to adopt these models. Reasons proffered for this include: a perception of models being too complex and time consuming; and an inability of their being able to account for dynamism inherent within data sets. To help address this situation, this research developed and tested a web-based data capture and information management system. Specifically, the system represents integration of a web-enabled relational database management system (RDBMS) with a model base management system (MBMS). The RDBMS captures historical data from geographically dispersed plant sites, while the MBMS hosts a set of (Autoregressive Integrated Moving Average – ARIMA) time series models to predict plant breakdown. Using a sample of plant history file data, the system and ARIMA predictive capacity were tested. As a measure of model error, the Mean Absolute Deviation (MAD) ranged between 5.34 and 11.07 per cent for the plant items used in the test. The Root Mean Square Error (RMSE) values also showed similar trends, with the prediction model yielding the highest value of 29.79 per cent. The paper concludes with direction for future work, which includes refining the Graphical User Interface (GUI) and developing a Knowledge Based Management System (KBMS) to interface with the RDBMS.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | database; model base; off-highway plant; plant breakdown; time series; web-based model |
| Index terms: | integration, mean square error, history, relational database, mathematical model, management system, time series, prediction model, user interface, plant and equipment, database, deviation |
| Subjects: | data management, organizational analysis, human-computer interaction, management, manufacturing engineering, probability and distributions, data science, prediction and forecasting, financial and cost management, architectural and construction history, mathematical modelling |
| Topics: | Digital Applications, Organizational Design, Research Practice, Engineering Principles, Cost Management |
| Descriptive scope: | 3 CTA |
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