Time Series Prediction and Neural Networks
Journal of Intelligent and Robotic Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ACM SIGMOD Record
Data Stream Prediction Using Incremental Hidden Markov Models
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Increasing availability of industrial systems through data stream mining
Computers and Industrial Engineering
Research on prediction models over distributed data streams
WISE'06 Proceedings of the 7th international conference on Web Information Systems
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
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Competition among today's industrial companies is very high. Therefore, system availability plays an important role and is a critical point for most companies. Detecting failures at an early stage or foreseeing them before they occur is crucial for machinery availability. Data analysis is the most common method for machine health condition monitoring. In this paper we propose a fault-detection system based on data stream prediction, data stream mining, and data stream management system (DSMS). Companies that are able to predict and avoid the occurrence of failures have an advantage over their competitors. The literature has shown that data prediction can also reduce the consumption of communication resources in distributed data stream processing. In this paper different data-stream-based linear regression prediction methods have been tested and compared within a newly developed fault detection system. Based on the fault detection system, three DSM algorithms outputs are compared to each other and to real data. The three applied and evaluated data stream mining algorithms were: Grid-based classifier, polygon-based method, and one-class support vector machines (OCSVM). The results showed that the linear regression method generally achieved good performance in predicting short-term data. (The best achieved performance was with a Mean Absolute Error (MAE) around 0.4, representing prediction accuracy of 87.5%). Not surprisingly, results showed that the classification accuracy was reduced when using the predicted data. However, the fault-detection system was able to attain an acceptable performance of around 89% classification accuracy when using predicted data.