Data stream forecasting for system fault prediction

支持向量机 数据挖掘 计算机科学 数据流 故障检测与隔离 数据流挖掘 断层(地质) 人工智能 电信 地质学 地震学 执行机构
作者
Ahmad Alzghoul,Magnus Löfstrand,Björn Backe
出处
期刊:Computers & Industrial Engineering [Elsevier BV]
卷期号:62 (4): 972-978 被引量:35
标识
DOI:10.1016/j.cie.2011.12.023
摘要

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.
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