Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process
Knowledge incorporated support vector machines to detect faults in Tennessee Eastman Process
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DOI：
10.1016/j.compchemeng.2005.06.006
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发表时间：
2005-09
期刊：
影响因子：
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通讯作者：
Abhijit J. Kulkarni;V. Jayaraman;B. Kulkarni
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文献类型：
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作者：
Abhijit J. Kulkarni;V. Jayaraman;B. Kulkarni
将知识融合的支持向量机应用于化工基准问题田纳西-伊士曼过程的故障检测。知识融合算法充分利用了数据集中实例在切线方向上的水平平移不变性信息。这本质上改变了训练算法时输入数据的表示。这些本地转换不会更改数据集中实例的类成员身份。二进制以及多故障检测的结果证明使用知识纳入。
A support vector machine with knowledge incorporation is applied to detect the faults in Tennessee Eastman Process, a benchmark problem in chemical engineering. The knowledge incorporated algorithm takes advantage of the information on horizontal translation invariance in tangent direction of the instances in dataset. This essentially changes the representation of the input data while training the algorithm. These local translations do not alter the class membership of the instances in the dataset. The results on binary as well as multiple fault detection justify the use of knowledge incorporation.