01511nas a2200217   4500008004100000022001400041245009700055210006900152260010600221300001400327490000700341520066100348653002001009653001401029653002801043653003001071100001701101700002601118700002101144856012801165       2005                        eng d  a0098-135400aKnowledge incorporated support vector machines to detect faults in Tennessee Eastman Process0 aKnowledge incorporated support vector machines to detect faults   aTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLANDbPERGAMON-ELSEVIER SCIENCE LTDcSEP  a2128-21330 v293 a<p>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. (c) 2005 Elsevier Ltd. All rights reserved.</p>10afault detection10aknowledge10asupport vector machines10aTennessee Eastman Process1 aKulkarni, A.1 aJayaraman, Valadi, K.1 aKulkarni, B., D.  uhttps://library.ncl.res.in/content/knowledge-incorporated-support-vector-machines-detect-faults-tennessee-eastman-process-0