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-001480nas a2200313   4500008004100000020002000041245008500061210006900146260001800215520049000233653002500723653001900748653002000767653001800787653001400805653002600819653001900845653002100864653002700885653001700912653003400929653001200963653001500975100001600990700001601006700001501022700001501037856011401052       2017                        eng d  a978-150901338-800aFault detection and analysis of Kapton film in fuel cells using image processing0 aFault detection and analysis of Kapton film in fuel cells using   c28 April 20173 a<p>Fuel cell is one of the best alternatives of fossil energy in the modern scenario. But still there are some shortcomings such as durability and reliability, which block the wide application of fuel cells. To overcome these barriers, fault diagnosis is an efficient solution. This paper proposes a method for finding dimensional accuracies in Kapton films in membrane electrode assemblies (MEA) of fuel cells using image processing tools for offline fault analysis. © 2016 IEEE.</p>10aDimensional accuracy10aFault analysis10afault detection10aFossil energy10afuel cell10aGas fuel purification10aimage analysis10aImage processing10aImage processing tools10aKapton films10aMembrane electrode assemblies10aOffline10apolyimides1 aFirdous, R.1 aAherrao, S.1 aNarute, B.1 aBorkar, V.  uhttps://library.ncl.res.in/content/fault-detection-and-analysis-kapton-film-fuel-cells-using-image-processing