01590nas a2200193   4500008004100000245007800041210006900119260000900188300001400197490000700211520095500218653002101173653002101194653002301215653001601238100001101254700001401265856011701279       2013                        eng d00aFault prediction of nonlinear system using time series novelty estimation0 aFault prediction of nonlinear system using time series novelty e  c2013  a207 - 2120 v113 aMany complex nonlinear systems have the characteristics such as difficult modeling, dangerous testing, and high cost to experiment with fault. Aim at these points, a new kind of novelty estimation method based on an on-line Least Square Support Vector Regression (LS-SVR) algorithm is presented for fault prediction of nonlinear system in this paper. Firstly, a set of characteristic token values, which represent normal state of time series at the beginning, are selected as the training data to establish the on-line LS-SVR model. Then, the output values of the on-line model are analyzed to estimate novel pattern. Once the novel pattern is affirmed, the fault of system is forecasted immediately. The proposed method needs neither the math model of system nor the fault training data and primary knowledge, and it is believed with good real-time capability. The results of simulation on Continuous Stirred Tank Reactor (CSTR) show the efficiency.10aFault prediction10aNonlinear system10aNovelty estimation10aTime series1 aSu, S.1 aZhang, W.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84880818113&partnerID=40&md5=5fe267d630eb652afa900d99483c0dcf