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Download fileProcess Monitoring Based on Orthogonal Locality Preserving Projection with Maximum Likelihood Estimation
journal contribution
posted on 19.03.2019, 00:00 authored by Jingxin Zhang, Maoyin Chen, Hao Chen, Xia Hong, Donghua ZhouBy
integrating two powerful methods of density reduction and intrinsic
dimensionality estimation, a new data-driven method, referred to as
OLPP–MLE (orthogonal locality preserving projection-maximum
likelihood estimation), is introduced for process monitoring. OLPP
is utilized for dimensionality reduction, which provides better locality
preserving power than locality preserving projection. Then, the MLE
is adopted to estimate intrinsic dimensionality of OLPP. Within the
proposed OLPP–MLE, two new static measures for fault detection TOLPP2 and SPEOLPP are defined. In order to reduce algorithm
complexity and ignore data distribution, kernel density estimation
is employed to compute thresholds for fault diagnosis. The effectiveness
of the proposed method is demonstrated by three case studies.
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Keywords
case studiesfault detection T OLPP 2algorithm complexitydimensionality reductionorthogonal localitySPE OLPPdimensionality estimationMLEOrthogonal Localitydata-driven methodprocess monitoringkernel density estimationdata distributionMaximum Likelihood Estimationprocess Monitoringdensity reductionfault diagnosis