Digital Media Lab 1 Data Mining Applied To Fault Detection Shinho Jeong Jaewon Shim Hyunsoo Lee {cinooco, poohut,

Logo Digital Media Lab 2 Introduction Aims of work Neural Network Implementation of the Non-linear PCA model using Principal Curve algorithm to increase both rapidity & accuracy of fault detection. Data mining? Extracting useful information from raw data using statistical methods and/or AI techniques. Characteristics Maximum use of data available. Rigorous theoretical knowledge not required. Efficient for a system with deviation between actual process and first principal based model. Application Process monitoring  Fault detection/diagnosis/isolation Process estimation  Soft sensor

Logo Digital Media Lab 3 Fault Detection? Fault introduction

Logo Digital Media Lab 4 Issues Major concerns Rapidity  Ability to detect fault situation at an earlier stage of fault introduction. Accuracy  Ability to distinguish fault situation from possible process variations. Trade-off problem Solve through  Frequent acquisition of process data.  Derivation of efficient process model through data analysis using Data mining methodologies.

Logo Digital Media Lab 5 Inherent Problems  Multi-colinearity problem  Due to high correlation among variables.  Likely to cause redundancy problem.  Derivation of new uncorrelated feature variables required.  Dimensionality problem  Due to more variables than observations.  Likely to cause over-fitting problem in model-building phase.  Dimensional reduction required.  Non-linearity problem  Due to non-linear relation among variables.  Pre-determination of degree of non-linearity required.  Application of non-linear model required.  Process dynamics problem  Due to change of operating conditions with time.  Likely to cause change of correlation structure among variables.

Logo Digital Media Lab 6 Statistical Approach Statistical data analysis Uni-variate SPC  Conventional Shewart, CUSUM, EWMA, etc.  Limitations  Perform monitoring for each process variable.  Inefficient for multi-variate system.  More concerned with how variables co-vary.  Need for multi-variate data analysis Multi-variate SPC  PCA  Most popular multi-variate data analysis method.  Basis for regression modesl(PLS, PCR, etc).

Logo Digital Media Lab 7 Linear PCA(1) Features Creation of…  Fewer => solve ‘Dimensionality problem‘ &  Orthogonal => solve ‘Multi-colinearity problem‘ new feature variables(Principal components) through linear combination of original variables. Perform Noise reduction additionally. Basis for PCR, PLS. Limitation Linear model => inefficient for nonlinear process.

Logo Digital Media Lab 8 Linear PCA(2) Theory Decoding mapping Encoding mapping

Logo Digital Media Lab 9 Linear PCA(3) ERM inductive principle Limitation Alternatives Extension of linear functions to non-linear ones using…  Neural networks.  Statistical method.

Logo Digital Media Lab 10 Kramer’s Approach Limitations Difficult to train the networks with 3 hidden layers. Difficult to determine the optimal # of hidden nodes. Difficult to interpret the meaning of the bottle-neck layer.

Logo Digital Media Lab 11 Non-linear PCA(1) Principal curve (Hastie et al. 1989) Statistical, Non-linear generalization of the first linear Principal component. Self-consistency principle  Projection step(Encoding)  Conditional averaging(Decoding)

Logo Digital Media Lab 12 Non-linear PCA(2) Limitations Finiteness of data. Unknown density distribution. No a priori information about data. Additional consideration  Conditional averaging => Locally weighted regression, Kernel regression Increasing flexibility(Span decreasing)  Span : fraction of data considered to be in the neighborhood. ~ smoothness of fit ~ generalization capacity

Logo Digital Media Lab 13 Proposed Approach(1) LPCA v.s. NLPCA

Logo Digital Media Lab 14 Proposed Approach(1) Creation of Non-linear principal scores

Logo Digital Media Lab 15 Proposed Approach(2) Implementation of Auto-associative N.N.

Logo Digital Media Lab 16 Case Study Objective Fault detection during operating mode change using 6 variables Data acquisition & Model building NOC data : 120 observations => NLPCA model building Fault data : another 120 observations drift

Logo Digital Media Lab 17 Model Building  Auto-associative N.N. using 2 MLP’s 5 iterations 50 iterations 30 iterations 1st MLP N.N. 2nd MLP N.N.  Principal curve fitting

Logo Digital Media Lab 18 Monitoring Result NLPCA model more efficient than LPCA model!!! Fault introduction

Logo Digital Media Lab 19 Conclusion Result Fault Detection performance was enhanced in terms of both speed and accuracy when applied to a test case. Future work Integration of ‘Fault Diagnosis’ and ‘Fault Isolation’ methods to perform complete process monitoring on a single platform.