data mining manufacturing data dave e. stevens eastman chemical company kingsport, tn
TRANSCRIPT
Data MiningManufacturing
DataDave E. Stevens
Eastman Chemical Company
Kingsport, TN
Presentation Outline
• Intro: Data Mining Manufacturing Data
• Data Preparation
• Principal Component Analysis
• Partial Least Squares
• PLS Discriminate Analysis
Manufacturing DataThen and Now
• 40 Years Ago - Few Measurements - Temp, Press., Flows• Today - Many Measurements - Very Often - Creates Large Data Sets• Purposes For Measuring - Process “State” - Relationships (X, X to Y) - Classification - Optimization
Concerns With Current Manufacturing Data
• Dimensionality: (Large)
>1000 process variables every few seconds
>10 quality variables every few hours
Data Overload - Analyst concentrates on only a few variables and ignore most of the information!
• Collinearity: Not 1000 independent things at work. Only a few underlying events affecting all variables. Variables are all highly correlated.
• Noise:
• Missing Data:
Multivariate Data Concept
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BreakLoad Control Chart
Elongation Control Chart
Is This Process In Control?
*
Data Preparation• Data collected in a Process Data Historian will have Process
Up and Down Times recorded from the instrumentation• Need a software tool that will permit easy methods to clean
the data and do initial Exploratory Data Analyses• JMP Software
– Interactive Graphing– Removal of Outliers
• Graphically or Variable Selection Criteria– Join and/or Subset Data Tables– Statistical Analyses
Principle Components
AnalysisUnderstanding Relationships Between Process Variables
Principle ComponentAnalysis
• Principle Component Analysis is a Projection Technique• Raw data are first “Centered” and “Scaled”• Each Principle Component represents a direction through
the data that captures the maximum amount of raw data variation
• For each Principle Component (a), new data values are generated for each obs. (i) which are a linear combination of the raw X variables (k):
ti,a = ba,1*Xi,1 + ba,2*Xi,2 . . . ba,k*Xi,k for each obs. i
Where the b’s are loadings (-1 to 1)• Increasing number of Principle Components represent less
and less raw data variation
Principle Component AnalysisFundamentals
2nd PC
1st PCProjections
X1
X2
X3
PCA: Scores
x1
x2
x3
1st PC
2nd PC
Obs. i
ti,1
ti,2
The scores tia (observation i, dimension a) are the places along the component lines where the observations are projected.
PCA: Loadings
x1
x2
x3
The loadings pak (dimension a, variable k) indicate the importanceof the variable k to the given dimension. pak is the direction cosine(cos of the given component line vs. the xk coordinate axis.
1x1
x2
x32
3
1st PC
Cos(X/PC
PCA Example
• 10 process responses obtained on each observation
• Data represented weekly process response averages
• Data spanned 10 months• Objective: Determine if the system
was stable.
Process ShiftJune 30 (5_30)
PCA Score Plot
PC #2
PC #1
Loadings PC#2
Loadings PC #1
X3
X7
X2X4
X8
X6
X9
X1
X5
X10
PCA Loadings Plot
Process ShiftJune 30 (5_30)
PC #1
PC #2
Relative to processshift, X1 and X5 werehigh in value and X4
and X8 were low invalue. Pos. Corr. Vars.were X1, X5 and X4, X8
Neg. Corr. Vars. wereX1, X5 to X4, X8
Process variable X1 increased in value when the system shifted from the left side to the right side on the PCA Score plot
Variables X1 and X5 were positively correlated
PartialLeast Squares
TechniqueUnderstanding Relationships
Between Process & Response Variables
Partial Least Squares Fundamentals
X Space Y Space
PlanesProjections
X1
X2
X3
Y1
Y2
Y3
TA Filter Example
• Objective: Relate Filtrate, TA Catalyst and Dryer Temp to Filter Speed, Vacuum, Wash Acid, Weir Level, Nash Discharge Pressure and Feed Tank Temperature– Keep Filtrate High, TA Catalyst Low
• Data: 12 Hour Averages from PI collected over a four month period
TA Filter
TA Filter Relationships
Catalyst
Higher filter speed and vac. pressure increased the filtrate flow and catalyst content but lowered the dyer temp.Higher weir level, nash discharge pressure and Op tank temp increased filtrate flow. Wash acid flow had no driving effect on the responses.
PLS Results• Obtain Weight Plots (Previous Slide)
– Shows the inter-relationships between the Xs and Ys
• Obtain Regression Coefficients– Can be used to generate response surface plot
• Display Variables Important to Prediction (VIP)
• Display Residual Plots and Distance to the Model Plot
CorrelationDoes Not
Always MeanCausation
PLS DiscriminateTechnique
Determine What Drives Data Groups To Be Different
Objective• Given groups of data from a particular process,
determine what makes the groups different with respect to the given measurements.
• Example: TA %T
– Measurements: 4-HMB, TMA, TPAD, 4-HBA, 4-CBA, IPA, BA, PTAD, p-TA, 2,7-DCF, 2,6-DCF, 4-4-DCB, 3,5-DCF, 9-F-2-CA, 9-F-4-CA, 2,6-DCA, 4,4-DCS, L*, a*, b*, .1%, .9%, Mean, %T
– Daily Numbers
– Data taken from Convey Line #1 and #2
TA %T
PLS Discriminate Analysis
High %T
Low %T
What Measurements Separated the Groups?
The high %T group ($DA1) was high in %T, 0.1, Mean and L. The low %T group ($DA2) had severalmeasurements that were high in value and were positively correlated (see next slide for details).
2
The low %T group ($DA2) had several variables that were correlated and high in value: 4 4’-DCS, 4-CBATMA and p-TA
Cat
Computer Software
• JMP Software– http://www.jmpdiscovery.com
• SIMCA-P from Umetrics– http://www.umetrics.com