# STRUCTURING TECHNIQUES Data Mining for analysis. Boilers & Compressor - Data organization  Defining the key variables  Organizing the variables in the.

## Presentation on theme: "STRUCTURING TECHNIQUES Data Mining for analysis. Boilers & Compressor - Data organization  Defining the key variables  Organizing the variables in the."— Presentation transcript:

STRUCTURING TECHNIQUES Data Mining for analysis

Boilers & Compressor - Data organization  Defining the key variables  Organizing the variables in the logical sequence of influence  The order of influence should ideally be from left to right (ascending from left to right)  Statistical inferences should be available for each parameter  Normalization is the process of finding the distance of the cluster nucleus from an ideal desired value (DV)  Standardization is the normalization process in MS-EXCEL that needs to be used extensively to account for data clusters  Averaging out the normalized values for each of the key parameters TO GET A SINGLE VALUE DESCRIBING THE STATE OF THE PROCESS ON THE EQUIPMENT  Plotting the normalized values to track relative performances of each equipment  INTERDEPENDENCE OF THE VARIABLES IN DEFINING THE OVERALL PERFORMANCE OF THE EQUIPMENT IS THE KEY DERIVATIVE OF THE DATA STRUCTURE

DEFINING THRESHOLDS THROUGH NORMALIZATION TECHNIQUES Integrating variables around the standard

Illustrating the normalization process Cluster-1 with scattered data pointsCluster-2 with closely populated data points Cluster-1 has a longer distance from the nucleus Cluster-2 has a shorter distance from the nucleus Desired value- NUCLEUS

Normalizing an indicator  An indicator can be evaluated based on the normalization technique  A cluster of data might have co-ordinate values that are skewed from the desired path  A single measure that indicates the distance of the cluster from the standard is the key  Farther the distance from the nucleus, greater is the value of the skew

Evaluating the distance  The distance is denoted by an indicator  This would be a negative indicator  Negativity indicates a hovering around the nucleus but is just short of the target  Positivity includes that the standards have been overshot  Negativity implies that there should be a convergence around 0- the nucleus  Positivity implies that the paradigms of standards have to be uplifted; in other words, “the bar has to be raised”

MACHINERY QUALITY INDEX (MQI) CONCEPTUALIZATION Defining an index - MQI

Conceptualizing the MQI  MQI = MACHINERY QUALITY INDEX MQI = EXP(normalized values) EXP => exponential function amplifies the low entropy of a normalized trend and delivers a reasonable index to reflect the state of the process

Comparative analysis for Normalization V/S MQI grades Normalization Linking the variables that form the matrix for defining equipment performance Indicates the cumulative distance from the standard on each parameter as also on the summative assessment Intuitive explanation on an equipment’s performance Tailor-made for the core- professionals who are deputed on the job MQI Gross index describing relative performance Movement around the standard is neither implied nor indicated Scale is relative and NOT intuitive and hence only describes a perception Suitable to describe the performance to non-core professionals or stakeholders

Download ppt "STRUCTURING TECHNIQUES Data Mining for analysis. Boilers & Compressor - Data organization  Defining the key variables  Organizing the variables in the."

Similar presentations