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© 2012 by Honeywell International Inc. All rights reserved

Tutorial T4: Practical Data Mining

PHM Conference, Minneapolis, MN

9/23/2012 1:30-3:00 PM

Ravi Patankar

Principal Engineer

Honeywell Aerospace

Phoenix, AZ ravindra.patankar@honeywell.com

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Objective

• Getting one started with data mining

using RapidMiner

Download RapidMiner from Rapid-i.com and install

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RapidMiner

• Leading open source data mining tool

• 100% java

• Uses CRISP-DM

From crisp-dm.org

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Agenda

• RapidMiner Overview - Operators

- Repositories Data

Process

• (remote) Execution

• Saving

- Data visualization

- Documentation?

• ETL - Loading from data files and databases

- Data preparation

- Transformations

- Missing values

- Filtering

- Outliers

- Attribute reduction

- Attribute selection

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Agenda (continued)

• Supervised learning - Modeling methods

- Applying models

- Comparing models

- Performance metrics and evaluation

- Validation

- Automatic supervised learning (PaREn)

• Unsupervised Learning - Association rules

- Frequent item set mining

- FP-Growth

• Text Mining Introduction

• Delivering Analytics via the Web - RapidAnalytics

Collaboration

Webservices

Reports

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Data Visualization and ETL

• Titanic data set from R

• Introduction to process construction

- Decision Tree

• ETL operators

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Examples

• Incorrect settings of automotive DTCs

• Engine X : Fuel System Failure

• Engine Y : Turbine blade and Fuel control

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Control Module Vs Powertrain Voltages

$42<$148D likely powering down

$42>$148D likely powering up

US patent application 12/638,592

DTC P1682

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Fuel System Related Failure

• Analyze normalized engine X data

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Machine Learning

• Supervised

A method for creating a function from training data

Data consist of pairs of input objects and outputs

Ex: Data mining for reasons for known fault that has occurred

Algorithms: Trees - Decision Tree, Bayes, Neural Networks

• Unsupervised

A method where a model is fit to observations.

No a priori output is known.

Ex: Data mining for patterns (faults unknown)

Algorithms: Apriori, Predictive Apriori, Tertius, FPGrowth

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Using Supervised Learning

• Normalized engine Y data

• Detect Failure Onset

- Turbine blade

- Fuel control

• Using different Techniques

• Comparing Performance of Techniques (Methods)

• Validation Methods

• Automation/Optimization

• Using PaREn Plugin

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Fault Detection Performance

Truth

Table

Detected

Faulty No Fault

Actu

al Fau

lty

Good

True +

Consumer

Risk

False -

No

Fa

ult

Manufacturer

Risk

False +

Good

True -

recall

pre

cis

ion

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Optimization to Tune CA-CBM

PHM Algorithms

CA-CBM

performance

Evaluator

Historic

Data

Player

actual conclusions for the data streams

CA-CBM input

data streams

CA-CBM

conclusions

tunable parameters

Optimization automatically tunes parameters to obtain the maximum performance

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RapidAnalytics

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Delivering Analytics via the Web

• Deployment of Analyses with RapidAnalytics

- Sharing data, models, and processes

- Managing processes and services

- User Management

- Configuration

• Process Execution

- Remote execution of processes

- Scheduling processes

- Exporting processes as Web services

• Reports

- Using RapidMiner Processes as Web services in reports