Transcript Design for Data analysis for Engineers

Design for Data Analysis
for Engineers
MPD 575 DFX
Cohort 8
November 29, 2007
Developed by: Tjuana Buford
Professor: Jonathan Weaver
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Design for Data Analysis
for Engineers
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Introduction to DFDA (Design for Data Analysis)
Define DFDA for Engineers
DFDA and SEF (Systems Engineering Fundamentals)
Examples
Summary
References
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Design for Data Analysis
for Engineers
Introduction to DFDA (Design for Data Analysis)
Definitions for Data Analysis:
Data analysis is the process of looking at and summarizing data with the intent
to extract useful information and develop conclusions.1
Confirmatory data analysis is based on confirming or falsifying existing
hypotheses. 1
Exploratory data analysis is based on discovering new features in the data1
Data analysis – a procedure that prepares a data model for implementation as
a non-redundant, flexible, and adaptable database2
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Design for Data Analysis
for Engineers
Introduction to DFDA Cont.
Design for DFDA relates to the following Design for X
Modules:
Design for Testability
Design for Serviceability
Design for Robustness
Design for Reliability
Design for Reuse
Design for Failure
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Design for Data Analysis
for Engineers
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Introduction to DFDA (Design for Data Analysis)
Define DFDA for Engineers
DFDA and SEF (Systems Engineering Fundamentals)
Example
Summary
References
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Design for Data Analysis
for Engineers
Define DFDA for Engineers
Testing falls within Systems Engineering Fundamentals, which creates a
need for analysis of data. Falling within the generic, right hand side of the
SEF “V”, verification using data analysis may include:
Analysis of large or small amounts of data
Varying data formats from multiple sources
Data from multiple engineers within a department
Data from several engineering departments
Report generation
Result retention capability
Engineers are required to meet requirements through testing,
but who designs the test and analysis?
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Design for Data Analysis
for Engineers
Define DFDA for Engineers Cont.
Tests and analysis are designed by Test engineers, R&D engineers,
Technical Specialists, Software Engineers, and many more!
"Just as a requirement specifies the functional performance to be
delivered (not how it is to be designed), a Design Verification Method
defines what the test or analysis must deliver, not how the test or
analysis is to be designed.“3 (emphasis added)
How good is your Analysis?
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Design for Data Analysis
for Engineers
Define DFDA for Engineers Cont.
Test Engineers who create analysis procedures need to have both engineering
and computer science knowledge to be able to move beyond Microsoft Excel
as a method of data analysis. Past experience with strictly IT personnel
creating or choosing data analysis tools has resulted in tools which do not
stand up to engineering requirements, for example:
Allowing final results to be altered
Misinterpretation of engineering theory
Various forms of analysis documentation throughout company
No version control
No means of sharing (process, analysis, results) easily with other engineers
Cost to local engineering departments for design, and maintenance of tools
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Design for Data Analysis
for Engineers
Define DFDA for Engineers Cont.
According to Broy (2006)4,
The typical electrical engineer lacks the following:
Do not know enough about software
Do not understand the software processes
Do not understand project management
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Design for Data Analysis
for Engineers
Define DFDA for Engineers Cont.
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Design for Data Analysis
for Engineers



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Introduction to DFDA (Design for Data Analysis)
Define DFDA for Engineers
DFDA and SEF (Systems Engineering Fundamentals)
Examples
Summary
References
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Design for Data Analysis
for Engineers
DFDA and SEF (Systems Engineering Fundamentals)
Customer Focused
Feedback
Verification using data
analysis can occur at any
level
Verification
Requirements
Customer
Satisfaction
System
Verification
Integrate
Ver.
Req.
Subsystem
Verification
Integrate
Component
Design
Fabrication/
Verification
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Design for Data Analysis
for Engineers
DFDA and SEF (Systems Engineering Fundamentals)
Customer Focused
Feedback
Analysis and test results
are recorded in the DVP&R
as the Verification plan is
executed.
(actual results vs. targets)
Verification
Requirements
Customer
Satisfaction
System
Verification
Integrate
Ver.
Req.
Subsystem
Verification
Integrate
Component
Design
Fabrication/
Verification
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Design for Data Analysis
for Engineers
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

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Introduction to DFDA (Design for Data Analysis)
Define DFDA for Engineers
DFDA and SEF (Systems Engineering Fundamentals)
Examples
Summary
References
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Design for Data Analysis
for Engineers
Example 1:
My manager asked me to develop a regression
model for some data from a new process, what
are the basic steps I should follow?5
Input Data
Process The Data
Output Data
“On a fundamental level, all computer programs do the same thing”6
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Design for Data Analysis
for Engineers
Start
Example 1 Cont.
The Basic Steps
In the form of a
Flowchart.
Select form of model
based on current
data or results from
prior model.
New Data
needed to
fit model?
Yes
Design
New
Experiment
No
No
Fit model using
parameter estimation
method suggested by
data and /or process
knowledge
Collect
New
Data
Validate model to
assess its adequacy
New model
describes
data well?
Yes
End
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Design for Data Analysis
for Engineers
Example 2
I use Excel to analyze data
collected from highway
driving studies. It takes me
20 hours to analyze
approximately 22,000 data
points, and create a report.
What can I use to speed up
this process?
*Fictitious Data
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Design for Data Analysis
for Engineers
Example 2 Cont.
There are many software packages on the market to analyze data
including:
Excel Macros/Data Analysis Toolkit
MATLAB/Simulink
Minitab
Labview
NumPy and SciPy for Python (Freeware)
Software choices have to take into account cost, functionality, ability to
generate reports, customer service and support.
Use trial software offers to help test software packages.
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Design for Data Analysis
for Engineers
Example 2 Cont.
Most Engineers that have a multitude of data to analyze will eventually
move away from the most basic products to products that make their time
more productive.
Some issues with software packages:
Excel – Okay for small projects but for large projects, major version
changes from Microsoft can kill macros and Excel functions you depend
on.
MATLAB/Simulink – Large online community of users, support available
online, by phone, and in person (for a fee). Ability to handles large
amounts of Data. Each additional toolbox costs more money. Has report
generator capability.
Labview – Widely used in the automotive industry.
Python – Customizable to individual needs. Free to use. Growing in popularity.
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Design for Data Analysis
for Engineers
Example 2 Cont.
General Comparison of Numerical Analysis Software7
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Design for Data Analysis
for Engineers
Example 2 Cont.
Operating System Compatibility7
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Design for Data Analysis
for Engineers



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
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Introduction to DFDA (Design for Data Analysis)
Define DFDA for Engineers
DFDA and SEF (Systems Engineering Fundamentals)
Examples
Summary
References
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Design for Data Analysis
for Engineers
Summary
Use data collection software to collect data as close as possible to
the software format that will be used to analyze the data
Make sure the software package chosen has the functions you need
Software should be able to process data from different sources
The goal is consistent results
Reach out to other engineers and developers
Participate in the data analysis community
Ford Employees - Contact Tjuana Buford (Core Developer) –
[email protected] for access to the Data Processing tool
presented in example 3.
The Death tool is used by 400+ engineers within Ford PD
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Design for Data Analysis
for Engineers






Introduction to DFDA (Design for Data Analysis)
Define DFDA for Engineers
DFDA and SEF (Systems Engineering Fundamentals)
Examples
Summary
References
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Design for Data Analysis
for Engineers
References
1. http://en.wikipedia.org/wiki/Data_analysis
2. http://asgard.kent.edu/systems1/dataanal/tsld005.htm
3. Ford Motor Company – Systems Engineering Fundamentals Reference Guide (2005)
Ford Confidential
4. Broy, M., Pretschner, A., Salzmann, C., and Stauner, T., "Software-Intensive Systems
in the Automotive Domain: Challenges for Research and Education," 2004-01-1780,
SAE World Congress, Detroit, Michigan 2006
5. NIST/SEMATECH e-Handbook of Statistical Methods,
http://www.itl.nist.gov/div898/handbook/, 2007.
6. Bronson, Gary J., “C for engineers and scientists an introduction to programming”,
West Publishing company, St. Paul, MN 1993
7. http://en.wikipedia.org/wiki/Comparison_of_numerical_analysis_software
8. DEATH Design developed by Erwin Peters 2002, Ford Motor Company Confidential.
Copyright 2002-2007 All Rights Reserved
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