FCP Short Course

FCP Short CourseData Validation and Reduction for Fatigue

Stephen D. DowningMechanical Science and Engineering

© 2001 - 2015 University of Illinois Board of Trustees, All Rights Reserved

ME 431 - Lecture 21 © 2015 Stephen Downing, University of Illinois at Urbana-Champaign, All Rights Reserved 1 of 29

Outline

Test PlanningData AcquisitionData ValidationData Reduction Modes


Data Collection Exercise

Vehicle Instrumentation

Test Planning

Data Collection

Data Validation

Data Analysis


Why Do We Collect Data?

Assess product performance Estimate product durability Establish customer usage Model Verification Put Out Fires Other


Data Collection Equipment

Analog Tape RecordersDigital Tape Recorders Solid State Digital DevicesData Loggers Time History Recorders Field Computers


Data Collection Equipment


Field Data Collection

Field tests are expensive Do it in one trip Do I have the right data? Do I have enough to be representative? Is it of sufficient quality and accuracy?

Good data acquisition equipment Rugged Easy to use Flexible


Huge Investment


Durability Testing

Test Article

Strain Gages

Data Collector


What Is Data Validation?

Process of verifying that the data collected meets the objectives set forth during test planning

Data quality is sufficiently high Data quantity is enough to ensure statisical

relevancy Data can sometimes be corrected


What Kind of Effort Is Validation?

Most laborious, least interesting form of data analysis

Time consuming - 20% to 50% of total data analysis effort

Critically important - bad data begats bad analysis


Data Validation Steps

Visual Inspection

Quality Tests

Sufficiency Tests

Corrective Action

Data Transfer


Outline

Time History Time at Level Histograms Sequential Peak Valley Peak Valley Matrix (Markov)Rainflow Matrix FFT


Time History

Burst History

Time at Level

Peak-Valley

Time HistoryRange - Mean To - FromRange Only

Rainflow/ Peak-Valley

Matrix

Relationship of Modes

FFT


Time History - everythingBurst History - finding rare eventsSequential Peak Valley - durability analysisTime @ Level Histogram - usage and performance

determinationPeak Valley Matrix - statistical history characterizationRainflow - durability analysis and statistical history

characterizationFFT –everything like a time history but in the frequency

domain

Data Mode Uses


Time History Maintains amplitude Maintains sequence Maintains phase between multiple channels Maintains frequency content


Burst History (Triggered) Maintains amplitude Maintains sequence Maintains phase between multiple channels Maintains frequency content


0 5.49time-10

10

Load

Peak Valley Extraction

Maintains amplitude Maintains sequence Destroys phase between multiple channels Destroys frequency content


Time At Level Histogram Maintains statistics Destroys sequence Destroys phase between multiple channels Destroys frequency content

Counts the total number of samples in each input signal category.0

0 200-100

100

2

8910

76543

1

7


0 1.4Collection Time (secs)-400

400

Volts

-400 400Volts0

79

Counts

Counts the total number of samples in each input signal category.

1D Time At Level


0 200Time-100

100

Sine

0 200Time-100

100

Cosine

-100

Sine

-100 100Cosine

100

A

A

B

B

2D Time At LevelCounts the total number of samples in each unique combination of input signal categories

Maintains statistics Destroys sequence Maintains time between

multiple channels Destroys frequency content


Counts the total number of samples in each unique combination of input signal categories

Cosine

0 200Time-100

100

-100

100

Sine

-100

100

Sine

-100

100

Cosine

0

2

Cou

nts

2D Time At Level


0 5.49time-10

10

1

2

8

9

10

7

65

4

3

B

C

D

E

F

G

H

RANGE

RANGE = | A - B | MEAN = ( A + B ) / 2

PEAK VALLEY PAIRS = AB, BC, CD, DE, EF, FG, GH, HI, IJ, …...

MEAN J

A

Peak Valley Matrix


0 27Scans-400

400

0 800Range (uStrain)0

18

Counts

Peak Valley Matrix (Range Only)

Maintains amplitudes Maintains sequence Destroys phase between



0 27Scans-400

400

RangeMean 50 150 250 350 450 550 650 750-350-250 1-150 1 1 1-50 5 150 6 1150 6 3 1250350

Peak Valley Matrix (Range-Mean)




0 27Scans-400

400

FROMTo -350 -250 -150 -50 50 150 250 350-350 1-250 1-150 1-50 1 1 4 150 3 6 1150 1 5250 1350

Peak Valley Matrix (To-From)




Rainflow Counting




0 5.49time-10

10

1

2

8

9

10

7

65

4

3

A

B

C

D

E

F

G

H

RANGE

PEAK VALLEY PAIRS = AB, BC, CD, DE, EF, FG, GH, HI, IJ, …...

MEAN J

RAINFLOW PAIRS = AB, CD, EF, GH, IJ, …...

Rainflow Counting


FFT (How’s Fourier Work?)

Time

Stre

ss

FrequencyFF

T St

ress

Time Domain Frequency Domain

Fourier Transform

Inverse Fourier Transform


How’s Fourier Work?

Frequency

Am

plitu

de

FourierTransform

etc...

(Theory of Fourier, 1768-1830)

FCP Short Course

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