lesson 2 digital data acquisition and data processing

7/18/2019 Lesson 2 Digital Data Acquisition and Data Processing

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Digital Data

Acquisition and Processing for

Nondestructive Evaluation

Prof. Krishnan Balasubramaniam

Professor of Mechanical Engineering andHead of Centre for Nondestructive Evaluation

Department of Mechanical Engineering,

IIT Chennai 600 036

[email protected]

Lesson 2: Advanced Nondestructive Evaluation

mailto:[email protected]

mailto:[email protected]



Why Data Acquistion ?• Complex specifications leads to difficulty in

decision making.

• Field environment requires un-biased, quickdecisions.

• Seeing is sometimes deceiving.

• Quantitative information is essential.• Degree of automation is process and

product dependent.



Why Data Analysis ?• Reduce Data Volume

• Noise Reduction

• Feature Extraction for Discrimination

Analysis

• Multi-facet approach to data interpretation

(time, frequency,…domains)



Nondestructive Evaluation

Excitation

Source

TestSpecimen

Signal

Conditioning

Inverse

Model

Input Transducer

Output Transducer



Systems Based Approaches

Signal

Conditioning

Output

Transducer

Response

Characterization

Results

Signal Conditioning

Sampling and Quantization

Signal Enhancement

Signal Restoration

Inverse

Model



Signal Classification• Analog Digital

-4

-3

-2

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9

-4

-3

-2

-1

0

1

2

3

4

1 2 3 4 5 6 7 8 9



Introduction To Linear Systems

Signals

x(t)

tContinuous-Time Signal

Discrete-Time Signal

x[n]

x(t)Input

y(t)Output

x[n]Input

y[n]Output

1

2

1

3

1.5

n0 1 2 3 4

Continuous-Time System Discrete-Time System



If x1[n] y1[n]

x2[n] y2[n]

Linearity ax1[n] + bx2[n] ay1[n] + by2[n]

Similarly, ax1(t) + bx2(t) ay1(t) + by2(t)

Properties of Systems

Linearityx[n]

x(t)

y[n]

y(t)



Types of Data

• Transient 1 dimensional Signals

• Spatially Distributed-Time Signals

• Static Images (photos)• Dynamic Images (movies)



Signal Domains• Time/Spatial Domain

• Frequency Domain (Amplitude)

• Frequency Domain (Phase)

• Time (Space)-Frequency Domain (Wavelets,

STFT)

• Transfer Function Domains



Digital Signal Analysis• Acquire digital data using DAQ.

• Process Data for Noise Reduction.

• Extract Features

• Multi-parameter Discrimination.

• Damage Estimation.• Damage Reporting



DAQ System

Analog Digital



DAQ Hardware

• Analog to Digital Convertors

– Ni 5102/5112 – up to 2.5 GHz. Data Aq Boards

– DAQ Boards – Slow (1-1 MHz)

– DSO – Agilent, Tektronics

• Frame Grabbers



Image Grabbers



Triggering

• Allows you to efficiently capture short-

duration and high-speed events by

eliminating the need to continuously acquiredata while waiting for the event to occur

• Usually done using a voltage signal input.

• The Trigger level and +/- values are key.

• Both analog and digital triggers are

possible.



Speed of Acquisition

• Images of a moving toy motorcycle taken

with a progressive scan camera using

exposure times of 33 ms, 10 ms, and 1 ms,respectively



Nyquist CriterionSR at least = 2*f max



Digitization Basics• Horizontal Resolution (Sampling 100 kHz.)

• Vertical Resolution (Bits =16 bits)

• Amplitude Range (+10V to – 10V)

• Gain (10 dB)

• Multiplexing

• Effective bits

R v = 20/216 = 3.05* 10-4V

R h = 1/100*103

=10-5

s



Delays and Offsets and …..

• DC Bias Offset

• Trigger Delay

• Pre-trigger

• Equivalent Time Sampling for stationary

signals



Interfaces

• RS-232• RS-170

• IEEE 488.2 GPIB

• IEEE 1394• PCI busAnalog

Cameras

Parallel Digital

Cameras

Camera Link

Cameras

IEEE-1394

Cameras

Data Rate Slow Fast Fast Slow

Spatial

Resolution Low High High Medium

Functionality Simple andeasy

Advanced Advanced Simple andeasy

Pixel Depth 8-bit to 10-bit Up to 16-bit Up to 16-bit Typically 8-bit

Cabling Simple BNC

cabling

Thicker, custom

cabling

Simple,

standard

cabling

Simple,

standard

cabling

USB 2.0RJ 45

PCIe



Frequency Bandwidth



Time Frequency• Fourier Transform

F(t) = aicos(wi)

• Fast Fourier Transform (FFT) – Complex Operation

• Discrete Fast Fourier Transform (DFFT)



Time-Freq. Paradox• A classic trade-off.

• High resolution in Time – Poor resolution in

Freq. – vice versa.

• New time-freq. Analysis such as wavelets

allow a more balanced approach –

SIMULTANEOUSLY.

EXAMPLE



Data Reduction• Fourier Domain

• JPEG

• MPEG

• CODEC

• WT

• …….

EXAMPLE



NOISE Reduction• SNR improvement

• Filtering

• Time Averaging

• Spatial Averaging

• Split Spectrum Processing

• Wide Spectrum Processing

EXAMPLE



Median FilteringRAW DATA MEDIAN FILTERED



Filte



Feature Extraction• Time Domain Features

– Peak to Peak Amplitude, +Peak/-Peak, Pulse

Duration, ……….. • Envelope Domain Features

– Energy, Skewness, Kurtosis ,…….

• Frequency Domain Features

– Peak Frequency, Mag of Peak Freq., Slope ofPhase Spectrum…. EXAMPLE



Multiple DomainsThe figure below shows the RF signal and the Amplitude Spectrum. Controls such as

Zeropadding, Undersampling, phase wrap/unwrap, window type, are provided. The

signal can be windowed by dragging the ends of the red bar over the RF signal.



Multiple DomainsThe figure below shows the Envelope of the RF signal and the Phase Spectrum. It is possible to

analyze several signal simultaneously by using data name patterns using wildcard (*).



Feature ExtractionThe extracted features are imaged here using a intensity plot. Each

sample is displayed separately and the selector can be used to

dynamically change the feature type. The histogram is also provided forthe 5 samples. The color bar and the # of bins can also be dynamically

changed. Mean and Standard deviation are provided for each sample.



Discrimination AnalysisIt can be observed by looking at the two feature scatter plot, that the samples 1 and 2

are clustered near the origin (0,0) location while the Samples 3 and 4 are at relatively

large distance from the origin.

Y-Feature

X-Feature



Some Applications

• Find the hidden echo and reduce dead zoneeffect. `

• Find thickness of thin coatings/structures infrequency domain

• Find Grain Characteristics from noise dataanalysis.

• Discrimination between defect types.

• Computer Aided Tomography

EXAMPLE

EXAMPLE

EXAMPLE



Defect DiscriminationExtraction of Signal

Shape will allow for

discrimination between

defect types.

Digital Industrial Laminography



Digital Industrial Laminography Ewert, Redmer, BAM-VIII.3

Camera trip through the object:

3D- reconstruction

of the crack range afternumeric filtering.

Reconstruction from the

line scan measurements

at 180 kV

Mechanized Weld Inspection

by Tomosynthesis

from 5 Projections

3D Representation



Summary• Data Acquisition is the single most important step

in an NDE procedure.• Good Data Acquisition, Data Archiving, Digital

Signal Processing, Discrimination Algorithms areall now feasible with low cost hardware and

software.• Good DAQ is a combination of hardware,

software, and settings, and the data is only as goodas the weakest link in this chain.

• Signal Analysis solves problems, previouslydeclared un-solvable.

• Signal Analysis will not fully compensate for poordata acquisition since new information cannot be

lesson 2 digital data acquisition and data processing

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