Digital Data

Acquisition and Processing for

Nondestructive Evaluation

Prof. Krishnan Balasubramaniam

Professor of Mechanical Engineering and

Head of Centre for Nondestructive Evaluation

Department of Mechanical Engineering,

IIT Chennai 600 036

balas@iitm.ac.in

Lesson 2: Advanced Nondestructive Evaluation

Why Data Acquistion ?

• Complex specifications leads to difficulty in decision making.

• Field environment requires un-biased, quick decisions.

• 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

Test

Specimen

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)

t Continuous-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

n 0 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

Linearity

x[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 acquire

data 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 Criterion SR at least = 2*fmax

Digitization Basics

• Horizontal Resolution (Sampling 100 kHz.)

• Vertical Resolution (Bits =16 bits)

• Amplitude Range (+10V to –10V)

• Gain (10 dB)

• Multiplexing

• Effective bits

Rv = 20/216 = 3.05* 10-4V

Rh = 1/100*103=10-5s

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 bus Analog

Cameras

Parallel Digital

Cameras

Camera Link

Cameras

IEEE-1394

Cameras

Data Rate Slow Fast Fast Slow

Spatial

Resolution Low High High Medium

Functionality Simple and

easy Advanced Advanced

Simple and

easy

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 2.0

RJ 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 Filtering

RAW 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 of Phase Spectrum…. EXAMPLE

Multiple Domains The 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 Domains The 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 Extraction The 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 for

the 5 samples. The color bar and the # of bins can also be dynamically

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

Discrimination Analysis It 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 zone effect. `

• Find thickness of thin coatings/structures in frequency domain

• Find Grain Characteristics from noise data analysis.

• Discrimination between defect types.

• Computer Aided Tomography

EXAMPLE

EXAMPLE

EXAMPLE

Defect Discrimination Extraction of Signal

Shape will allow for

discrimination between

defect types.

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

Camera trip through the object:

3D- reconstruction

of the crack range after

numeric 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 are all now feasible with low cost hardware and software.

• Good DAQ is a combination of hardware, software, and settings, and the data is only as good as the weakest link in this chain.

• Signal Analysis solves problems, previously declared un-solvable.

• Signal Analysis will not fully compensate for poor data acquisition since new information cannot be created.