chapter6_signal processing and analysis

8/19/2019 Chapter6_Signal Processing and Analysis

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Signal Processing, Analysis & VITopics:

• Introduction

• Conversion form Time toFrequency Domain

• Typical SpectrumExamples

• The Fourier Transform• Discrete Fourier

Transform

• Digitization

• Spacing of Lines• Resolution

• Sampling and Digitization

• Problems of Sampling

• Effect of Undersampling

• How to take care of Aliasing

• Anti-Alias Filter

• Windowing

• Averaging• Real Time Bandwidth

• Coherence

• Correlation Coefficient

• Auto Correlation• Cross Correlation

• Transfer Function

Advanced Signal Processing

Wavelet TransformHilbert-Huang Spectrum

Virtual Instrumentation

Signal Analysis:Cepstrum

Enveloping, HFRT


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Introduction

• Digital Signals:

Sampling, Digitizing, ADC, Multiplexer • Fourier Transform, FFT

– Aliasing

– Leakage

– Windowing

– Averaging• Coherence

• Correlation


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3e.g., trying to detect first sounds of

bearing failing on a noisy machine… No masking of smaller ones by larger ones

Conversion form Time to Frequency Domain


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Typical Spectrum Examples


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• The transformation from the timedomain to the frequency domain isbased on forward Fourier Transform

• and back again to time domain from the

frequency domain is based on inverseFourier Transform

( ) )(2

1

∫

∞

∞−

= ω ω π

ω d e X t x t i

( ) )(∫∞

∞−

−= dt et x X t iω ω

Valid for both periodic and non-periodic signals

The Fourier Transform


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• To compute Fourier Transform digitally

• Instead of getting continuous function, weget discrete values of the FT

∑−

=

−

Δ=Δ

1

0

2

)()(

N

n

N

mn j

et n x N

T f mSx

π

∑

−

=

=

1

0

2

)()(

N

k

N

nk j

ek F n x

π

( ) )( 2

∫

∞

∞−

= ω π d e f S t x ft j x

( ) )( 2∫∞

∞−

−= dt et x f S ft j xπ

where, m = 0, ±1, ±2, ±3,….

continuous discrete

Discrete Fourier Transform


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HOW MANY NUMBER OF LINES??

FFT Transforms these N equally spaced

samples to N/2 equally spaced lines in

the Frequency Domain

FT / FFT requires digitized samples of the input for its digital calculations

N2 Multipln

Vs N log2 N

Digitization


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Maximum measurable frequencyRecord Timeof

1*

2max

Period

N f =

Lowest Nonzero Measured Frequency = = Δf

This Δf is also the spacing between the lines of frequency spectrum

To increase the frequency range of our measurement,

sample at faster rate,

so that for same number of lines (N), shorter period of time record

Record Timeof

1

Period

Ns

i

f i f = where,

i=0,1,2,…,N/2

e.g., if sampling frequency f s is 5000Hz, for timerecord of N=4096 samples, frequency lines are at

0Hz, 1.22Hz, 2.44Hz, 3.66Hz,…..,2500Hz

Spacing of lines ???


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Sampler & ADC FFT Processor DisplayInput Voltage

ADC:

High Resolution

and Linearity

For 70dBdynamic range,

12 bit resolution

required

Processing

Software(eg. LabView)

Digitizing


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The resolution of Data Acquisition Board

with n-bit resolution is

Resolution = Range/2n

e.g. for ±5Volts range with 12-bit system,

we get a resolution of 10/212=2.44mV,

whereas with 16-bit boards, for the same

range, we get a resolution of 0.1528mV

Resolution


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Multiplexer (MUX)

Programmable

Gain Amplifier

(PGA) ADC

Input

AnalogSignal

Output

Digital

Signal

Digitization conversion rate depends on

Channel switching time for the multiplexer (single/multichannel rate)

Gain value of the PGA

Time required at ADC for conversion

Sample

& Hold

Circuitry

Conversion

Circuitry

ADC

Sampling and Digitization


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Two signals are said to alias if

the difference of their

frequencies fall in the frequencyrange of interest

Problems of Sampling


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If input frequency in

signal f in is higher

than samplingfrequency f s, a low

alias frequency

(= f in- f s) is

generated

If f s>2*f

max, the alias

products will not fall

within f max

How to take care of Aliasing


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• In practice, input signal may contain

some spurious unknown

frequencies that are greater than f s

• A low pass filter (Anti-Alias Filter)

after the sampler that filters all f

above fmax followed by sampling @

fs>2*fmax, will avoid aliasing

• Minimum Sample Rate requirement

is called Nyquist Criterion that is

stated as,

fs≥2*fmax

Anti-Alias Filter


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Three Classes of Frequency Response


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FFT Assumption – time record

repeated throughout all time

Time Record


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18Input signal periodicin time record

Input signal NOTperiodic in time record

Time Record


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Windowing helps FFT ignore thediscontinuities at the ends and

concentrate at the middle

Windowing


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Problem:

Improper Time Record

Effect: Leakage

Solution: Windowing

Sharp phenomenon in one domain

convolved in other domain


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• Hanning Window• Commonly used for most

signals (periodic andrandom)

• Uniform Window• Weighs all of the time

record uniformly

• Used for transient signals

• Flattop Window• To take care of rounded

top of the Hanning window

• Used where accurateamplitude is essential

• But at the cost offrequency resolution

Types of Windowing Function


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Self Windowing Functions

These functions generate

no leakage in the FFT

Hamming

Blackman

Extra BlackmanBlackman Harris

Triangle

Cosine Tapered , etc

Other Windowing Function


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• In practice, signals are mix of deterministic

component and noise component

• Desired signal is to be separated/extracted from

significant level of noise

• Averaging: RMS Averaging, Linear Averaging

– RMS Averaging:

dt t xT

x

T

RMS ∫=0

2 )(1

Averaging


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Synchronizing signal reqd.

Several time records

added to reduce noise

effects

The more averages we

take,

the closer the noise

comes to zero and we

keep improving

the signal to noise ratio

Linear Averaging


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• Real Time Operation:

Recall that

The frequency span where the time record is equal to the FFTcomputation time is called Real Time Bandwidth

Time Record 1 Time Record 2 Time Record 3

FFT 1 FFT 2

Record Timeof

1*

2max

Period

N f =

Real Time Bandwidth


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• Measures power in the response that iscaused by the power in the input/reference

• It is the output power that is coherent with

input power

• Coherence value ranges between 0 and 1

– 1 : All the o/p power at a freq. is caused

by the input

– 0 : no o/p power is due to input

)()()()(

2

2

f G f G f G f

yy xx

xy

xy =γ Extraneous uncorrelated noise in measurements of x

and/or y cause coherence to approach 0

CoherenceMeasure of Linearity


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Correlation Coefficient


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∫ += ∞→T

x xT

xx dt t f t f T

Lim R

0

)()(1

)( τ τ

1 *( ) ( ) ( ) xx x x R F S f S f τ − ⎡ ⎤= ⎣ ⎦Here, Sx(f) is Fourier Transform of f x(t)

Autocorrelation: similarity between a

signal and time-shifted version of itself

Correlation is a measure of the similarity between two

quantities (vibration waveforms/signals)

correlation coefficient is a normalized measure of the strength of the linearrelationship between two variables.

Correlation


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29 Autocorrelation Function as a function of time shift

Auto Correlation


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Detection of periodicity (mostly desired signal) buried in Noise.

Noise Sine Wave

Important difference between autocorrelationand averaging is that synchronizing trigger is

not required for the former

Hence useful in Signal identification

problems like Radio astronomy and passive

sonar

Correlation


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• Cross Correlation Function – To determine to what extent a signal measured at one point originates

from a particular source, and with what time delay.

– To detect the existence of a signal (not necessarily periodic) buried inextraneous noise

• Cross Power Spectrum – frequency transform of the cross correlation function – Used for measurement of Transfer Function

0

1( ) ( ) ( )

T

xy x yT

R f t f t dt LimT

τ τ →∞

= +∫

( ) *( ) ( ) ( ) ( ) xy xy x yG f F R S f S f τ ⎡ ⎤= = ⎣ ⎦

Cross Correlation


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Measure of similarity between two different

non-identical signals is cross correlation

functionThe cross correlation can be used to detect

the presence of one signal in another signal.

If the same signal is buried in both the

waveforms, it will be reinforced in the crosscorrelation function, whereas the noise

which is uncorrelated will be reduced

Practical Examples: Radar, Active Sonar,

Room Acoustics, Transmission Path Delays,

in which input stimulus can be measuredand used to remove contaminating noise

from the response by cross correlation

The frequency transform of the cross

correlation function is Cross PowerSpectrum

Cross Correlation


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Cross Correlation


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• Defined as the complex ratio of the output

to the input of the system as a function offrequency

• Impulse Response

*

*

( ) ( ) ( )( )( ) *( ) ( ) ( ) ( )

y y yx x

x x x xx

S f S f G f S f H f S f S f S f G f = = =

[ ]1( ) ( )h t F H f −=

Transfer Function


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Decomposition of time domainsignal in frequency domain

Frequency (Hz)

0 10 20 30 40 50 60

A

m p l i t u d e

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Drawback: Time information is lost

Problem not serious for stationary signals

Important for signals having non-stationary characteristics

Ex. Drift, trends, abrupt changes, beginnings & end of events


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Fourier Analysis

To find different frequency components

Amplitudes of different components


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Fourier Analysis

Breaking down a periodic signal into its constituent sinusoids ofdifferent frequencies

∑

−

=

−=

1

0

2

)(

1

)(

N

n

N

nk j

en f N

k F

π


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Cepstrum Analysis

• Cepstrum is defined as inverse Fouriertransform of the logarithm of the power spectrum

• If one or more periodic structures appear in aspectrum, each one appear as a distinct peak in

cepstrum

{ })(log)( 1 ω τ X S F c −=


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Cepstrum of gearbox vibration signal

Cepstrum for Spectrum

Quefrency for FrequencyRahmonics for Harmonics

Gamnitude for Magnitude


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Example of cepstrum of gear boxvibration signal


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Quefrency domain analysis

Mechanical Vibrations: S S Rao


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• Spectral analysis of gear faults gives arather confusing picture

• Cepstrum analysis is better suited in suchtype of faults and gives a clearer picture


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High Frequency Resonance Technique (Shiroishi et al.)

MFRT utilizes the fact that much of the energy resulting from adefect impact manifests itself in the higher resonant frequencies of

the system. Defect frequency if periodic, presents in the spectra ofthe enveloped signal. ALE enhances the spectrum of envelopedsignal by reducing broadband noise


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Wavelet Analysis

• Wavelet Transforms – Why & When?• Basic Theory

• Simple Examples• Case Studies-

FFT not able to detect

CWT proved very effective


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Time domain & itsFrequency Domain

Representation

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Time (Sec)

A m p l i t u d

e

0 50 100 150 200 250 300 350 400 450 5000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Frequency (Hz)

A m p l i t u d e

A 20Hz sinusoidal signal


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470 0.2 0.4 0.6 0.8 1 1.2 1.-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

Time (sec)

A m

p l i t u d e

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

Time (Sec)

A m p l i t u d e

+

Short Duration Transient Signal

Pure Sine Wave


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0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

-1.5

-1

-0.5

0

0.5

1

0 50 100 150 200 250 300 350 400 450 5000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Frequency (Hz)

A m

p l i t u d eResultant Sine wave +

Transient disturbance

Fourier Transform fails to

detect clearly, event ofdisturbance is lost

Perturbations/minute

changes localised intime are not revealedin FDS

Si l / T i t


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Wavelet Transform Locates thedisturbance in Time-Frequency

Representation

Signal w/o Transient

Signal with Transient


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Short Time Fourier Transform

Analyzing a small section of the signal at a time with Fourier Transform

Same Basis Functions (sinusoids) are used

Window size is fixed (uniform) for all frequencies

so all spectral estimates have same (constant) bandwidth


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Short Time Fourier Transform• Maps a signal into a two-

dimensional function of timeand frequency

• Technique is called windowing

the signal

• A compromise between thetime- and frequency- based

views of the signal

• Provides some info @ both

when & at what frequencies asignal event occurs


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Can we have something better?

• NEED? – Varying window size

• To determine more accurately either time or frequency

Wavelet Analysis – A windowing technique withvariable sized regions

Allows use of long time intervals where we needmore precise low-frequency information

& use of shorter regions where we want high-

frequency information


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Wavelet TransformFourier Transform –

signal broken into sinusoids

that are global functions

Wavelet Transform –

signal broken into a series of

local basis functions

called wavelets, which arescaled and shifted versions ofthe original (or Mother) wavelet


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Comparison ofTransforms

Frequencyinformation notavailable

Event (time)information lost

SimultaneousHigh resolutionin both Time &

Freq. domains NOT possible

Short data window of time T – B/Wof each spectral coeff is 1/T - wide


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Wavelet

Morlet wavelet (blue dashed) as a Sine curve (green)modulated by a Gaussian (red)

• Sine waves – basis functions for Fourier Analysis extends from

+∞ to -∞• Wavelets have limited duration that has an average value ofzero

• Sinusoids are smooth & predictable, Wavelets tend to beirregular & asymmetric


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• Wavelet means a small wave

• The function that defines a

wavelet integrates to zero• It is local in the sense that it

decays to zero when sufficientlyfar from its center

• It is square integrable, i.e., it hasfinite energy

Wavelet

∫

∞

∞−

= 0)( dt t ψ

∫∞

∞−

∞


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Wavelets

Signals with sharp sudden changes could be betteranalyzed with an irregular wavelet than with a

smooth sinusoid

In other words, local features can be better captured

with wavelets which have local extent


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Scaling

Scaling a wavelet means stretching (or compressing) it


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Scaling


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Shifting

Shifting a wavelet means delaying or hastening its onset


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Continuous Wavelet Transform

Sum over all time of the signal multiplied by scaled and shifted versions of the wavelet

Ensures energy stayssame for all s&b


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Continuous Wavelet Transform

20Hz

50Hz

120Hz

290Hz


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Process of CWT

Dilate the mother wavelet

Redo the above sweeping

Sweep over the

entire span of thesignal


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Relation between scale & frequency

Fa = pseudo frequency ( for the scale value s )

Δ = sampling time

s = Scale

Fc = central frequency of mother wavelet in Hz.

Central frequency of the Morlet wavelet is 0.8125Hz

It is the freq. that maximizes the FFT of the wavelet or isthe leading dominant frequency of the wavelet

Δ

=

s

F F ca

Matlab Help Module


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Experimental Results

NO RUB

RUB


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CWT of the SignalsNO RUB

RUB


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PARTIAL/INTERMITTENT RUB

Partial

RUB

NO RUB


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CWT of Partial Rub


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ROTOR RUB DETECTION

• Localized (in time) rubbing is detectedusing wavelet transform

• Intermittent rub is better detected• High frequency components are also

localized in a cycle of rotation


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Gear Fault detection using WaveletsDifficult to evaluate the spacing and evolution of sideband families

Several gear pairs other mechanical componentsContribute to the overall vibration.

Local faults in gears produce impactstransient modifications in vibration signals.

Signals have to be considered as non-stationary

Most of the widely used signal processing techniques are based on theassumption of stationarity and globally characterize signals.

Not fully suitable for detecting short-duration dynamic phenomena.

Wavelet transform (WT) is better suited in such situations.


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From the above WT map of TSA vibration, it is possible to clearlydistinguish the transient effects introduced by the cracked tooth.

Moreover, such a procedure makes it possible to localize the damage in mostof the cross-sections.

Experimental study conducted by Dalpiaz


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WAVELET TRANSFORM

• Wavelet Transform is an excellent tool fordetection of non-stationary vibration

signals

• Features that are obscured during Fourier

Transformation are revealed with better

clarity• Time information is preserved


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AcousticEmission

Technique

AE is the phenomena of transient elastic wavegeneration due to a rapid release of strain energycaused by a structural alteration in a solid materialunder mechanical or thermal stresses. The most

commonly measured AE parameters are peakamplitude, counts and events of the signal.

Some studies indicate that Acoustic emission

measurements are better than vibrationmeasurements and can detect a defect even beforeit appears in vibration acceleration.


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Results on test rig simulating very slow speed

rolling bearings of Air Preheater (1.3-1.4rpm)


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HILBERT – HUANG TRANSFORMBASED ON

EMPIRICAL MODE DECOMPOSITION


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Empirical Mode Decomposition - Procedure

Step 1:Identify all the local

extrema (i.e. maxima

and minima), andthen connect all the

local maxima and

minima by cubic

spline lines.

Contd…


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Step 2:

From upper and lower envelopes of the

vibration data find mean

of it. Find difference (h1)between the original

signal and mean.

Contd…

Mean

h1


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Empirical Mode Decomposition- Procedure

Contd…

Step 3:If h1 is not an

IMF, h1 is

treated as theoriginal signal

and above

procedure isrepeated. After

repeated

shifting, i.e. up

to k times, h1kbecomes an

IMF (c1).

After three

shifting, still h3is not an IMF

h3

After nine

shifting, an IMF

is obtained.

IMF


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Contd…

Step 4:

Subtract c1 from the original signal to obtain residue (r1).

Now, the residue is considered as signal and step 1 to

step 3 are repeated to get next IMF.

Step 5:

The decomposition procedure is repeated until the residue

becomes monotonic function, from which no more IMFs

can be extracted.


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Empirical Mode

Decomposition

- Procedure


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Hilbert-Huang Transform based on EMD

Hilbert transform

Analytic signal

[ ] j ( )( ) ( ) j ( ) ( ) i t i i i i z t c t H c t a t e f = + =

[ ]2 2( ) ( ) ( ) ,i i ia t c t H c t = +Where,

[ ]( )( ) arctan ( )i

i

i

H c t t c t

=

Instantaneous frequency

d ( )( ) .d

ii

t t t

f w = Contd…


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Contd…

Signal: x = 0.5*sin(2*pi*0.1*t)

+ 2*sin(2*pi*0.01*t)

Sampling frequency = 1Hz No. of samples = 1000


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Contd…

Signal: x(t) = sin(8π t) for t ≤ 5

x(t) = sin(4π t) for 5 < t ≤ 10dt = 0.005; sampling frequency = 1/dt = 200Hz


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Contd…

f = fs*fc/afc = 0.8125Hz

fs = 200Hz

a = 58-106

(i.e. 1.53Hz

to 2.8Hz)

a = 28-52

(i.e. 5.8Hz to

3.12Hz)

f = fs*norm. freq

fs = 200Hz

f = 200*0.02

= 4Hz

f = 200*0.01

= 2Hz


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Use of HHT for analysis of rolling bearings

Time interval betweenconsecutive impacts =

3.5 – 4.0ms

Estimated characteristics

frequency = 250 – 286Hz

Outer race defect:

Characteristic frequency;

Theoretical estimated

frequency = 263-267Hz


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VIRTUAL INSTRUMENTATION


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Historical Perspective

Introduction to Virtual Instrumentation

Capabilities and functionalities

Case studies


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COMPUTERS IN INTRUMENTATION

Early days: Process monitoring and control – limited to large plants

Computer Hardware: Computing power, Bus-basedcomputers


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Advancements in Hardware:

Mainframes Miniframes Personal Computer (desktop)

Bus based computer architecture

PC and AT buses like VESA & EISA

In 1993, Intel came up with a standard called

Peripheral Components Interconnect (PCI)

most commonly used even today

PCs came with Interrupt (IRQ) and Direct Memory Access (DMA)structure permitting fast data transfers with peripherals


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Development of Buses allowed easy interfacing

Buses are shared data highways on which data, commands,etc., move and are shared by various components, making it possible to add additional modules in a simple and systematicmanner.

Buses: Internal to computer (UNIBUS, PCI, ISA,etc.)

or External (e.g., GPIB, USB, Firewire, etc).

Earlier each interface problem was unique; i.e., to connect 12

instruments to 5 different computers required 12x5=60 uniqueways.


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SOFTWARE

Earliest operating systems such as VAX-VMS dominated the controlapplications

UNIX and its variants HPUX – control applications

Microsoft:

DOS: Integration of device drivers in OS – bigAdvantage

Windows: S/W for additional user hardware

integrated into the overall system

through drivers developed for specific devices.

GUI based OS: 1990.


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INTERFACES

Different types of devices require interfaces of differentcapabilities

Led to development of various buses and interfaces fordifferent purposes coexisting in the same system

In computer interfacing – Internal computer buses and

interface standard play a role

Internal bus: used to integrate add-on h/w into PC and act as platform for standardized h/w.

3 Buses are extended to cater to instrumentation

VME extended to VXI (VEM extension for instrumentation)

PCI extended to PXI (PCI extension for instrumentation)SCSI bus for peripherals SCXI standard


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INTERFACE STANDARDS

To connect external devices to the computer

Serial Connection: Sequential transfer of data

Recommend Standard No. 232 (RS232C),Universal Serial Bus (USB), Firewire, etc.

GPIB Connection: separate line for each bit,transfer is fast


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CRUX OF THE VI

Progressively moving the intelligence of the instrument

into software

H/w reduced to

actual sensors (thermocouples, accelerometers, etc.) and

actuators (switches, motors, valves, etc)

All signal handling, analysis and control done through s/w


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VIRTUAL INSTRUMENT is defined as

Industry standard computers

equipped with user-friendly application software,

cost effective hardware

and driver software

that together perform the function of traditional instruments.

PC Based,All major OS

Powerful GUI forquick development& implementation

of test, meas. &

control solutions

Use of General Purpose Data Acquisition

hardware as against custom hardware

Cutting edge H/W, low

costReliability & reduced

obsolescenceCheap & freely available from all

instrument manufacturers


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VI Compatible InstrumentAny instrument with computer interface (most commonly

RS232C or GPIB)

VI Based Instruments

required computer to operate & their operating capabilitiesare built around host computer


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Typical Modern Day DAS


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PROCESS

Transducer

Physical

Parameters

Signal Conditioning

Data Display

Signal Manipulation

Data

Record storage

Example: Thermocouple CRO or

Accelerometer Charge Amplifier CRO or Vibration Meter

EVOLUTION STAGES - STAGE I


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STAGE II:

Continuous Record: Data Logger / Data Recorder

Data logging on a hardcopy devices such as paper tape

Permanent Record

Primarily for checking anomalies, keeping track(trending)

Example: Temperature / pressure recorders in power

plants, nuclear reactors, process industries, etc.


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STAGE III:

Paper tape replaced by Magnetic tape, etc., (Analog Recording)

Can replay the original recorded signal

Ex. ECG recorders.


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STAGE IV:

Digital Processing of the signal comes on the scene

Microprocessor based instruments

Signal manipulation and display on display devicesDedicated microprocessor in the instrument

Eg. FFT analyzer, Digital oscilloscope.


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STAGE V:

Digital instrument interfaces with the computer

The Microprocessor in the instrument - controlled by the computer

Display could be on instrument screen

Microprocessor design could includeRemote ControlAccept predefined control commands

Remote functionality gets enhanced with time

The original instrument is gradually robbed of processing power.


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STAGE VI:

Computer processor replaces the instrument microprocessor

Computer screen replaces instrument display

Instrument control panel is translated to screen controls

Only bare minimum primarysignal conditioning(if required) is doneoutside the computer

The result of this is

software is the instrument(VIRTUAL INSTRUMENT)


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LabView LabWindows from National Instruments

HP-VEE From Hewlett Packard (now Agilent)

DasyLab from Advantech Genie etc.

LabView is extensively used in the Industry and academia

It provides a powerful and integrated environment for thedevelopment of instrumentation application.

SOFTWARES FOR VI

L bVIEW


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LabVIEWLaboratory Virtual Instrument Engineering Workbench

• National Instruments, Austin, Texas, USA

• 1986- interpreted package on Apple Mac

• 1990- compiled package – improved performance

• 1993- LabVIEW3 for Mac, Sun-Sparc & PC

• 1999- LabVIEW on Linux platform

• Version 5- networking support on smaller systems

• Version 6- Highly network oriented (LabVIEW 6i)

• Version 7- Expanded and simpler, use of Express utilities, Assistants, supports embedded systems, PDA, etc.


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JUST AS YOU CAN CONVENIENTLY

Type a document in MS Word

Make a spreadsheet in Excel

Make a presentation in powerpoint

Make a scientific program in Fortran

Maintain a database in FoxPro

Similarly you can conveniently

DEVELOP A VIRTUAL INSTRUMENT IN A VIRTUALINSTRUMENTATION SOFTWARE


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SALIENT FEATURES OF LABVIEW

Intuitive Graphical Development for test, measurement, and

control Fast development with interactive configuration and graphical

programming

Tight integration of real world I/O, measurement analysis and datarepresentation

Built-in tools for data acquisition, instrumentation control,measurement analysis, report generation, communication andmore

Application templates and thousands of example programs

Compiled for fast performance

Can operate on Windows, Linux, Sun Solaris, Mac OS


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ADD ON TOOLS

Very popular for specific needs of the user

IMAGE PROCESSINGJOINT TIME FREQUENCY ANALYSIS

OCTAVE ANALYSIS

PID CONTROL, FUZZY LOGIC CONTROL

NUMBER OF OTHER TOOLBOXES

Control & Simulation, Sound & Vibration, Machine Vision& Motion Control, Signal Processing

Latest version of s/w can develop stand alone executables


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Allied ProductsLabVIEW RT

For real time applications

Stable timing performance

BridgeVIEW

Industrial Control Applicationssupport for multi-level security, passwords,optimised for large number of sensors &

transducers


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LIMITATIONS

Inherent limitations of Digital Signal Processing

Not 100% real time

Sampling delays are present

Computation delays – limits the Max. Freq.(control purposes)

Information between samples is lost.


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Efforts to overcome these challenges

Computer based instruments Labview program downloaded to DSP Card

Use of Newer and Faster Buses

VXI (HP), PXI (NI), SCXI (for signal conditioning)

and Faster versions of GPIB

IEEE 488.1 transfer rates up to 1.8 MB/s (Std.) and

7.2 MB/s (HS488) using NI

DIFFERENT ROUTES


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DIFFERENT ROUTES

Computer based Instruments

Communication with Instruments Through GPIB

Use Data Acquisition Cards

L bVi G i l


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LabView: G programming language

Program Window Panel Window

Available Facilities : Control Elements

Function Elements

Using G Program, one can

Build the panel, Controls and Displays

Assign signal manipulation and signal analysis tasks

Wire the control functions and the displays

Make connectivity with the A/D card I/O channels

Execute the commands in Real Time on the signal


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A VIRTUAL INSTRUMENT PERFORMS THE SAME JOBAS REAL INSTRUMENT

Virtual Data Logger Virtual Signal Generator Virtual CRO Virtual Multimeter

Virtual FFT Analyser Virtual Frequency Meter

But, additionally, it gives more

Flexibility User defined Controls

Removes Obsolescence Low CostActual numerical values are available anytime for import/export

Add on Software for control and specific requirements

Avoid redundancy Reusability Reconfigurability

SOME TYPICAL TASKS THAT CAN BE PERFORMED ON THE


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SOME TYPICAL TASKS THAT CAN BE PERFORMED ON THE

SIGNAL

Measurement:

AC, DC Amplitude and frequency estimate

Amplitude and phase spectrum

Harmonic Analyser

Transfer Function

Signal Generation:

Arbitrary Wave

Amplitude and Phase Spectrum

White Noise

Impulse, Ramp and chirp pattern

Wi d i All l d H i H i


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Windowing: All commonly used types, Hamming, Hanning,

Uniform, Flat

Filters: Bessels, Butterworth, Chebyshev, etc

Statistical Estimation, Mean, Mode, Std.Deviation, Chi sq.distribution, T-distribution

Signal Processing:

Convolution Cross Power Derivative Integral

Hilbert, Fourier Transforms

Peak Finding, Power Spectrum

Curve Fitting: Linear, Nonlinear, Exponential

Linear Algebra: Linear Complex Equations, Eigen Valueanalysis


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References

www.ni.com

Measurement and Automation: Catalogue 2005 NationalInstruments

HP application NotesMatlab User manual

Virtual Instrumentation using LabVIEW - Sanjay Gupta& Joseph John, Tata McGraw Hill, New Delhi

chapter6_signal processing and analysis

Documents