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ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

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Page 1: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

ME 392Chapter 5

Signal Processing

February 20, 2012week 7 part 1

Joseph Vignola

Page 2: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Signal Processing

We have been talking about recording signal from sensors like microphones of accelerometers

Page 3: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Signal Processing

We have been talking about recording signal from sensors like microphones of accelerometers and

expressing the result as either a time history

Page 4: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Signal Processing

We have been talking about recording signal from sensors like microphones of accelerometers

expressing the result as either a time history or frequency spectrum

Page 5: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Signal Processing

Now we want to think about manipulating these signal once they are recorded

expressing the result as either a time history or frequency spectrum

Page 6: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

With motion data we often need to integrate of differentiate experimental data

Measured with

Displacement LVDT

velocity Laser Vibrometer

acceleration accelerometer

Page 7: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

With motion data we often need to integrate of differentiate experimental data

Measured with

Displacement LVDT

velocity Laser Vibrometer

acceleration accelerometer

Page 8: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

With motion data we often need to integrate of differentiate experimental data Measured with

Displacement LVDT

velocity Laser Vibrometer

acceleration accelerometer

Page 9: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

With motion data we often need to integrate of differentiate experimental data Measured with

Displacement LVDT

velocity Laser Vibrometer

acceleration accelerometer

Page 10: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

With motion data we often need to integrate of differentiate experimental data Measured with

Displacement LVDT

velocity Laser Vibrometer

acceleration accelerometer

Page 11: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

With motion data we often need to integrate of differentiate experimental data Measured with

Displacement LVDT

velocity Laser Vibrometer

acceleration accelerometer

Page 12: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

Integration is a process of finding the area under a curve

Page 13: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

Integration is a process of finding the area under a curve

For discreet data (sampled data)We can find the area of each of the trapezoids shown in the figure and add them up

Page 14: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

Integration is a process of finding the area under a curve

For discreet data (sampled data)We can find the area of each of the trapezoids shown in the figure and add them up

Page 15: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

Integration is a process of finding the area under a curve

For discreet data (sampled data)We can find the area of each of the trapezoids shown in the figure and add them up

So …

Page 16: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Integration and Differentiation

Differentiation can be thought of as finding the local slope

For discreet data (sampled data)We can find approximate the local Slope by the ratio of the rise over the run

As a practical matter is the Sampling interval

Page 17: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

So all I need to do to integrate discreet data is divide by

Integration in Frequency Domain

You know that

Assuming that

And that

Page 18: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

So all I need to do to differentiate discreet data is multiply by

Differentiation in Frequency Domain

You know that

And you remember that any signal can be reduced to sines and cosines

Assuming that

And that

Page 19: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

What Could Go Wrong?

For example

Page 20: ME 392 Chapter 5 Signal Processing ME 392 Chapter 5 Signal Processing February 20, 2012 week 7 part 1 Joseph Vignola

Time Shifting

Shift TheoremIf is Fourier Transform of then is Fourier Transform of