U nited A rab E mirates U niversity C ollege of E ngineering Graduation Project (II) Course

1. United Arab Emirates University College of Engineering Graduation Project (II) Course Design and Implementation of Noise Control System Using Data Acquisition with MATLAB • Group members: • Mona Al Abri 200220567 • Khawlah Al Shehhi 200212436 • Moza Nagher 200211359 • Amal Al Manei 200202464 Advisor: Dr. Qurban Ali Second Semester 2007-2008

2. Outline • Executive Summary • Objectives • Background Theory • MATLAB Simulation • Hardware components • Off-line modeling • Problems • Conclusion

3. Executive Summary (1/3) • The project is about designing and implementation of active noise control system in a duct using data acquisition with MATLAB. • The basic idea is to cancel the (low frequency) unwanted disturbance.

4. Executive Summary (2/3) • How? • By generating an anti-phase signal.

5. Executive Summary (3/3)

6. Objectives

7. Noise Problem and Ways of Noise Reduction • Acoustic noise problems become more and more evident as increased numbers of large industrial equipment such as: • Engines • Fans • Transformers • compressors

8. Noise Problem and Ways of Noise Reduction (1/2) • There are two approaches to control acoustic noise 1. Passive Noise Control Barrier Noise 2. Active Noise Control = Residual Noise

9. General Applications of ANC Systems (2/2) • Automotive: Noise attenuation inside vehicle passenger compartments. • Appliances: Air conditioning ducts, refrigerators, washing machines, vacuum cleaners, and so on. • Industrial: Fans, air ducts, transformers, power generators, blowers, compressors, pumps, wind tunnels noisy plants, headphones, and so on. • Transportation: Airplanes, ships, boats, helicopters, diesel locomotives, and so on.

10. MATLAB Simulation of FXLMS Algorithm with Practical Conditions • Single-tone noise • Multi-tone noise

11. Simulation for Single-Tone Noise • Simulation Parameters:

12. Simulation Results for Single-Tone Noise (1/3)

13. Simulation Results for Single-Tone Noise (2/3)

14. Simulation Results for Single-Tone Noise (3/3)

15. Simulation for Multi-Tone Noise • Simulation Parameters:

16. Simulation Results for Multi-Tone Noise (1/3)

17. Simulation Results for Multi-Tone Noise (2/3)

18. Simulation Results for Multi-Tone Noise (3/3)

19. Hardware Components (1/7) • PVC pipe • Length = 168 cm. • Diameter = 16 cm.

20. Hardware Components (2/7) • Two reference microphones. • Two error microphones. • Frequency response: 30Hz-20KHz • Connecting wire: 2 x 4000mm • Operating Voltage: 1.5 – 3V

21. Hardware Components (3/7) • 2 loudspeakers • 1 noise source loudspeaker • 1 cancelling loudspeaker • Frequency Response: 45Hz to 20KHz. • Diameter = 16 cm

22. Hardware Components (4/7) • Power amplifier with two channels.

23. Hardware Components (5/7) • Fan (noise source)

24. Hardware Components (6/7) • Function Generator • To produce a signal with low frequency • Output the signal through the loudspeaker

25. Hardware Components (7/7) • Pentium-4 desktop PC with full version of MATLAB, and including Data Acquisition and Signal Processing Tool Boxes

26. Prototype

27. Programs and Software used in ANC System • MATLAB with data acquisition tool box (version 1.7 or above). • Provides a complete set of tools for analog input, analog output, and digital I/O from the data acquisition card. • DAQ Adapter • allows MATLAB users direct access to analog and digital I/O data

28. Off-Line Modeling Technique (1/2) • FXLMS algorithm requires knowledge of the transfer function of the: • Primary path P(z) • Secondary path S(z) • Feedback path F(z) • Off-line modeling can be used to estimate these paths during an initial training stage. • At the end of the training interval, the estimated model is fixed and used for ANC operation.

29. Off-Line Modeling Technique (2/2) • The off-line modeling procedure is summarized as follows: • Generate random noise signal. • Obtain desired signal from a sensor (microphone). • Apply adaptive filter algorithm to get the FIR model.

30. Off-Line Modeling P(z) F(z) S(z)

31. 1. Primary Path P(z): Generate a random noise at the noise source point, and record the signal at the location of error microphone. Take this input-output data to MATLAB, and use LMS to get an appropriate order FIR model for the primary path P(z). System Identification (1/3)

32. 2. Secondary Path S(z): Generate a random noise at the location of cancelling loudspeaker. Record the signal at the location of error microphone. Take this input-output data to MATLAB, and use LMS to get an appropriate order FIR model for the  S(z). System Identification (2/3)

33. 3. Feedback Path F(z): Generate a random noise at the location of cancelling loudspeaker. Record the signal at the location of reference microphone (noise source point). Take this input-output data to MATLAB, and use LMS to get an appropriate order FIR model for the  F(z). System Identification (3/3)

34. MATLAB Code for recording the Reference signal and the Desired Response

35. Code for Off-line Modeling

36. Problems • DAQ • Data acquisition tool box • FIR model for the paths • Time limitation

37. Conclusion • The ANC system was studied and analyzed. • LMS and FXLMS was implemented by MATLAB. • The materials of the prototype were selected and bought. • The prototype was built.