UNIVERSITY OF NEW BRUNSWICK
FACULTY OF ENGINEERING
ME 4613
LAB 1 – INTRODUCTION TO DATA
ACQUISITION
Name: Dilraj Singh (3731937)
Group# 6
ME 4613 LAB 1 REPORT
Date Due: Tuesday, February 4th, 2025
I warrant that this is my own collaborative work, except for portions that are clearly
cited as the work of others.
Dilraj Singh
UNB Mechanical Engineering – Group 6
677 Windsor St, Fredericton, E3B 4G3
January 29, 2025
Phil Garland, PhD, PEng
University of New Brunswick
Dear Professor Garland,
I am submitting my lab report for the ME4613 vibration testing experiment. This lab aimed to
develop a practical understanding of data acquisition and signal processing by utilizing
MATLAB and National Instruments data acquisition boards. The experiment involved generating
modulated sine and square waves, collecting data through the DAQ system, and conducting
Fourier analysis to identify frequency components and compare them with theoretical
expectations.
The primary objective of the lab was to familiarize students with the procedures for acquiring
and analyzing analog voltage signals. Using MATLAB 2017, a script was developed to generate
and collect signals via a National Instruments DAQ system. The experimental setup consisted of
a BNC 2110 connector block interfaced with the DAQ system, with a coaxial cable connecting
the analog output (AO 0) to the analog input (AI 0). The acquired data was stored in a MATLAB
file for further post-processing.
During the experiment, two signals were analyzed: a modulated sine wave, defined as x =
5×sin(5t)×sin(100t), and a square wave with an amplitude of 5V and a frequency of 5 rad/s. The
sampling rate was set to 2000 Hz, ensuring adequate resolution for frequency analysis. The
collected signals were generated using MATLAB plots, and their frequency was evaluated
through Fourier Transform analysis (e.g., Figure A1, Figure A2).
During post-processing, we determined the frequency content of the collected signals and
compared them to the expected values. The Fourier transforms of the signals were computed
using MATLAB’s fft function, and the frequency peaks were identified (e.g., Figure B3, Figure
B4). For the square waveform, Fourier coefficients were manually calculated and compared to
the first 10 peaks obtained from MATLAB analysis (e.g., “see Appendix- C”).
To ensure the accuracy of our results, multiple trials were conducted while adjusting parameters
to minimize errors. The acquired signals were also examined for noise interference, and filtering
techniques were explored. Some minor distortions were observed, likely due to hardware
imperfections or signal processing limitations. Future experiments could incorporate better
shielding techniques and higher precision data acquisition equipment to enhance signal clarity.
Beyond the technical aspects, understanding the practical implications of these signals is crucial.
Accurate data collection plays a vital role in engineering applications such as structural
monitoring, vibration analysis, and control systems. By refining our approach, we can improve
predictive capabilities and enhance overall system performance.
Our results showed that the acquired signals largely matched theoretical expectations, though
minor deviations were noted. These differences were likely caused by environmental noise,
minor hardware inaccuracies, and software rounding errors. The enclosed graphs of collected
signals, FFT plots, and hand calculations provide further insights into data acquisition
methodologies and signal behavior under varying conditions.
The differences observed in the sine waveform frequencies were relatively small, suggesting that
the experimental setup performed well (e.g., Table D1). These minor discrepancies may have
resulted from background noise, equipment constraints, or data rounding errors. Future studies
should focus on refining calibration techniques to further minimize such variations.
In contrast to the sine waveform, the square waveform showed larger differences, especially in
higher-order harmonics (e.g., Table D2). These discrepancies can likely be attributed to the
limited resolution of the data acquisition system, nonlinearities in the hardware, and imperfect
signal reconstruction. The increased error at higher harmonics suggests potential aliasing effects
or sampling rate limitations. Further refinement of the setup and the use of higher precision
equipment could help minimize these errors.
In conclusion, this lab provided a comprehensive hands-on experience with data acquisition and
signal processing using MATLAB. Moving forward, refining the experimental setup,
incorporating noise reduction techniques, and exploring other signal types could further enhance
our understanding. This report underscores the importance of precise data acquisition in practical
engineering applications and highlights the need for continuous improvement in measurement
accuracy.
Sincerely,
Dilraj Singh
Appendix- A
Figure 1: Modulated Sine Waveform
Figure 2: Square Waveform
Appendix- B
Figure 3: FFT plot of Sine Waveform
Figure 4: FFT plot of Square Waveform
Appendix- C
Appendix- D
Table 1: Sine Waveform calculated and experimental frequency comparison table
Calculated Frequency
Experimental Frequency
(Hz)
(Hz)
Difference (%)
15.1197
15.0992
0.135768782
16.71126
16.6992
0.072219028
Table 2: Square Waveform calculated and experimental frequency comparison table
Fourier Coefficients
Magnitudes
6.366197724
2.122065908
1.273239545
0.909456818
0.707355303
0.578745248
0.489707517
0.424413182
0.374482219
0.335063038
6.34757
2.06672
1.18229
0.784789
0.551489
0.394818
0.341985
0.337235
0.328122
0.314896
Difference (%)
0.29346228
2.677958693
7.692659562
15.88552052
28.26281261
46.58532478
43.19561303
25.85087004
14.12895784
6.404348765