Hi @solleti prabhakarchary ,
After reviewing your comments and achieving the desired outcome, not having access to pressure.mat file, I had to create synthetic pressure data, apply the necessary signal processing techniques, and visualize the results. Below is a detailed MATLAB code that fulfills your requirements.
% Parameters
sampling_frequency = 400000; % Sampling frequency in Hz
Nfft = 8192; % FFT size
overlap = 0.8; % Overlap percentage
no_of_segments = 2132; % Number of segments
window = hann(Nfft); % Hanning window for PSD calculation
% Generate synthetic pressure data
t = 0:1/sampling_frequency:1; % Time vector for 1 second
pressure_data = 0.5 * sin(2 * pi * 1000 * t) + randn(size(t)) * 0.1;
% Synthetic pressure data with noise
% Detrending the pressure data
mean_pressure = mean(pressure_data);
pressure_data_detrended = pressure_data - mean_pressure;
% Segment length and overlap samples
segment_length = floor(length(pressure_data_detrended) / no_of_segments);
overlap_samples = floor(segment_length * overlap);
% FFT of the detrended pressure data
Y = fft(pressure_data_detrended, Nfft);
f = (0:Nfft-1) * (sampling_frequency / Nfft); % Frequency vector
% Plotting the FFT
figure;
plot(f, abs(Y));
title('FFT of Unsteady Pressure Data');
xlabel('Frequency (Hz)');
ylabel('Magnitude');
grid on;
xlim([0 sampling_frequency/2]);
% Limit x-axis to Nyquist frequency axis tight;
% Power Spectral Density calculation using Welch's method
[pxx, f_pxx] = pwelch(pressure_data_detrended, window, overlap_samples,
Nfft, sampling_frequency);
% Plotting the Power Spectral Density
figure;
semilogy(f_pxx, pxx);
title('Power Spectral Density of Unsteady Pressure Data');
xlabel('Frequency (Hz)');
ylabel('Power/Frequency (dB/Hz)');
grid on;
xlim([0 sampling_frequency/2]); % Limit x-axis to Nyquist frequency
axis tight;
Please see attached.
1. Parameters Initialization: The code begins by defining the necessary parameters such as sampling frequency, FFT size, overlap percentage, and the number of segments.
2. Synthetic Data Generation: A synthetic pressure signal is created using a sine wave combined with random noise. This simulates real-world pressure data.
3. Detrending: The mean of the pressure data is calculated and subtracted from the original data to remove any DC offset, ensuring that the analysis focuses on the variations in the signal.
4. Segment Length and Overlap Calculation: The segment length is determined based on the total length of the detrended data and the specified number of segments. The number of overlapping samples is calculated accordingly.
5. FFT Calculation: The Fast Fourier Transform (FFT) is computed on the detrended pressure data, and a frequency vector is generated for plotting.
6. FFT Plotting: The magnitude of the FFT is plotted against frequency, providing insight into the frequency components of the pressure data.
7. Power Spectral Density Calculation: The pwelch function is used to compute the Power Spectral Density using Welch's method, which averages the periodograms of overlapping segments of the signal.
8. PSD Plotting: Finally, the PSD is plotted on a logarithmic scale, allowing for a clear visualization of the power distribution across frequencies.
By following the outlined steps, you can adapt the code to analyze real pressure data as needed. If you have any further questions or require additional modifications, please feel free to ask.
