Why I Would use a PSD to Analyze Vibration Data?
ChatGPT vs NVH Expert
When it comes to vibration analysis the Power Spectral Density (PSD), like the FFT recently featured, stands out as a particularly powerful tool. But how does an AI, like ChatGPT, understand and explain the use of an FFT when compared to an experienced NVH engineer?
In this post we ask Kresimir Trdak, NVH Group Fellow and Lab Manager at Hutchinson’s Center for Research and Innovation, to analyze ChatGPT's response, and provide his own expert opinion to the same question - hopefully enriching your understanding of the intersection between advanced AI and human expertise in the field of vibration data analysis.
ChatGPT Prompt:
In 100 words or less summarize why I would use a PSD to analyze vibration data?
ChatGPT Response
A Power Spectral Density (PSD) analysis is used on vibration data to quantify the power present across different frequencies, providing a detailed view of the energy distribution over the frequency spectrum. This is crucial for understanding the nature and source of vibrations, identifying resonant frequencies, and detecting mechanical faults or wear in machinery. PSD helps in isolating problematic frequencies and assessing the impact of vibrations on structures and components, making it essential for predictive maintenance, system design, and ensuring the longevity and reliability of mechanical systems.
NVH Expert's Response - Kresimir Trdak
Knowing the word limitation imposed by the initial prompt, I found the response of ChatGPT to be concise and informative in general.
The aim of vibration measurement is to obtain an understandable and exploitable metric of the phenomenon under analysis. Vibration signals can be of deterministic or random nature. It is the randomness of vibration phenomena that is behind the usage of PSD in vibration measurements. Typical examples are flow induced vibrations (wind noise in cars, flow noise in jet planes) or vibrations induced by tire pavement interaction (road noise in the interior of cars).
For the vibration signal in time domain, the suitable metric is the root mean square value. The RMS value of the signal is a single number determined out of the signal time history over the time T (the duration of the signal acquisition). The RMS value squared gives the power of the signal. This one is related to the class of quadratic statistic descriptors of random signals.
The PSD – power spectral density – is the equivalent metric in frequency domain. The great advantage of PSD is that it is not a single number – it is a function depending on frequency – it takes the form of the X – Y sequence with X values being frequencies and Y values being spectral power densities of the signal. This X-Y sequence can be plotted in a diagram – spectrum – which allows to analyse measured vibrations and possibly understand what originated them. The PSD is related to the RMS through the power - the integral of the PSD over the frequency gives the power of the signal.
PSD Diagram Example:
The PSD of a signal acquired over the time T is calculated by using the FFT algorithm. The calculation procedure is not straightforward and implies the operations of time windowing and signal sectioning. These are the reasons why the frequency based PSD and time based RMS can deliver slightly different values for the power of the signal.
For more information on PSDs and their relationship with FFTs I invite you to check out these posts:
- Vibration Analysis: FFT, PSD, and Spectrogram Basics
- Comparing the Fourier Transform, the Power Spectral Density, and the Aggregate FFT for Vibration Analysis
- How to Calculate a Better FFT by Leveraging the PSD Method