Common Mistakes When Interpreting FFT Results
Fast Fourier Transform (FFT) is one of the most widely used tools in signal processing for analyzing frequency components of signals. However, many engineers and students misinterpret FFT results, leading to incorrect conclusions about signal behavior.
In this article, we will explore the most common mistakes when interpreting FFT results and explain how to avoid them in practical signal analysis workflows.
1. Misinterpreting Frequency Peaks
One of the most common mistakes is assuming that every visible peak represents a true signal component
Problem
Cause
Aliasing in signal
Harmonics in signal
Noise in signal
Key Insight
Not all peaks are meaningful. Understanding the source of each peak is essential for accurate analysis.
2. Ignoring the Sampling Rate
FFT results are directly dependent on the sampling rate (Fs)
Problem
Cause
Incorrect or misunderstood sampling frequency.
Example
If the sampling frequency is too low (Fs < 2 × Fsignal), aliasing occurs. The aliasing frequency can be determined using the following method. Substitute integer values of n starting from 1, and the smallest resulting frequency is the aliasing frequency.
Normal Case
- Time-domain signal: Original signal without sampling issues
- FFT result: A clear peak appears at the true frequency
When an appropriate sampling frequency is used, the correct frequency components can be accurately identified in the FFT spectrum.
Normal Case: origianl time-domain signal and FFT result before aliasing occurs (f < Fs/2)
(refer to Samples/aliasing.mmj)
Abnormal Case (Aliasing occurs in signal)
- Time-domain signal: Distorted signal due to low sampling frequency
- FFT result: The peak appears at an incorrect frequency, shifted from its true position
Abnormal Case: distorted time-domain signal and FFT result caused by undersampling (f > Fs/2)
(refer to Samples/aliasing.mmj)
3. Poor Frequency Resolution
FFT resolution depends on signal length
Problem
Cause
Frequency Resolution (Δf ) = Fs / N ≈ 1 / Trecord
Short signals (small N) make resolution bad, which means the value of Δf is large.
Solution
Increase signal length (time record) → improves the actual frequency resolution
Use zero padding → does not increase frequency resolution; it only provides a finer sampling of the spectrum (i.e., interpolation)
4. Spectral Leakage Due to No Windowing
FFT assumes the signal is perfectly periodic within the observation window.
Problem
Cause
Signal discontinuity at boundaries.
Solution
Spectral leakage observed when no window function is applied (refer to Samples/windowing effect.mmj)
5. Incorrect Amplitude Interpretation
Many users misinterpret FFT amplitude values.
Problem
Cause
Missing normalization (scaling)
Confusion between one-sided and two-sided spectrum
Some spectrum uses RMS FFT instead of amplitude FFT
| Category | Amplitude FFT | RMS FFT |
|---|
| Basis | Peak | RMS |
| Peak value | Value A | A / √2 |
| Physical meaning | Amplitude | Energy |
| Common use | Magnitude estimation in signal | Power analysis (Power, PSD, ...) |
| x(t) = sin(2πft) | Peak value = 1 | Peak value = 1 / √2 = 0.707 |
Key Insight
FFT output must be properly scaled to reflect true signal amplitude.
6. Noise Dominating the Spectrum
Real-world signals often contain noise.
Problem
Cause
Solution
Example
Envelope FFT looks messy due to noise
Envelope FFT after band-pass filtering, showing improved peak clarity (refer to Samples/envelope_bandpass.mmj)
In such cases, appropriate filtering and signal processing techniques enable clearer separation of the underlying signal components.
7. DC Offset / Missing of Mean Removal
Problem
- Strong DC component appears
- Spectrum dominated by low-frequency energy
Cause
- Signal not mean-centered
- Presence of DC offset
Solution
- Apply mean centering before FFT
- Remove DC offset from the signal
- Use detrending if the signal contains slow drift
- Optionally, apply a high-pass filter to remove low-frequency components
- Then apply the correct FFT amplitude scaling
FFT results: before(offset) and after detrending(offset removal)
(refer to Samples/detrend and HPF.mmj)
8. Confusing FFT with Time-Varying Analysis
FFT provides a meaningful frequency representation only if the signal is approximately stationary within the analysis window.
Problem
Solution
FFT provides no time resolution, while STFT can track time-varying frequency components
(refer to Samples/chirp spectrogram.mmj)
Real Example Using MALMIJAL
In practical workflows, many of these issues occur simultaneously.
Using MALMIJAL, these problems can be identified and corrected visually:
Noisy time-domain signal before applying filtering (refer to Samples/whistle filtering.mmj)
Frequency analysis showing low-frequency background noise before filtering
Time-domain waveform after band-pass filtering, showing frequencies around the whistle sound (1,745 Hz)
FFT spectrum after band-pass filtering, showing the isolated whistle frequency with background noise removed
Such a visual workflow reduces FFT interpretation errors and improves the accuracy of signal analysis.
Why These Mistakes Matter
Incorrect FFT interpretation can lead to
Wrong vibration diagnosis
Incorrect frequency detection
Poor engineering decisions
Understanding these pitfalls is essential for reliable signal analysis.
Conclusion
FFT is a powerful tool, but incorrect interpretation can easily lead to misleading results.
By understanding common mistakes such as aliasing, poor resolution, spectral leakage, and noise interference, you can significantly improve your analysis accuracy.
Tools like MALMIJAL simplify this process by providing a visual, drag-and-drop workflow that allows engineers to quickly identify and fix these issues without complex coding. Furthermore, you can model the same workflow in modeling mode.
Suggested Further Reading
#You may also find these topics helpful:
Common Mistakes When Interpreting FFT Results
Fast Fourier Transform (FFT) is one of the most widely used tools in signal processing for analyzing frequency components of signals. However, many engineers and students misinterpret FFT results, leading to incorrect conclusions about signal behavior.
In this article, we will explore the most common mistakes when interpreting FFT results and explain how to avoid them in practical signal analysis workflows.
1. Misinterpreting Frequency Peaks
One of the most common mistakes is assuming that every visible peak represents a true signal component
Problem
Peaks may appear at unexpected frequencies
Multiple peaks may exist even for a simple signal
Cause
Aliasing in signal
Harmonics in signal
Noise in signal
Key Insight
Not all peaks are meaningful. Understanding the source of each peak is essential for accurate analysis.
2. Ignoring the Sampling Rate
FFT results are directly dependent on the sampling rate (Fs)
Problem
Frequency(Hz) appears incorrect
Peaks are shifted
Cause
Incorrect or misunderstood sampling frequency.
Example
If the sampling frequency is too low (Fs < 2 × Fsignal), aliasing occurs. The aliasing frequency can be determined using the following method. Substitute integer values of n starting from 1, and the smallest resulting frequency is the aliasing frequency.
Normal Case
When an appropriate sampling frequency is used, the correct frequency components can be accurately identified in the FFT spectrum.
Normal Case: origianl time-domain signal and FFT result before aliasing occurs (f < Fs/2)
(refer to Samples/aliasing.mmj)
Abnormal Case (Aliasing occurs in signal)
Abnormal Case: distorted time-domain signal and FFT result caused by undersampling (f > Fs/2)
(refer to Samples/aliasing.mmj)
3. Poor Frequency Resolution
FFT resolution depends on signal length
Problem
Spectral peaks look wide or unclear
Close frequencies cannot be distinguished
Cause
Short signals (small N) make resolution bad, which means the value of Δf is large.
Solution
Increase signal length (time record) → improves the actual frequency resolution
Use zero padding → does not increase frequency resolution; it only provides a finer sampling of the spectrum (i.e., interpolation)
4. Spectral Leakage Due to No Windowing
FFT assumes the signal is perfectly periodic within the observation window.
Problem
Energy spreads across frequencies
Spectral peaks look smeared
Cause
Signal discontinuity at boundaries.
Solution
Apply window functions (Hann, Hamming, Blackman, and etc.)
Spectral leakage observed when no window function is applied (refer to Samples/windowing effect.mmj)
Compare FFT before and after applying window
Show reduced leakage
5. Incorrect Amplitude Interpretation
Many users misinterpret FFT amplitude values.
Problem
Amplitude appears too small or too large
Results differ from expected signal magnitude
Cause
Missing normalization (scaling)
Confusion between one-sided and two-sided spectrum
Some spectrum uses RMS FFT instead of amplitude FFT
Key Insight
FFT output must be properly scaled to reflect true signal amplitude.
6. Noise Dominating the Spectrum
Real-world signals often contain noise.
Problem
FFT looks messy
True signal peak is hard to identify
Cause
Broadband noise
Low SNR (Signal-to-Noise Ratio)
Solution
Apply band-pass filtering
Use averaging techniques
Example
Envelope FFT looks messy due to noise
Envelope FFT after band-pass filtering, showing improved peak clarity (refer to Samples/envelope_bandpass.mmj)
In such cases, appropriate filtering and signal processing techniques enable clearer separation of the underlying signal components.
7. DC Offset / Missing of Mean Removal
Problem
Cause
Solution
(refer to Samples/detrend and HPF.mmj)
8. Confusing FFT with Time-Varying Analysis
FFT provides a meaningful frequency representation only if the signal is approximately stationary within the analysis window.
Problem
No time information after FFT
Cannot track frequency changes over time
Solution
Use STFT (Short-Time Fourier Transform)
Use spectrogram analysis
FFT provides no time resolution, while STFT can track time-varying frequency components
(refer to Samples/chirp spectrogram.mmj)
Real Example Using MALMIJAL
In practical workflows, many of these issues occur simultaneously.
Using MALMIJAL, these problems can be identified and corrected visually:
Adjust sampling rate
Apply window functions (a window is not required when the goal is simply peak detection)
Filtering of noise
Inspect exact FFT values using data cursor
Noisy time-domain signal before applying filtering (refer to Samples/whistle filtering.mmj)
Frequency analysis showing low-frequency background noise before filtering
Time-domain waveform after band-pass filtering, showing frequencies around the whistle sound (1,745 Hz)
FFT spectrum after band-pass filtering, showing the isolated whistle frequency with background noise removed
Such a visual workflow reduces FFT interpretation errors and improves the accuracy of signal analysis.
Why These Mistakes Matter
Incorrect FFT interpretation can lead to
Wrong vibration diagnosis
Incorrect frequency detection
Poor engineering decisions
Understanding these pitfalls is essential for reliable signal analysis.
Conclusion
FFT is a powerful tool, but incorrect interpretation can easily lead to misleading results.
By understanding common mistakes such as aliasing, poor resolution, spectral leakage, and noise interference, you can significantly improve your analysis accuracy.
Tools like MALMIJAL simplify this process by providing a visual, drag-and-drop workflow that allows engineers to quickly identify and fix these issues without complex coding. Furthermore, you can model the same workflow in modeling mode.
Suggested Further Reading
#You may also find these topics helpful: