Aliased Frequency Detection in Video Vibration Analysis
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Solution Overview
Problem
Conventional methods for analyzing vibrations in video recordings of machinery and structures fail to accurately detect aliased frequencies, leading to incorrect analysis and conclusions due to the absence of anti-aliasing protection in camera systems, making it laborious to determine true frequency values.
Innovation Solution
A method involving the collection of video recordings at two different sampling rates and the application of an iterative algorithm to identify and correct aliased frequencies, allowing for the determination of true frequency values in the frequency spectrum, particularly for frequencies higher than the Nyquist sampling rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video recordings are collected at a single sampling rate, then data collection is simple and fast, but aliased frequencies cannot be detected and true frequency values cannot be determined
Solution Approach 1:
The system collects video recordings at multiple different sampling rates (e.g., 25 fps and 30 fps) to enable frequency detection. By changing the sampling rate parameter, the system can identify aliased frequencies and determine true frequency values that would be impossible to obtain at a single sampling rate.
Solution Approach 2:
The system performs preliminary data collection at multiple sampling rates before final analysis. This preliminary action of collecting data at different rates enables subsequent identification of aliased frequencies and calculation of true frequency values through comparison and iterative algorithms.
2Measurement precision
If automated alias detection is implemented, then analysis accuracy improves, but processing time and computational complexity increase
Solution Approach 1:
The system uses iterative algorithms that compare frequency spectra from different sampling rates to identify and correct aliased frequencies. This feedback loop continuously refines frequency measurements until accurate true frequency values are determined, automating the detection process while maintaining high precision.
Solution Approach 2:
The system creates copies of the video data at different sampling rates and compares these copies to identify aliasing. By working with multiple versions of the same data, the system can automatically detect and correct frequency measurements without requiring manual intervention.
3Reliability
If anti-aliasing filters are applied before digitization, then aliasing is prevented, but frequency information above Nyquist rate is lost
Solution Approach 1:
Instead of applying filters before digitization that would remove high frequency information, the system performs preliminary data collection at multiple sampling rates. This allows the system to later identify and correct aliased frequencies through comparison, preserving high frequency information while preventing aliasing errors.
Solution Approach 2:
The system creates multiple copies of the signal at different sampling rates without applying anti-aliasing filters. By comparing these copies, the system can identify which frequencies are aliased and determine true frequency values, preserving information that would otherwise be lost or distorted.
Data Source
AI summary
Present embodiments pertain to systems, apparatuses, and methods for analyzing and reporting the movements in mechanical structures, inanimate physical structures, machinery, and machine components, including automatically detecting aliased frequencies of a component on the structure which exhibits frequencies higher than the maximum frequency of the FFT spectrum calculated from the acquired data. To automatically detect the presence of aliased frequencies, a second virtually identical recording is acquired using a slightly different sampling rate and this provides the basis for detecting frequencies which are greater than the Nyquist sampling rate of the video recording and calculating the true frequency value of the aliased peaks in the frequency spectrum.