Signal Abnormality Detection Using Two-Dimensional Time-Frequency Transform
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Solution Overview
Problem
Existing methods for detecting abnormality in cycle variable frequency signals using one-dimensional patterns lose features inherent to the correspondence between time and frequency, leading to inaccurate results due to uncorrected cycle deviations.
Innovation Solution
A signal abnormality detection system and method that involves a signal sensor generating a sample signal, a computing device performing corrections and time-frequency transforms to create a two-dimensional time-frequency signal, and using an abnormality detection model to calculate a reconstructed difference value for determining signal abnormalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a one-dimensional pattern signal is used for abnormality detection, then the detection process is simple, but features inherent to the correspondence between time and frequency are lost
Solution Approach 1:
The patent transforms the one-dimensional signal into a two-dimensional time-frequency signal through time-frequency transform. This dimensional transformation allows the system to preserve both temporal and frequency information simultaneously, resolving the contradiction between simplicity and information retention by adding a frequency dimension while maintaining computational feasibility through established transform methods.
2Productivity
If cycle deviation correction is not performed, then the processing is faster, but misdetermination occurs for signals with large cycle deviation
Solution Approach 1:
The patent performs cycle deviation correction as a preliminary step before the main abnormality detection process. By pre-aligning the signals to remove cycle deviations, the system ensures that subsequent detection operations work with properly synchronized data, thereby preventing misdetermination while maintaining overall processing efficiency through optimized correction algorithms.
3Power
If a one-dimensional signal is used for detection, then the computational load is low, but the detection accuracy is insufficient for variable frequency signals
Solution Approach 1:
The patent introduces a frequency dimension by transforming the one-dimensional signal into a two-dimensional time-frequency signal. This additional dimension enables the detection system to capture variable frequency characteristics that are invisible in the time domain alone, significantly improving detection accuracy for variable frequency signals while managing computational requirements through efficient transform implementations.
Data Source
AI summary
A signal abnormality detection system and a method thereof are provided. The signal abnormality detection system includes a signal sensor and a computing device. The signal sensor generates a sample signal to be tested through sensing. The computing device is signal-connected to the signal sensor to receive the sample signal to be tested, perform a correction on the sample signal to be tested, and perform a time-frequency transform on a one-dimensional signal generated after the correction to generate a two-dimensional time-frequency signal. The computing device reconstructs the two-dimensional time-frequency signal by using an abnormality detection model to calculate a reconstructed difference value. The computing device performs comparison to determine whether the reconstructed difference value is greater than a detection threshold to determine whether the sample signal to be tested is an abnormal sample.