EMI Fingerprint Detection via Sinusoidal Load for Component Verification
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Solution Overview
Problem
Existing techniques for detecting unwanted electronic components like spy chips or counterfeit components in critical assets face challenges due to stationary random noise when the assets are idle, making it difficult to distinguish small characteristic signatures from the noise band.
Innovation Solution
A system generates a sinusoidal load for the target asset, monitors electromagnetic interference (EMI) signals, and compares the generated EMI fingerprint against a reference fingerprint to detect unwanted components, using techniques like Fast Fourier Transform, cross power spectral density, and multivariate state estimation to enhance detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by stationary object
If the critical asset is left idle during training, then power consumption is reduced, but EMI signal detection precision deteriorates due to stationary random noise
Solution Approach 1:
The patent applies dynamics by transitioning the asset from a static idle state to a dynamic operational state during training. The asset is configured to perform operations that generate varying EMI signals over time, transforming the stationary random noise into dynamic signals that can be more effectively distinguished from unwanted component signatures.
Solution Approach 2:
The patent employs periodic action by configuring the asset to perform repetitive operations at different times during training. These periodic operations generate characteristic EMI signal patterns that can be learned by the inferential model, allowing differentiation between normal periodic signals and anomalies caused by unwanted components.
2Measurement precision
If the asset is powered on and operated during training, then EMI signal detection precision improves, but power consumption increases
Solution Approach 1:
The patent applies partial action by selecting and configuring only specific operations from the asset's available operation set during training. Rather than running all possible operations, the system chooses a subset that provides sufficient EMI signal variation for effective training while minimizing unnecessary power consumption.
3Device complexity
If traditional EMI detection methods are used with idle assets, then device complexity is reduced, but detection capability deteriorates due to noise band limitations
Solution Approach 1:
The patent transforms the detection approach from analyzing static idle signals to analyzing dynamic operational signals. By configuring the asset to perform operations during training, the system generates diverse EMI signal patterns that enhance the detectability of unwanted components without significantly increasing system complexity.
Solution Approach 2:
The patent applies parameter changes by varying operational parameters such as operation type, timing, and sequence during training. These parameter variations create diverse EMI signal characteristics that improve the inferential model's ability to detect unwanted components across different operating conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for effective detection of unwanted components by introducing a deterministic dynamic workload, reducing false alarms and improving signal-to-noise ratios, enabling reliable identification of components within the noise band.
Implementation Method 1
the system obtains target electromagnetic interference (EMI) signals by monitoring EMI signals generated by the target asset while the target asset is executing the sinusoidal load
Implementation Method 2
the system performs a target Fast Fourier Transform (FFT) operation on the target EMI signals to transform the target EMI signals from a time-domain representation to a frequency-domain representation
Implementation Method 3
the system computes a cross power spectral density (CPSD) between the sinusoidal load and the target EMI signals
Data Source
AI summary
The disclosed embodiments provide a system that detects unwanted electronic components in a target asset. During operation, the system generates a sinusoidal load for the target asset. Next, the system obtains target electromagnetic interference (EMI) signals by monitoring EMI signals generated by the target asset while the target asset is executing the sinusoidal load. The system then generates a target EMI fingerprint from the target EMI signals. Finally, the system compares the target EMI fingerprint against a reference EMI fingerprint for the target asset to determine whether the target asset contains unwanted electronic components.