Contactless Power Converter Aging Detection with Acoustic Signals
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Solution Overview
Problem
Current methods for detecting power converter aging require intrusive circuits that can cause damage and are sensitive to environmental noise, making them unreliable and impractical for online, in-situ aging detection.
Innovation Solution
A contactless method using acoustic noise measurement from power converter switching events, analyzed by a microphone and machine learning, to predict remaining useful life without inserting detection circuits, allowing for nondestructive and accurate aging assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If intrusive detection circuits are inserted close to power switches for aging detection, then measurement precision is improved, but device complexity and risk of circuit failure increase
Solution Approach 1:
The patent uses acoustic emissions as an intermediary medium to detect power switch aging. Instead of directly measuring electrical parameters through intrusive circuits, the system captures acoustic signals generated by the power switches during operation. These acoustic signals serve as indirect indicators of device aging, eliminating the need for direct electrical measurement circuits close to the switches.
Solution Approach 2:
The patent replaces electrical measurement systems with acoustic measurement systems. Instead of using voltage/current sensors that require direct circuit insertion, the system uses microphones or acoustic sensors to detect sound waves generated by the power switches. This substitution eliminates the need for intrusive electrical circuits while maintaining detection capability.
2Productivity
If intrusive detection circuits are inserted for online aging detection, then productivity is improved, but reliability decreases due to potential short circuits
Solution Approach 1:
The patent employs acoustic emissions as a safe intermediary that does not require direct electrical contact with power switches. The acoustic signals are captured by external microphones and processed to detect aging indicators, enabling online monitoring without risking short circuits or damaging the power converter components.
Solution Approach 2:
By replacing electrical measurement circuits with acoustic sensing, the system achieves online detection capability while eliminating the reliability risks associated with intrusive electrical circuits. The acoustic measurement system cannot cause short circuits or damage to the power converter, ensuring safe operation throughout the device lifetime.
3Measurement precision
If reflectometry-based methods are used for detecting gate impedance variation, then measurement precision is improved, but device complexity increases due to additional circuits
Solution Approach 1:
The patent replaces complex electrical measurement systems with acoustic sensing. Instead of using reflectometry-based circuits to measure gate impedance variations, the system captures acoustic emissions from the power switches and analyzes these signals to detect aging indicators. This substitution dramatically reduces device complexity while maintaining measurement capability.
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
Enables online, nondestructive aging detection of power converters, providing reliable predictions of remaining useful life and reducing the risk of circuit failure by avoiding intrusive methods.
Implementation Method 1
measuring acoustic noise near an operating power converter
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for contactless power converter aging detection. An example method may include receiving, from a microphone, first acoustic data associated with a power converter. The example method may also include converting the first acoustic data into second acoustic data, wherein the first acoustic data is time domain data and the second acoustic data is frequency domain data. The example method may also include determining, by a machine learning model and based on the second acoustic data, a remaining useful life value associated with the power converter.