Acoustic Emission Detector Using Multi-Parameter Space Analysis
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Solution Overview
Problem
Conventional acoustic emission detectors face challenges in accurately detecting warnings of destruction due to low certainty and reliability, often misjudging noise as a warning of destruction by relying solely on amplitude measurements.
Innovation Solution
An acoustic emission detector that uses a sensor to detect acoustic emissions and calculates multiple parameters, employing a correlation parameter determining section to identify strongly correlated parameters, allowing for accurate judgment of destruction by locating points within specific regions of a parameter space, thereby distinguishing between noise and actual destruction signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AE detector judges the warning of destruction simply by the amplitude of AE, then the detection process is simple and fast, but the certainty and reliability are low with frequent misjudgment of noise as destruction warning
Solution Approach 1:
The patent transitions from one-dimensional amplitude-based detection to multi-dimensional parameter space analysis. By calculating multiple parameters (effective value, frequency, energy, kurtosis, etc.) and analyzing their correlations in parameter space, the system achieves more reliable destruction warning detection while filtering out noise that would be misidentified in simple amplitude-based methods.
Solution Approach 2:
The patent changes from using a single parameter (amplitude) to using multiple parameters (effective value, frequency, energy, kurtosis, and their correlations). This parameter expansion allows the system to distinguish between noise and actual destruction warnings by analyzing the relationships between parameters, thereby improving reliability without excessive complexity increase.
2Measurement precision
If multiple parameters are calculated and analyzed in parameter space to improve detection accuracy, then the reliability and accuracy of destruction warning detection are improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent extracts and focuses on the most critical parameters and their correlations from the full set of possible acoustic emission parameters. By identifying key parameters (effective value, frequency, energy, kurtosis) and their interrelationships, the system achieves high detection accuracy while avoiding unnecessary computational overhead from analyzing all possible parameters.
Solution Approach 2:
The patent performs preliminary calculation of multiple parameters and their correlations during normal operation, storing them in parameter space. When a potential destruction warning is detected, the system can quickly query the pre-calculated parameter relationships rather than computing everything in real-time, reducing the time loss during critical detection moments.
3Reliability
If conventional AE detection methods are used, then the device structure is simple, but the ability to distinguish between noise and actual destruction signals is poor leading to false alarms
Solution Approach 1:
The patent adds dimensional depth to the detection system by moving from single-parameter amplitude detection to multi-parameter correlation analysis in parameter space. This dimensional expansion provides the system with the ability to distinguish noise from actual destruction signals by examining relationships between multiple parameters simultaneously, achieving high reliability while managing complexity through focused parameter selection.
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 enables reliable and rapid detection of warnings of destruction, reducing false alarms and improving accuracy by utilizing parameter spaces defined by correlated parameters, allowing for correct judgment even when previously unknown parameters are involved.
Implementation Method 1
an AE (Acoustic Emission) detector that detects a warning of destruction by acoustic emission
Implementation Method 2
an AE sensor and a signal processing unit. The AE sensor detects an AE wave elastically generated
Data Source
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AI summary
An AE detector has an AE sensor (5) and a destruction judging section (18). The destruction judging section (18) judges it as a warning of destruction of a bearing (3) when there are a predetermined number or more of points defined by parameters calculated based on signals from the AE sensor (5) in a predetermined region of a parameter space defined by a plurality of parameters which can be created based on the signals from the AE sensor (5).