Metal Defect Detection with Hammerstein Nonlinear System Identification
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Solution Overview
Problem
Existing technologies struggle to effectively combine system identification with non-destructive detecting for detecting defects in metal materials, particularly due to the nonlinear effects caused by defects, which are not adequately addressed by linear models.
Innovation Solution
A defect detection method and device utilizing nonlinear system identification, involving hammer excitation, laser vibrometry, and a Hammerstein model optimization process to identify defects in metal materials without causing damage, using a modal force hammer, laser vibrometer, and cross-validation to refine the model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If linear models are used for system identification in non-destructive detecting, then the detection process is simple and mature, but the nonlinear effects caused by defects cannot be adequately addressed
Solution Approach 1:
The patent transforms the linear system identification parameters into nonlinear parameters by introducing a Hammerstein model structure. This model separates the system into a nonlinear static component followed by a linear dynamic component, allowing the detection of nonlinear defect effects while maintaining the mathematical tractability of linear models. The parameter transformation enables capturing defect-induced nonlinearities without abandoning the simplicity of linear identification methods.
Solution Approach 2:
The patent combines linear and nonlinear modeling approaches into a composite Hammerstein model structure. This composite model integrates the simplicity of linear systems with the defect-sensitivity of nonlinear systems, creating a hybrid identification framework that leverages the strengths of both approaches for enhanced defect detection capability.
2Measurement precision
If nonlinear system identification is used to detect defects in metal materials, then the detection accuracy for nonlinear defect effects is improved, but the complexity of the detection system increases
Solution Approach 1:
The patent segments the complex nonlinear system identification problem into two distinct components: a nonlinear static gain component and a linear dynamic component. This segmentation allows each component to be identified and analyzed separately, reducing the overall complexity. The nonlinear static part captures defect effects while the linear dynamic part represents the structural response, enabling simplified identification procedures.
Solution Approach 2:
The Hammerstein model structure serves as an intermediary framework that bridges simple linear identification methods and complex nonlinear analysis. By introducing this intermediate model structure, the patent enables the use of relatively simple identification algorithms while still capturing essential nonlinear defect characteristics, thus reducing the complexity burden.
3Object-affected harmful factors
If hammer excitation and laser vibrometry are used for non-destructive detecting, then the objects to be detected are not damaged, but the detection process requires sophisticated equipment and procedures
Solution Approach 1:
The patent employs periodic hammer excitation to induce vibrations in the test specimen. This periodic excitation method is non-destructive as it uses controlled mechanical impacts within safe amplitude limits. The periodic nature of the excitation allows for systematic data collection and facilitates the identification process by providing consistent input signals for analyzing structural response.
Solution Approach 2:
The patent replaces direct mechanical contact measurement with laser vibrometry, which uses optical fields instead of mechanical sensors. This substitution eliminates the need for physical contact with the specimen, ensuring non-destructive testing while capturing vibration responses. The optical measurement system avoids mechanical loading that could potentially damage the specimen.
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 non-destructive detection of defects in metal materials by establishing a nonlinear system model, optimizing the Hammerstein model to accurately identify defects and avoid damage during the detection process.
Implementation Method 1
performing, by a modal force hammer, hammer excitation on the test specimen according to the test schedule to generate an excitation signal; and making the test specimen vibrate based on the excitation signal to generate a response signal
Implementation Method 2
acquiring, by a laser vibrometer, the excitation signal and the response signal
Data Source
AI summary
A defect detection method and device based on nonlinear system identification are provided. The defect detection method includes: performing, by a modal force hammer, hammer excitation on a test specimen according to a test schedule to generate an excitation signal and a response signal; acquiring, by a laser vibrometer, the excitation signal and the response signal; performing parameter calculation on the excitation signal and the response signal to obtain model parameters; constructing an initial Hammerstein model based on the model parameters; optimizing the initial Hammerstein model to obtain a Hammerstein model of the test specimen; adjusting the Hammerstein model of the test specimen by using a cross-validation method; perform defect determination based on the Hammerstein model of the test specimen adjusted and a pre-constructed template specimen model to obtain a determination result, and determining whether the test specimen is defective based on the determination result.