Pipeline Defect Inversion Through Heterogeneous Signal Normalization
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Solution Overview
Problem
Existing defect inversion methods for pipelines rely heavily on artificial experience and suffer from poor universality and low accuracy due to variations in heterogeneous field signals, which are influenced by pipeline conditions and signal characteristics.
Innovation Solution
An intelligent inversion method that includes real-time abnormality judgment, base value correction, wavelet denoising, signal padding and transformation, sparse autoencoder, and multi-classification neural networks to adaptively extract and classify defect characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional defect inversion methods are used with heterogeneous field signals, then the inversion process can be performed, but the accuracy is low due to signal variations caused by pipeline conditions
Solution Approach 1:
The patent transforms heterogeneous field signals into unified defect characteristics by changing the parameter representation from raw signal values to extracted geometric features (length, width, depth). This parameter transformation enables consistent defect inversion across different pipeline conditions without being affected by signal variations.
Solution Approach 2:
The patent replaces traditional manual parameter design and algorithm updating with an intelligent system that automatically extracts defect characteristics. This substitution eliminates the need for artificial experience in parameter selection and achieves consistent inversion accuracy across different heterogeneous signals.
2Ease of manufacture
If artificial parameter design is used for characteristic extraction, then the extraction process can be performed, but the technology has poor universality due to reliance on artificial experience
Solution Approach 1:
The patent implements self-service by enabling the system to automatically extract defect characteristics from heterogeneous signals without requiring manual parameter design. The intelligent algorithm autonomously identifies and extracts relevant features, eliminating dependence on artificial experience while maintaining extraction effectiveness.
Solution Approach 2:
The patent achieves universality by designing a characteristic extraction method that works across different types of heterogeneous field signals (magnetic flux leakage, eddy current, ultrasonic). The same extraction framework adapts to various signal types and pipeline conditions, making the technology broadly applicable without reconfiguration.
3Measurement precision
If inversion algorithm models are updated for different heterogeneous field signals, then accuracy can be maintained, but the process becomes complex and requires artificial intervention
Solution Approach 1:
The patent extracts only the essential defect characteristics (geometric parameters) from the complex heterogeneous signals, separating the critical information from the noisy variations. This extraction approach simplifies the inversion process by focusing on invariant geometric features rather than processing entire signal waveforms.
Solution Approach 2:
The patent changes the parameter space from raw signal domains to defect geometric parameter domains. This transformation simplifies the inversion algorithm by working directly with meaningful defect characteristics rather than manipulating complex signal parameters, reducing algorithmic complexity while maintaining accuracy.
Data Source
AI summary
Provided is an intelligent inversion method for pipeline defects based on heterogeneous field signals. The method includes the following steps: firstly, acquiring heterogeneous field signals, performing an abnormality judgement, then correcting base values of the heterogeneous field signals, and performing denoising treatment; padding the denoised heterogeneous field signals corresponding to the pipeline defects, unifying the heterogeneous field signals of different sizes into the heterogeneous field signals of same sizes, and performing a nonlinear transformation on signal amplitudes; designing a sparse autoencoder with an axisymmetric structure, and obtaining primary characteristics of the heterogeneous field signals; classifying the pipeline defects according to lengths, widths and depths to obtain category labels of the pipeline defects; designing a multi-classification neural network to classify the heterogeneous field signals, and extracting deep characteristics containing defect size information; and constructing a random forest regression model to realize intelligent inversion for sizes of the pipeline defects.