A Pinning Actor-Critic Structure-Based Algorithm for Sizing Complex-Shaped Depth Profiles in MFL Inspection with High Degree of Freedom

Zhenning Wu,Lixing Wang,Yiming Deng,Rui Wang

doi:10.1155/2021/9995033

Zhenning Wu, Lixing Wang + Show 2 more

Open Access

https://doi.org/10.1155/2021/9995033

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Journal: Complexity	Publication Date: Apr 23, 2021
Citations: 1	License type: CC BY 4.0

Affiliation: Northeastern University, Michigan State University

Abstract

One of the most efficient nondestructive methods for pipeline in-line inspection is magnetic flux leakage (MFL) inspection. Estimating the size of the defect from MFL signal is one of the key problems of MFL inspection. As the inspection signal is usually contaminated by noise, sizing the defect is an ill-posed inverse problem, especially when sizing the depth as a complex shape. An actor-critic structure-based algorithm is proposed in this paper for sizing complex depth profiles. By learning with more information from the depth profile without knowing the corresponding MFL signal, the algorithm proposed saves computational costs and is robust. A pinning strategy is embedded in the reconstruction process, which highly reduces the dimension of action space. The pinning actor-critic structure (PACS) helps to make the reward for critic network more efficient when reconstructing the depth profiles with high degrees of freedom. A nonlinear FEM model is used to test the effectiveness of algorithm proposed under 20 dB noise. The results show that the algorithm reconstructs the depth profile of defects with good accuracy and is robust against noise.

Highlights

Magnetic flux leakage (MFL) is one of the most widely used NDT techniques, which has been widely used for inspection of oil and gas pipeline since the 1960s
Estimating the shape of defect is the key problem of inspection. ough MFL is efficient in finding the defects and anomalies, the reconstruction process from inspection signal to defect depth is not an easy task, as it is usually contaminated by sampling noise [4]
An actor-critic structure-based RL method for complex depth profile reconstruction is proposed. e algorithm of Deep Deterministic Policy Gradients (DDPG) is adopted to train the actor-critic structure [35,36,37]. e definitions of parameters for the problem of sizing the depth profile of MFL inspection are described as follows. e state is defined as st, which consists of two parts, the normalized reconstructed depth profile and the reference signal. dt is the normalized reconstructed depth at time-step t. sref is the sampled reference signal

Summary

Introduction

Magnetic flux leakage (MFL) is one of the most widely used NDT techniques, which has been widely used for inspection of oil and gas pipeline since the 1960s. Non-model-based methods solve this inverse problem by building a mapping between sampled signal and the shape of defect. Ese methods usually have some limitations to assume the shape of the defect a priori Another kind of solution uses a mapping which is trained to replace the numerical forward model. For the model-based method, the iteration strategy is designed based on the forward model in use and highly relies on it For numerical model, it has high performance in simulating the inspection signal, but it is hard to build an iteration strategy based on it. Ough the RL algorithm is basically a machine learning technique which needs training, the general structure has similarity with the classic iteration method which makes it possible to design an iteration strategy for numerical forward physics model.

Model and Principle

Algorithm

Results and Discussion

Conclusion