Magnetic flux leakage (MFL) testing is an effective non-destructive testing (NDT) method for pipeline safety operation, and defect detection is one of the core issues in MFL signal processing. Currently, MFL defect detection is tough due to insufficient defect samples. To obtain more serviceable information, heterogeneous signals are collected in MFL process, while taking full advantage of heterogeneous signals is still a hard problem. In this paper, an end-to-end MFL defect detection architecture with insufficient training samples called heterogeneous signal fusion method (IHSF) is proposed. Firstly, heterogeneous signals from the same pipeline are collected by axial and radial sensors. Secondly, the features from fine-tuned and non-fine-tuned pre-trained models are fused, which increases the generality and adaptability of the features. Moreover, fine-tuning of a few parameters reduces the number of parameters during model training, which is more suitable for the insufficient sample training. Thirdly, a mutual supervision training strategy based on the features fusion of general and adaptive features is proposed to update the parameters of the proposed network, which establishes the latent relationship of heterogeneous signals. Finally, experiments on MFL defect detection are conducted, and IHSF is compared to the state-of-the-art methods. The results validate that the proposed method is effective.Note to Practitioners—The motivation of this paper is a hot signal processing issue on pipeline safety operation called defect detection with insufficient samples. Traditionally, the defect is usually detected by training a large number of defect samples, while defect samples are usually insufficient in practical MFL measurement. Moreover, conventional deep networks only use a single signal, which ignores information from multiple signals. Regarding the problems above, a heterogeneous signal mutual supervision method is proposed to replace the traditional fixed label mechanism. The mutual supervision method fully extracts the information in multiple signals by establishing hidden connections between defective heterogeneous signals. Furthermore, the fusion of the general features and adaptive features, which are extracted by the pre-trained model with non-fine-tuning and fine-tuning respectively, not only reduces the parameters in the network to be suitable for the insufficient sample problem, but also enriches the extracted features. The case on MFL signals demonstrates that the proposed method is more effective than the comparison methods.

In industrial processes, the defect data is continuously accumulated over time, and new classes of defects can arise at any time. When a well-trained model is adapted to new classes, its performance in old classes will sharply decline. To solve the above issue, a novel incremental defect detection method named elastic heterogeneous distillation network (EHD-Net) is proposed. First, an elastic knowledge transfer method is proposed to selectively transfer the core features of the old classes, so that the old knowledge is retained and new knowledge is effectively learned at the feature level. Second, warping heterogeneous distillation learning is proposed for the first time. In warping heterogeneous distillation learning, a gradient warping layer is proposed to balance the optimization direction of gradients for both old and new classes, and the proposed heterogeneous distillation learning strategy can clarify the association and difference between new and old classes, so that the rapid learning of new knowledge and comprehensive transfer of old knowledge are guaranteed at the decision level. Finally, a well-trained evolutionary model is employed to achieve the detection of both old and new classes. The proposed method can effectively overcome the catastrophic forgetting of old classes and guarantee the independent learning of new classes. Experimental results on two industrial datasets show that EHD-Net outperforms existing advanced methods. Note to Practitioners—The motivation for this paper is an important and highly practical issue called class-incremental defect detection. Specifically, new classes of defects always appear in industrial scenarios over time and when a well-trained detection model adapts to the new classes, its performance in the old classes decreases dramatically. To solve the above issues, an elastic heterogeneous distillation network (EHD-Net) via elastic knowledge transfer and warping heterogeneous distillation learning is proposed. The proposed method not only effectively suppresses the catastrophic forgetting of old classes while realizing the fast learning of new classes, so that the model can simultaneously achieve the high-accuracy detection of both the new and old classes without the participation of old class samples. The validity of the proposed method is verified under two industrial datasets, which fully guarantees its practical application value.

This article studies the modeling method of false data injection attacks (FDIAs) considering topological changes and relevant countermeasures. A novel robust FDIA model is built, which incorporates network and measurement uncertainties into the attack subnet and can be applied to attack scenarios during topological changes. To tackle such FDIAs, a flexible dynamic time warping strategy-based distributed unsupervised detection mechanism is developed. Furthermore, an enhanced recognition model via hierarchical agglomerative clustering and local outlier factor techniques is proposed to facilitate operators to distinguish FDIAs. Compared with related works, the proposed model is more applicable to topological change scenarios and the detection framework can effectively discern such FDIAs in a distributed fashion. Simulation results demonstrate the stealthiness of the proposed FDIA model during and related to topological changes and the effectiveness of the distributed unsupervised detection method in tackling such attacks.

Magnetic flux leakage (MFL) testing and analysis is an effective non-destructive testing (NDT) method of pipeline health. The health status of pipelines is evaluated by analyzing MFL signals, especially the defect analysis. However, complex operating conditions and equipment deviations may cause deformation on the defects. Availability and integrity of the MFL defects are the keys to accurate signal processing. Due to the limited interpretability of abstract networks and insufficient feature extraction, it is hard to accurately recover the deformed defects. Considering the problems above, an MFL deformed defect recovery method with the guidance of physical features is proposed. The proposed method integrates a deep feature extraction model into a sparse autoencoder, whose parameters are guided by the combination of MSE loss and physics loss. First, the deep feature extraction model fully extracts the abstract features, which contains more information on deformed defects. Second, the sparse penalty factor in the sparse autoencoder reduces the feature redundancy of deformed defects. Third, the physical features are extracted to guide the MFL deformed defect recovery relying on the MFL mechanism for the first time, which enhances the robustness and accuracy of the network. Finally, several comparison experiments have been conducted on measured and simulated MFL defects. The results show that the proposed method is effective in deformed defect recovery.Note to Practitioners—The motivation of this paper is a practical problem of MFL deformed defect recovery. The deformation on MFL defects affects defect inversion and evaluation seriously, which causes great potential harm to pipeline safety transmission. In this paper, compared with traditional neural network-based methods, physical features based on MFL mechanism are extracted to guide the parameter update. In addition, a pre-trained model replaces regular convolutional layers to extract features sufficiently, while a sparse factor is applied to reduce redundant features. The proposed method not only utilizes deep features from deep networks, but also increases the guidance of physical features, so that the algorithm is more robust and accurate. The experiment results show that our proposed method is more effective than the comparative methods on MFL simulated and measured defects. In a word, the proposed method has strong theoretical research and practical value.

Pipeline magnetic flux leakage (MFL) detection is an efficient and energy-saving nondestructive testing (NDT) method. However, under the high-speed detector, MFL signals become distorted with the influence of velocity effects, which adversely affects the accuracy of defect size inversion. The essential cause is the distorted signal multiplicity by velocity effects. In response to this issue, a high-precision defect size inversion method is proposed for the first time, which is called knowledge-guided contrastive fusion network (KCF-Net). First, MFL and eddy current (EC) mechanisms are analyzed, which are concluded that the sensitivity of EC signals to speed is much lower than that to defect sizes, so that EC and MFL abstract features are mined to improve the sensitivity of defect sizes. Moreover, MFL mechanism representations are mined to supervise neural networks to enhance the interpretability of the network. MFL and EC knowledge including abstract features and mechanism representations is fused to highlight the disparities between undistorted and distorted signals and enrich available information. Then, joint decision-making is proposed to eliminate the instability of fusion knowledge and enhance the universality and effectiveness of defect inversion. Finally, the experiments prove the effectiveness of KCF-Net. The length, width, and depth inversion MAEs of measured signals reach 2.2676, 1.6185, and 0.5664, respectively.

Deep learning methods have demonstrated promising performance in magnetic flux leakage (MFL) defect detection under adequate amounts of labeled samples. However, in industrial occasions, obtaining adequate amounts of labeled samples is time-consuming and expensive, and applying only limited labeled samples can lead to unsatisfactory defect detection accuracy. To address the above issues, a defect detection method named semisupervised circular teacher network (SSCT-Net) is proposed in this article. First, a parallel feature extraction network with hybrid attention is proposed in SSCT-Net so that the useful features of multiview MFL signals can be extracted simultaneously. Second, semisupervised circular learning is proposed for the first time. In semisupervised circular learning, a distinguishable feature embedding space is constructed, and two structurally identical deep networks cosupervise and collaborate through the proposed consistent circular strategy so that the decision bias of unlabeled samples can be reduced. Finally, the trained model is applied for defect detection in practice. The proposed method can establish a potential connection between multiview MFL signals and fully utilize labeled and unlabeled MFL signals. The experiments in simulations and real-world applications demonstrate that SSCT-Net can reach 92% detection accuracy with only 20% labeled samples, which is more effective than the state-of-the-art methods and leads to a promising practical utility of the proposed method.

To improve the validity of magnetic flux leakage (MFL) multisensor signals, anomaly detection has become a significant part of MFL signal processing. The anomalies in MFL are uncertain and have no prior information or labels. Therefore, the detection and location of the anomalies become a difficult issue. Regarding the abovementioned problem, we propose an unsupervised method called multisensor cycle-supervised convolutional neural network (CsCNN). The CsCNN is built including multiple CNNs with the same structure and a cycle-supervised part. The proposed model realizes unsupervised anomaly detection through multiple cycle-supervised CNNs for the first time. Moreover, the latent relationship between multisensor signals is established by CsCNN to take full use of multisensor information. Besides, a dynamic threshold is applied to detect anomalies. In the end, experiments on simulated signals and measured signals are conducted, and CsCNN is compared to the state-of-the-art methods. The results show that the proposed method is effective.

Safe transmission of oil pipelines is one of the guarantees of national defense and environmental protection. Magnetic flux leakage (MFL) testing is critical to the safety inspection of in-service pipelines. In the detection process, the incompleteness of MFL data affects defect location and inversion severely. This article proposes an MFL data recovery method based on multifeature condition risk, which can effectively handle the block data gap problem. First, a preprocessing mechanism is proposed to determine defect boundaries and uniformly interpolate the raw data automatically. Second, a multifeature extraction method of MFL defect data is proposed, which takes full advantage of the complete data and reduces the impact of data gaps. Third, a novel data reconstruction method based on feature conditional risk is proposed, where prior information of MFL data is regressed, relying on the regression coefficient calculated by dynamic programming. Finally, comparison experiments on different sizes of data gaps, varying robustness, and average running time are conducted, respectively. The MFL data are derived from actual measurements. The results indicate that the proposed method is more robust, more efficient, and faster.Note to Practitioners—This article is motivated by the problem of data gaps in magnetic flux leakage (MFL) data in actual pipeline safety transmission measurements, which seriously affects defect inversion and assessment. In this article, in terms of the intrinsic characteristics for MFL data, we propose a new and effective approach by extracting the multifeature of defects and using conditional risk to recovery data gaps. Our method takes advantage of the no-missing part of MFL data and prior samples, which are obtained from practical measurements. The experiment results show that our proposed method is more advantageous than the comparison methods, and therefore, our proposed method has strong practical value. Our future work will aim at improving the versatility of the proposed method.