The limited fault information caused by small fault data samples is a major problem in wind turbine (WT) fault detection. This paper proposes a small-sample WT fault detection method with the synthetic fault data using generative adversarial nets (GANs). First, based on the prior knowledge, a rough fault data generation process is developed to transform the normal data to the rough fault data. Second, a data refiner is developed by GANs to make the rough fault data more similar with the real fault data. Moreover, to make the generated data better suited to the WT conditions, GANs are improved in both the generative model and the discriminative model. Third, AI-based WT fault detection models can be well trained by sufficient generated data. Finally, three groups of generated data evaluation experiments and four groups of WT fault detection comparative experiments are conducted. The results indicate that the proposed method is effective.

In most current nondestructive testing systems, a magnetic flux leakage (MFL) method is widely used in various industry fields, where the structural integrity of specimens is of vital importance. The estimation of defect size in specimen from the MFL measurements is a key and difficult problem. The traditional methods have low precision because feature extraction procedure relies on prior knowledge and the ability of designer. Inspired by the idea of convolutional neural network (CNN), a novel visual transformation CNN (VT-CNN) is proposed in this paper to overcome the limitation of traditional method in a feature extraction procedure. By adding a visual transformation layer according to the characteristics of the MFL measurements, the VT-CNN can distinguish the defect feature with different sizes more accurately. Moreover, since the VT-CNN method is designed based on the deep learning theory, more industrial big data with accurate label should be used to train the network. Due to the difficulty of making real industrial big data, a novel mesher magnetic dipole model is designed to simulate this industrial process. A large simulated MFL measurement of irregular defects produced by this model can increase the number of training samples and improve the robustness of the network. Experiments to estimate natural corrosion defects on real industrial pipelines are performed to validate the proposed framework. The experimental results are illustrated in detail, which highlights the superiority of the proposed method in industrial applications.

A fuzzy min-max neural network based on data core (DCFMN) is proposed for pattern classification. A new membership function for classifying the neuron of DCFMN is defined in which the noise, the geometric center of the hyperbox, and the data core are considered. Instead of using the contraction process of the FMNN described by Simpson, a kind of overlapped neuron with new membership function based on the data core is proposed and added to neural network to represent the overlapping area of hyperboxes belonging to different classes. Furthermore, some algorithms of online learning and classification are presented according to the structure of DCFMN. DCFMN has strong robustness and high accuracy in classification taking onto account the effect of data core and noise. The performance of DCFMN is checked by some benchmark datasets and compared with some traditional fuzzy neural networks, such as the fuzzy min-max neural network (FMNN), the general FMNN, and the FMNN with compensatory neuron. Finally the pattern classification of a pipeline is evaluated using DCFMN and other classifiers. All the results indicate that the performance of DCFMN is excellent.

The lack of fault samples is a challenging issue for fault diagnosis in the industrial process. It is difficult for conventional fault diagnosis methods to achieve satisfactory results under small samples. This article proposes a general transfer framework with evolutionary capability to address the above issue. First, a general transfer framework is proposed, in which the transfer learning strategy is applied to guarantee the number and diversity of expanded samples and achieve accurate modeling. Second, the adaptive mixup (Admixup) method is presented, which can adaptively expand the fault samples and make the shared distribution smoother to guarantee the stability and accuracy of the fault diagnosis results. Finally, an optimized evolution strategy is designed, in which the transformation matrix is used as an evolutionary channel to reduce the fault diagnosis errors without retraining the framework as fault samples increase. The presented framework can utilize the generalization of small samples and the knowledge of various working condition samples to achieve accurate modeling. The proposed framework is applied to simulated and real industrial processes. Experiment results illustrate that the fault diagnosis model can be effectively established by the proposed framework under small samples, and the proposed framework is evolutionary capable.

In the special equipment systems, welding quality assessment based on X-ray flaw detection is related to the industrial production safety. As a key component in weld quality monitoring, automatic defect detection system aims to locate and identify numerous welding defects (e.g., air holes and cracks). Some current defect detection methods are difficult to accurately detect dense and small defects in low-quality X-ray images. In order to tackle the current challenges, this article proposes an effective weld defect detection method, termed integrated and iterative deep network (I2D-Net). First, the original weld image is decomposed by using frequency domain filtering to make full utilization of high- and low-frequency information and enhance the weak defect features. Second, a parallel detection network based on residual connection blocks of different depth and width is designed, which can flexibly capture defect features from multiple perspectives. Then, an integrated and iterative model prediction method is proposed to better extract the feature representation of small defects, thus effectively improving the overall performance. In the experiment, we built a hardware testing platform and conducted a large number of defect detection experiments and analyses. In addition, we also elaborated on practical application cases to prove that the proposed method has high industrial application value. Finally, the evaluation results of the datasets of weld defects of pressure pipeline and vessel show that the proposed I2D-Net is superior to the state-of-the-art (SoTA) methods in terms of average precision (AP increased by 7.7%, recall increased by 11.5%, and detection rate reached 98.4%).

This paper proposes a quick and accurate method for reconstructing defect profiles in pipelines from magnetic flux leakage (MFL) signals employing a modified harmony search (MHS) algorithm. To improve the quality of the initial harmony memory, a multiple selection opposition-based learning strategy is presented in the MHS algorithm. Moreover, the pitch adjusting rate is set to increase linearly, and the distance bandwidth ( bw ) is designed to be dynamically adjusted to shorten the calculation time. A specially designed modified sum-squared error with weights is proposed and employed as the fitness function to improve the fitting degree between the reference and predicted signals in the defect response segment. The weights are determined by the characteristics of reference signals. We demonstrate the adequacy of the performance of the proposed model using both simulated and real MFL signals. We also present comparative results of the proposed model and other methods. The results prove that the proposed method can achieve higher efficiency while maintaining excellent accuracy.