In harsh and complex aerospace environments, stress concentration causes fatigue cracks to easily form at the edges of rivet holes in an aircraft fuselage, threatening overall aircraft safety. Although testing methods based on high-order focusing ultrasonic guided waves are sensitive to cracks, there remains a lack of high-precision quantification methods. Existing signal processing and machine learning methods have shortcomings. For example, the former cannot achieve intelligent detection and the latter has black box problems with inexplicability. To solve the above-mentioned problems, we propose a space-time cross-fusion bin-to-bin distance-theory-hybrid deep neural network (DNN) called SpatNet that combines the physical knowledge of defect quantification with a DNN to effectively detect and quantify rivet hole cracks in an aluminum alloy fuselage. In addition, we analyze the attenuation coefficientskR,G,Band window length$w_{l}$, which can describe the degree of physical knowledge introduction, and compare the proposed SpatNet with other classical methods. The results show that the reasonable introduction of physical knowledge can effectively improve the prediction accuracy of a DNN and reduce network dependence on large-scale datasets. Related research on SpatNet based on the fusion of physical knowledge and neural network space-time cross-fusion has the potential to promote the further development of the intelligent ultrasonic testing field.

Defect depth is an essential indicator in magnetic flux leakage (MFL) detection and estimation. The quantification errors for defect depth are closely related to length and width errors, and this feature has always been used to support the operator's judgment in defect identification. However, the existing defect quantification algorithms based on shallow and deep neural networks only employed simple general network structures inspired by the field of artificial intelligence; consequently, these network structures lack the support of physical concepts and result in large quantification errors regarding defect size, especially depth. In this article, to describe and integrate the above theory into a deep neural network, we propose a physics-informed doubly fed cross-residual network (DfedResNet) suitable for MFL defect detection based on deep learning. Physics-based MFL defect quantification theory is studied and integrated into loss functions during the neural network training. DfedResNet quantifies defects in MFL data and automatically extracts deep features of defects. The experimental results show that it effectively achieves high-precision quantification of defect length, width, and depth simultaneously, especially defect depth. Moreover, it considers data from all three dimensions during network training, and use the originally measured magnetic signal data in place of recognized images to avoid defect information loss and further improve the quantification accuracy. The deep DfedResNet model proposed in this article reduces defect length and width quantification errors to within 0.3 mm and defect depth quantification errors to within 0.4%t. In addition, compared with other network structures and traditional algorithms, DfedResNet improves defect quantification accuracy by 1–2 orders of magnitude and thus achieves a high quantification performance.

It is challenging to apply deep learning in professional fields that lack big data support, especially in industrial structure health assessments using ultrasonic guided wave nondestructive testing (NDT) method. To solve this problem, one feasible solution is to introduce the concept of NDT physics into a deep neural network to compensate for the network's poor predictive abilities when trained on small datasets. Therefore, we propose a physics-informed deep neural network, named GuwNet, based on a unidirectional oblique-focusing (UOF) high-frequency, high-order shear horizontal guided wave electromagnetic acoustic transducer (EMAT) to quantify microcrack defects more accurately. First, the designed focused-transmission omnidirectional-reception UOF-EMAT can produce pure high-frequency, high-order guided waves. Through a circumferential arrangement of multiple receiving transducers, the maximum amount of information can be obtained regarding the reflected waves of the defect. This method solves the inherent problems of an EMAT (i.e., low energy conversion efficiency) and achieves effective detection of microcrack defects. Second, we study the quantification principle of microcrack defects suitable for UOF-EMAT, and propose a deep neural network using physical knowledge regarding this theory. We rationally design the network structure based on the quantitative principles and logic obtained from this article. In addition, feedback and feedforward loss functions suitable for evaluating different forms of variables are proposed to integrate the physical concepts of ultrasonic guided wave testing into the neural network training. Finally, we verify the performance of the proposed GuwNet based on the UOF-EMAT. Compared with traditional nonphysics-informed methods, the length, depth, and direction of the quantification errors are reduced to 0.127 mm, 0.279%dt, and 1.843°, respectively, and the average quantification error is reduced by more than 80%.

In this work, a deep residual network named GFresNet-2D is proposed for a point-focusing shear horizontal guided wave electromagnetic acoustic transducer, which can be used to quantify different types of defects, such as pinholes, cracks, and corrosion, in materials. As the traditional feature extraction and statistical machine learning methods are too complex and rely on artificial recognition, an automatic feature extraction model based on deep learning is applied for defect detection and quantification. Owing to their similarity with the ultrasonic guided wave signals, the measured 1D signals from the experiments cannot be directly applied to train neural networks. Therefore, we used the normalization, minimum suppression, and continuous wavelet transform methods to convert the initial measured 1D signals into processed 2D images, and constructed a data set containing 1,440,000,000 signal/image data. The performance of the proposed GFresNet-2D model for this new data set was also compared with those of traditional models, and sensitivity analyses were performed for some of the representative parameters. The results confirm that the proposed method can contribute to the development of deep-learning-based defect quantification using the ultrasonic guided wave focusing method.

Cloud computing has brought a new method of resource utilization and management. Nowadays some researchers are working on cloud-based instrumentation and measurement systems designated as Instrumentation and Measurement Clouds (IMCs). However, until now, no standard definition or detailed architecture with an implemented system for IMC has been presented. This paper adopts the philosophy of cloud computing and brings forward a relatively standard definition and a novel architecture for IMC. The architecture inherits many key features of cloud computing, such as service provision on demand, scalability and so on, for remote Instrumentation and Measurement (IM) resource utilization and management. In the architecture, instruments and sensors are virtualized into abstracted resources, and commonly used IM functions are wrapped into services. Users can use these resources and services on demand remotely. Platforms implemented under such architecture can reduce the investment for building IM systems greatly, enable remote sharing of IM resources, increase utilization efficiency of various resources, and facilitate processing and analyzing of Big Data from instruments and sensors. Practical systems with a typical application are implemented upon the architecture. Results demonstrate that the novel IMC architecture can provide a new effective and efficient framework for establishing IM systems.