Stainless steel/carbon steel double-layer structures are commonly used in industries, but they are prone to generate internal defects (such as delamination and corrosions) at the bonding interface of the carbon steel layer. Eddy current pulsed thermography (ECPT) has shown promise for subsurface defect detection due to its high excitation and concentrated heating area. However, the complex heat transfer in multilayer structures lead to poor signal-to-noise ratios and poses challenges for defect identification. Moreover, the data-driven algorithms like PCA and ICA, though widely used in postprocessing, are faced with unstable performance and poor interpretability due to the lack of attention to the specific physical mechanism. To address these issues, this article proposes a thermal parameter reconstruction (TPR) imaging method to better detect the defect regions. Specifically, TPR regards the test sample as a 3-D thermal impedance space and projects it onto a parameterized grid plane. According to the thermal diffusion mechanism in ECPT, TPR builds a physical model to calculate the spatial thermal parameters of the projected surface. Through the reconstructed visual thermal parameters grid, the shape information of internal defects can be more clearly detected. An experiment on double-layer stainless steel/carbon steel structures is conducted, which validates the enhancement effectiveness of TPR on both round and irregularly shaped interlayer defects.

Graphs are widely recognized for their robust ability to represent objects through nodes and edges. This article aims to provide stable visual guidance for operators to identify electronic components on printed circuit board (PCB) images during the manufacturing and maintenance processes, using graph techniques to enhance the efficiency of component localization. A three-stage approach is proposed to achieve this objective. First, a method based on graph neural networks is introduced for electronic component detection, trained exclusively on the public FISC-PCB dataset. Second, the registration of the physical location with PCB images is modeled as a Markov Chain, and a fast subgraph matching algorithm is proposed to reduce time and space complexities by leveraging a second-order attribute similarity approximation. Finally, the random sample consensus algorithm is employed to eliminate outliers and calculate geometric relationships, enabling accurate positioning. The proposed framework is evaluated using ten PCB images captured by line-scan cameras or mobile phone cameras and compared with leading contemporary methods. The various experimental results demonstrate that the proposed method outperforms existing state-of-the-art solutions in localization accuracy and meets the requirements of efficiency and space utilization.

Insulated gate bipolar transistors (IGBTs) are the key semiconductor power devices in the systems of power electronics due to the advantages of large capacity, fast switching speed, easy to drive, low on-voltage and high input impedance. Due to the temperature swinging and mismatches of the coefficients of thermal expansion (CTE) of internal materials in IGBT modules, electrical-thermal-mechanical coupling is generated and inevitably results in thermal deformation. This thermal deformation is one of the key factors causing IGBT reliability issues. Therefore, it is of great significance to study the electrical-thermal-mechanical deformation characteristics of IGBT modules.

Switching mechanical wave (SMW) effect has attracted extensive attention over the past few years owing to its broad application prospects in condition monitoring for insulated gate bipolar transistor (IGBT) devices. The latest research suggests that the method of directly measuring the mechanical vibration signal on IGBT bare die by laser vibrometer is of great significance and potential to the mechanism revelation of SMW effect and the accurate extraction of its physical characteristics. Nevertheless, the measured vibration signals in IGBT modules by laser vibrometer typically contain a variety of other interference signals, such as thermal deformation signal, electromagnetic jamming, and background noise, in addition to the expected target SMW signals. Therefore, the aim of this work is to develop an effective signal decoupling method to realize the extraction of SMW signal from the aliasing signals obtained by laser vibration measurement.

Switching mechanical wave (SMW) occurring at switching moment in insulated gate bipolar transistor (IGBT) has attracted extensive attention in recent years. However, AE sensors widely utilized in current researches can hardly realize the SMW detection in the local area of single-tube IGBTs due to the limitations of large volume and low spatial resolution, thus hindering the further investigation of SMW effect mechanism revelation and physical properties. Therefore, this work aims to develop a high spatial resolution laser interferometric detection system to capture SMW signals at different spatial locations of single-tube IGBTs, thus enhancing the comprehension on the underlying mechanisms and physical properties of SMW effect in single-tube IGBTs.

Eddy current pulsed thermography (ECPT) has attracted much attention in nondestructive testing for its noncontact and large field of view. However, the ECPT images usually suffer from the thermal diffusion blurs. The fusion of temperature spatial and temporal features is the hotspot in the new research of ECPT enhancing methods, but these two features are contradictory on the speed and the accuracy of algorithms. Specifically, spatial features process fast but perform poorly in accuracy and antinoise ability, while temporal features are usually calculated from the whole ECPT video sequence, which inevitably increases the demand for data storage and the time cost, especially in the detection of large workpieces, such as engine blades or pressure pipelines. In this article, an enhanced diffusion-based method (EDBM) is proposed to solve this issue, which maps the temporal features through the spatial features of a single ECPT image, significantly reduces the input data volume and shows great potential in ECPT online detection. Experiments on multiple artificial and natural samples verify that, compared with raw ECPT image, the proposed EDBM can reduce root-mean-square error by 51.7%–86.5% (73.2% on average) and improve signal-to-noise ratio by 3.70–16.8 times (6.82 times on average), which performs better than the commonly spatial-based and temporal-based ECPT enhancement algorithms, such as enhanced Canny and independent component analysis, close to the latest sparse-model decomposition methods, but with two orders of magnitude less time cost.

Extensive research has demonstrated that memristors or trigonometric functions can enhance the complexity of discrete chaotic maps. This article introduces a four-dimensional trigonometric-based memristor hyperchaotic map (4D-TBMHM). By combining discrete memristors, sine, and cosine, the 4D-TBMHM exhibits complex dynamical behaviors. Bifurcation and multistability phenomena are demonstrated using numerical methods. The 4D-TBMHM demonstrates symmetric or attractor self-growth based on iteration length for various system control parameters and initial states, and its complicated complex fractal structure and exemplary performance metrics are also highlighted. Furthermore, an attractor hybrid control, capable of arbitrary positioning and shaping, is proposed. An field-programmable gate array (FPGA)-based hardware prototype is developed, and the attractors are experimentally captured. Moreover, by combining 4D-TBMHM with an arrayed linear feedback shift register, an ultra-fast pseudorandom number generator (UFPRNG) with a throughput of 195.2 Gbps is achieved on an FPGA, surpassing contemporary techniques. Last, the generated UFPRNG is employed for 2.8 Gbps signal generation with noise, and experimental results illustrate its remarkable real-time capability and the PRNG's inherent randomness, facilitating signal generation with any signal-to-noise ratios.

Accurately evaluating the 3-D profile of nonferromagnetic metal surface defects is essential for nondestructive evaluation. Eddy current pulsed thermography (ECPT) has been used for reconstructing surface defects, which utilizes multiphysics coupling of electromagnetic and thermal fields. Previous investigations mainly employ the thermal field for reconstruction. However, the thermal field varies slowly over time and has weak correlation, which causes serious blur and noise. In this article, a surface defect 3-D profile reconstruction method based on eddy current field distribution is proposed. The proposed method takes advantage of the strong global correlation of eddy current fields and proposes an inversion technique to reconstruct defects. Experimental results demonstrate that the proposed method can effectively reconstruct the 3-D profile of defects with depths within the skin depth, including complex-shaped defects and natural cracks.

The recently proposed induced current thermo-electrical impedance tomography (ICTEIT) is a nondestructive evaluation method for nonferromagnetic metal materials. The method reconstructs the defect profile by solving conductivity distribution from eddy currents, which has shown good performance in simple defects, but worse performance in complex defects (such as multiple adjacent defects and defects with complex contours). The main reason is that the unidirectional excitation produces low eddy current regions near the defect, which lacks reliable reconstruction information. For the problem, multidirectional excitation is able to make up for the low eddy current regions. However, previous methods simply superimpose the information of all eddy currents, and cannot integrate them for reconstruction. To solve the problem, we present a multidirectional information fusion method for defect reconstruction. The proposed method uses least squares optimization and transforms the information of multidirectional currents into a system of linear equations associated with conductivity, which constrains the solution and makes the solution satisfy each current simultaneously. Furthermore, to solve the ill-conditioning in the inverse problem, the total variation regularization method is introduced. Experiments are conducted on multiple neighboring defects and defects with complex shapes to validate the proposed method's performance.

Nonferromagnetic metal materials are widely used in industry. Defects generated during manufacture and use may lead to serious accidents. The defect reconstruction is important for nondestructive evaluation. Eddy current pulsed thermography (ECPT) is a well-known nondestructive testing and evaluation method, but hardly reconstruct fully defect profile. Electrical impedance tomography (EIT) shows promising potential in defect profile reconstruction, but suffers from electrode number limitation. In this article, EIT is introduced to ECPT image sequences processing, and a new method, induced current thermo-electrical impedance tomography, is developed. The proposed method takes each infrared camera pixel as a virtual electrode, captures the electric current distribution with spatial resolution as high as camera, and reconstructs conductivity distribution at pixel level from which the defect profile can be identified. Experiments with different defects are carried out to verify the performance of the proposed method.

In the context of industrial big data, the data-driven remaining useful life prediction for rolling bearings has been greatly developed. Aimed at the shortcomings of feature selection, feature fusion, and health state segment, this article proposes a synthetic feature processing method for remaining useful life prediction of rolling bearings. First, a double-layer feature selection method is proposed to screen the feature subset from the candidate features in order to lift the degradation trend sensibility and eliminate redundancy. Second, the health indicator is constructed by a comprehensive method based on the adaptive feature fusion method and auto association kernel regression model. Third, the bottom-up algorithm is utilized to divide the health states of health indicator and obtain the first prediction time point. In this way, the remaining useful life prediction error brought by random-state dividing can be avoided. Finally, in the case of dynamic working conditions and multiple failure modes, the remaining useful life prediction model is built by long short-term memory neural network and piecewise linear fitting health indicator to map the relationship between fitting health indicator and remaining useful life. The proposed piecewise linear fitting health indicator is compared with the original health indicator and original vibration signal. The experimental results demonstrate the more robust and accurate prediction effectiveness of the method.

This paper presents an investigation of dynamic thermal deformation characteristics in insulated gate bipolar transistor (IGBT) modules under bonding wire cracking condition by means of finite element simulation and experimental validation. Firstly, a realistically restored three-dimensional geometric model for IGBT modules is constructed and simulated to investigate thermal deformation field. Then the thermal deformation field characteristics under bonding wire intact and cracked conditions are compared and analyzed indepth. The result shows that the thermal deformation fluctuation amplitude of the cracked bonding wire decreases by 82%, while the thermal deformation value of other unbroken wires increases by 35% on average. Finally, the experimental verification is carried out, and the conclusion shows that it coincides well with the simulation results. This work provides confident evidence and important data to facilitate more precise life-time predictions and thermal-mechanical reliability assessment for power electronic modules.

In this article, the asynchronous fault detection (FD) strategy is investigated in frequency domain for nonlinear Markov jump systems under fading channels. In order to estimate the system dynamics and meet the fact that not all the running modes can be observed exactly, a set of asynchronous FD filters is proposed. By using statistical methods and the Lynapunov stability theory, the augmented system is shown to be stochastic stable with a prescribed$l_{2}$gain even under fading transmissions. Then, a novel lemma is developed to capture the finite frequency performance. Some solvable conditions with less conservatism are subsequently deduced by exploiting novel decoupling techniques and additional slack variables. Besides, the FD filter gains could be calculated with the aid of the derived conditions. Finally, the effectiveness of the proposed method is shown by an illustrative example.