The insulated gate bipolar transistor (IGBT) is a kind of excellent performance switching device used widely in power electronic systems. How to estimate the remaining useful life (RUL) of an IGBT to ensure the safety and reliability of the power electronics system is currently a challenging issue in the field of IGBT reliability. The aim of this paper is to develop a prognostic technique for estimating IGBTs’ RUL. There is a need for an efficient prognostic algorithm that is able to support in-situ decision-making. In this paper, a novel prediction model with a complete structure based on optimally pruned extreme learning machine (OPELM) and Volterra series is proposed to track the IGBT’s degradation trace and estimate its RUL; we refer to this model as Volterra k-nearest neighbor OPELM prediction (VKOPP) model. This model uses the minimum entropy rate method and Volterra series to reconstruct phase space for IGBTs’ ageing samples, and a new weight update algorithm, which can effectively reduce the influence of the outliers and noises, is utilized to establish the VKOPP network; then a combination of the k-nearest neighbor method (KNN) and least squares estimation (LSE) method is used to calculate the output weights of OPELM and predict the RUL of the IGBT. The prognostic results show that the proposed approach can predict the RUL of IGBT modules with small error and achieve higher prediction precision and lower time cost than some classic prediction approaches.

Lithium-ion batteries (LiBs) are the most important part of electric vehicle (EV) systems. Because there are two different degradation rates during LiB degradation, there are many two-phase models for LiBs. However, most of these methods do not consider the randomness of the changing point in the two-phase model and cannot update the change time in real time. Therefore, this paper proposes a method based on the combination of the two-phase Wiener model and an extreme learning machine (ELM). The two-phase Wiener model is used to derive the mathematical expression of the remaining useful life (RUL), and the ELM is implemented to adaptively detect the changing point. Based on the Poisson distribution, the distribution of the changing time is derived as a gamma distribution. To evaluate the theoretical results and practicality of the proposed method, we perform both numerical and practical simulations. The results of the simulations show that due to the precise and adaptive detection of changing points, the proposed method produces a more accurate RUL prediction than existing methods. The error of our method for detecting the changing point is about 4% and the mean prediction error of RUL in the second phase is improved from 4.39 cycles to 1.61 cycles.

Analog circuits play an essential role in electronic systems. To strengthen the reliability of sophisticated electronic circuits, this paper proposes a novel analog circuit fault diagnosis method. Compared with traditional fault diagnosis, the fault diagnosis process in this paper uses a square wave as the stimulus of the circuit under test (CUT), which is beneficial for obtaining the response of the CUT with rich time and frequency domain information. The improved empirical wavelet transform (EWT), which can more accurately extract the amplitude modulated–frequency modulated (AM-FM) components, is used to preprocess the original response. Finally, based on the preprocessed data, a multi-input deep residual network (ResNet) is constructed for fault feature extraction and fault classification. The multi-input ResNet is a powerful approach for learning the fault characteristics of the CUT under different faults by learning the characteristics of the AM-FM components. The effectiveness of the method proposed in this paper is verified by comparing different fault diagnosis methods.

In order to solve tiny defect detection for solder balls in high-density flip-chip, this paper proposed feasibility study on the effect of detectability as well as classification based on eddy current pulsed thermography (ECPT). Specifically, numerical analysis of 3D finite element inductive heat model is generated to investigate disturbance on the temperature field for different kind of defects such as cracks, voids, etc. The temperature variation between defective and non-defective solder balls is monitored for defects identification and classification. Finally, experimental study is carried on the diameter 1mm tiny solder balls by using ECPT and verify the efficacy of the technique.

Carbon fiber reinforced polymer (CFRP) is widely used in transportation and aerospace. However, CFRP is susceptible to impact damage, leading to defects such as cracks and delamination. Moreover, detecting 4 J low-energy impact damage in CFRP is challenging. To address this issue, this paper uses eddy current pulsed thermography (ECPT) in combination with the optical flow amplitude-based method (OFABM) for non-destructive testing. The optical flow is used to track the transient heat propagation state of the CFRP composites, and the principal component analysis (PCA) is employed to extract defect information from the optical flow amplitude. A relationship between optical flow amplitude and impact energy is established, allowing quantitative evaluation of impact damage. Experimental results demonstrate that the OFABM effectively detects 4 J low-energy impact damage in CFRP and has the potential for the quantitative assessment of impact damage.

In the pulse eddy current thermal imaging experiments, the trends of temperature response curves of buried defects at different depths are same, but there are differences in cooling rates. The accuracy of depth quantification of buried defects can be improved by making full use of the rich information contained in the temperature response curves. To this end, a feature extraction, defect segmentation and depth quantification algorithm named curves-based similarity method is proposed in this paper. By making comprehensive use of the global and local features of thermal image sequences, the average similarity of the temperature response curve is used as the feature extraction method and quantification parameter. The effectiveness of the method is verified by simulation and experiment. The results show that the method can better enhance the defect information and suppress the noise compared with PCA (Principal Component Analysis) and PPT (Pulsed Phase Thermography). Additionally, the average error of the quantization results of the algorithm is reduced by 1.33% compared with the characteristic time quantization method.