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.

Hard (open and short) faults and discrete parameter faults (DPF) are the mostly used fault models in simulation-before-test (SBT) method. Because that the parameter of analog element is continuous, the DPF can not elaborately characterize all possible continuous parameter faults (CPF) occurring in analog circuit. To address such problem, a genetic algorithm (GA) based simulation after test(SAT) fault diagnosis method is proposed in this paper. The fault diagnosis is transformed into an optimization problem. The genes represent the parameter values of potential faulty components. The faulty circuit response is the objective. Our target is to minimize the difference between the actual faulty response and the GA simulated response. The chromosome that minimize the difference gives the solution. This method does not save all possible fault in advance while it can diagnosis all continuous fault value. The effectiveness of the proposed method is examined by using filter circuit examples.

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.

Recently, the analysis of remaining useful life (RUL), which is central to the reliability assessment of lasers under various environment stresses, has become one of the most crucial issues in the field of laser reliability. In this paper, a similarity-based difference analysis (SbDA) approach is proposed to estimate the RUL of GaAs-based semiconductor lasers. SbDA utilizes the inherent relation between historical samples and the on-site sample in which we calculate the inherent difference to acquire a more exact performance degradation value. This method can make adequate use of the limited historical data sets to more accurately estimate the lifetime of an operating GaAs laser. In addition, we present several significant results of studies of model parameters, such as random disturbance, weight distribution factor, and similarity. Experimental results show the effectiveness and advantages of the proposed SbDA method.

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.