Owing to the lack of feasible fault modeling method, hard (open and short) faults, and discretized parametric faults are still the mostly used fault models. These models cannot characterize all soft (parameter shifting) faults because that the parameter of analog element be of continuity character. To address this concern, a complex field fault modeling method is presented first. If fault happens to passive element xi in linear analog circuit, the real (U_r) and imaginary (U_j) parts of faulty voltage phasor U°(U°=U_r+j U_j) must satisfy binary quadratic function F_i(Ur,U_j)=0, which is independent from the value of element xi and uniquely determined by its location and the nominal circuit under test. Hence, the binary quadratic function can model any continuous parameter shifting (soft) or hard fault. Second, to avoid calculating the explicit expression of binary quadratic function, a simulation-based method is given to obtain the locus of the change in the function F_z in the complex plane. Besides, the nominal (fault-free) point, the loci determined by the binary quadratic functions might intersect with each other. It is referred to as aliasing problem in this paper. This problem can be solved by adjusting the frequency of the input signal. Therefore, a constraint optimization method is proposed to select optimal test frequencies. The proposed test generation and compression methods, when combined with the complex field modeling method, can pinpoint much more fault components with less test points and frequencies when compared with the other methods.

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.

It is known that optimal tests selection is an important issue in design for testability field. The selection is subjected to constraints of testability metrics. At the same time, test time and economic costs need to be minimized. The tests selection is a combinatorial multiobjective optimization problem. According to the schema theory, the more the amount of testability constraints, the more the constraints are violated. Therefore, the canonical genetic algorithm (GA) evolves slowly, and the Pareto-optimal solutions are less likely to be found. Based on these considerations, a parallel-series multiobjective GA (PSMOGA) is proposed. First, each test procedure is handled by a submultiobjective GA (MOGA) independently. The chromosome length of the ith sub-MOGA is equal to the available tests number of the ith test procedure. The sub-MOGAs are executed in parallel. The Pareto-optimal solutions to every procedure are saved for further process. Second, the MOGA is used to handle the optimal test selection problem for the whole product. The length of the chromosome is equal to the amount of the test procedures. The ith gene can vary between one and ki, where ki is the solution amount of the ith procedure. The genetic material from the subproblem will not be changed. Hence, the subconstraints will never be violated in the MOGA. The effectiveness and efficiency of the proposed method are verified by statistical experiments.

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, let alone the double soft fault. 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. 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 faults in advance whereas it can diagnosis single and double continuous faults. The effectiveness of the proposed method is examined by using filter circuit examples.