The condition monitoring problem of power devices is significant for diagnostics and prognostics of a switched-mode power supply (SMPS) system. For power mosfet, the gate oxide degradation often occurs in various applications. However, there is no online condition monitoring method for gate oxide degradation so far. In this paper, a new precursor that can be used for online condition monitoring of power mosfet gate oxide degradation is proposed. Gate oxide degradation mechanisms and effect are summarized, and the mosfet turn-on process is analyzed. Then, a theoretical model is established to describe the relationship between miller platform voltage and two types of gate oxide defects, and miller platform voltage is identified as a new precursor. The precursor can be extracted without impacting system operation, thus online condition monitoring can be accomplished. The accelerated degradation test is carried out for power mosfets with both high electric field and gamma irradiation methods, and the degraded devices injection and in situ monitoring of miller platform voltage are conducted on a BOOST circuit to verify the feasibility of the new precursor. Experimental results demonstrate that the new precursor can be applied to online condition monitoring of power mosfet gate oxide degradation in the SMPS system.

Bearing incipient fault diagnosis is very crucial to the timely condition-based maintenance (CBM), but it is also relatively difficult because of the faint feature and environmental interference. In this article, an adaptive optimized time-varying filtering-based empirical mode decomposition (TVF-EMD) (AO-TVF-EMD) is put forward to extract more explicit and abundant incipient fault features by optimizing the parameter combination in conventional TVF-EMD. First, a novel integrated indicator termed sparsity-impact measure index (SIMI) constructed from dispersion entropy (DE) and envelope characteristic frequency ratio (ECFR) is designed as the parameter selection criterion. This index fully guarantees the optimal sparsity and impact properties of the obtained modes simultaneously, considering both time- and frequency-domain characteristics. Simulation verified that SIMI can not only sensitively characterize the variation of fault feature but also is robust to the environmental disturbance. Subsequently, we employ the Salp swarm algorithm (SSA) to realize the accurate search of parameter combinations where the minimum SIMI is defined as the fitness function. Moreover, comparative analysis in two runs to failure tests of bearings demonstrate that AO-TVF-EMD is more effective to extract incipient fault features than the maximum weighted Kurtosis (MWK) optimized TVF-EMD, the conventional TVF-EMD with fixed parameter, and some other advanced signal processing methods, which highlights the superiority of the proposed method.

Dependent degradation processes of performance characteristics are ubiquitous in engineered systems. The initial state of each degradation process is usually random and relates to the degradation rate. Unfort unately, most existing Wiener-based bivariate degradation models fail to consider these two features, which may limit the accuracy of system reliability assessment. In order to surmount this limitation, in this article, we develop a new bivariate degradation model, which involves a generalized Wiener process-based marginal degradation model that considers the random initial state and the correlation between the initial state and the degradation rate and a corresponding degradation increment-based dependence structure (DS). The proposed bivariate degradation model and its reliability function are derived first. Then, a two-stage statistical inference is introduced using the maximum likelihood estimation method. Furthermore, a simulation study investigates the performance of the statistical inference and the misspecification effects of DSs and marginal degradation models. Finally, an illustrative example demonstrates the effectiveness of the proposed model.