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.

The storage life prediction of the aerospace electromagnetic relays(AEMRs) has become an engineering challenge due to the nonlinearity of AEMRs’ complex degradation process. As the widely used life prediction approaches, data-driven methods also failed to address this issue effectively. The main reason is their inherent flaw: the inability to effectively quantify the direct correlation between top-level failures and underlying physical or chemical changes. Therefore, based on the fusion of the physics of failure(PoF) and data-driven, a storage life prediction method for AEMR has been proposed in this manuscript. Firstly, according to the characteristics of the AEMR during storage, the degradation performance parameters were analyzed, and the accurate expression of the degradation model was established based on the failed physical process. Then, the degradation process of AEMR was modelled by fusion of the PoF and data-driven, while the model parameters were updated by an improved particle filter based on degradation data. Finally, the storage life of AEMR was predicted by the updated model. Compared with typical methods and machine-learning-based methods, on the one hand, the results show that the proposed method can restrain the fluctuation of nonlinear degradation data to some extent and make the fluctuation range of prediction error more stable, thus making the life prediction result more accurate. On the other hand, even when there is only a small sample size of observation data, it also gives a good prediction effect. Moreover, it establishes the relationship between life and underlying physical and chemical processes, which will be conducive to the optimization design of AEMR, to further improve the reliability of weapons and equipment.

Fault diagnosis and bounce reduction of electromagnetic actuators require one to obtain the electromechanical transient characteristics (ETCs) in real-time. ETCs can be measured by sensors, but due to their large size and intrusiveness, the approach has its limitations. Acquiring ETCs through finite element method (FEM) could be an alternative, except that FEM is known to be time consuming. In contrast, real-time measurement of coil current is not subjected to these restrictions, yet, it has not been properly utilized for calculating ETCs. In this regard, this paper analyzes the source of the armature kinetic energy, and thus proposes a model based on energy conservation (ECM), describing the relationship between the mechanical and electrical characteristics. Based on the model, ETCs can be calculated from coil current, moving part mass, and the static counterforce. To exemplify the effectiveness of the procedure, an electromagnetic contactor is considered, and the results obtained using the ECM are found fairly consistent with that obtained using sensors. The merit of the proposed ECM lies in its non-intrusive nature and its ability to circumvent the time-consuming FEM in calculating ETCs.

This article presents a paradigm shift in the evaluation of electronic product reliability, a cornerstone in mitigating risks inherent in electrical systems. The accuracy of reliability assessments hinges critically on degradation models, which frame the landscape of analysis. However, the ubiquitous Arrhenius model, despite its merits, encounters limitations in broad temperature applicability, deviating from empirical data beyond a critical threshold. The elusive identification of this threshold challenges, as degradation tests are both time consuming and resource-intensive. Furthermore, the Arrhenius approach struggles with complex data from progressive stress accelerated degradation tests, where nonconstant stress patterns emerge. To address these shortcomings, this groundbreaking study delves into the microscopic mysteries of metal spring stress relaxation, uncovering the fundamental nature of this phenomenon. From this microscopic perspective, this article introduces a novel degradation model, grounded firmly in the error function. The results from exhaustive constant and progressive stress accelerated degradation tests validate the superiority of the proposed model, surpassing the empirical Arrhenius formula in accuracy and reliability. This triumph stems from this model's meticulous consideration of the microscopic dynamics of stress relaxation, offering a more precise and robust framework for electronic reliability analysis.

Electromechanical components (EMCs) such as relays and contactors have been used extensively in industrial and military areas. The storage reliability of these EMCs has a direct impact on the reliability of the system that contains them. However, during the design phase, it is difficult to predict the storage reliability of EMCs because of few failure rate data of parts, as well as limited testing time and budgets. To address these problems, a virtual Manufacturing and testing method is proposed in this paper, so as to simulate the storage degradation process of batch EMCs. By considering the influence of the quality screening process in the manufacturing process, as well as the unit-to-unit variability of EMCs on the storage degradation paths and the overall life distribution of batch products, the storage failure distribution function is obtained, based on Wiener process. At the same time, the distribution of the diffusion coefficient in the degradation model and the failure distribution model is quantified by introducing testing data of related products as priori information, so as to reflect the uncertainty of the storage degradation process of EMCs. A case study of an electromagnetic relay is carried out to illustrate the effectiveness of the proposed approach.

A hybrid electromagnetic interference (EMI) filter is combined with passive filtering components and an active cancellation part. This kind of EMI filter shows good noise attenuation ability, and the total size is tiny. However, during the design procedure of the hybrid EMI filter (HEF), the passive part and the active part are independent, and the interaction between those two parts is not well considered. To cope with this problem, this letter proposes a new negative inductance-based HEF (NI-HEF). By constructing a negative inductance-based sensing structure, the sensing transformer could not only sense the common mode (CM) current but also boost the impedance of primary windings, which, in turn, helps to increase the attenuation capability of CM noise. Together with the active cancellation part, the whole filtering system could better block the propagation of CM noise. A prototype of the proposed NI-HEF has been built. Experimental results show that this new NI-HEF could achieve dramatic noise attenuation with a tiny size and weight.