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• First introduce the transfer learning into the loose particle localization research. • Apply the majority voting strategy to process the predicted labels. • Propose the definition of equipment-level loose particle localization accuracy. • Give specific implementation steps of loose particle localization methods. • Experiments validate the effectiveness and feasibility of the proposed method. The existing supervised-learning-based loose particle localization method requires a lot of resources to create a complete loose particle localization data set, which leads to the problems of long localization testing cycle, large data storage requirement and low resource utilization. In this study, the authors proposed an instance-based transfer learning method for locating loose particles inside aerospace equipment. It used correlations between the localization data sets corresponding to the aerospace equipment with similar structure or size, to complete the knowledge transfer from the source localization data set to the new localization data set. Specifically, based on the existing localization data set containing a large amount of localization data, when facing new aerospace equipment, only a new localization data set containing small amounts of localization data needed to be created. The TrAdaBoost algorithm is used to learn on the new localization data set, the weights of the localization data in the existing localization data set are calculated, the localization data with higher weights are filtered to add to the new localization data set, thus create the transfer training data set and train the loose particle transfer localization (abbr. LPTL) model. This effectively improves the localization efficiency and data resource utilization. In addition, based on engineering application requirements, the authors proposed the definition of equipment-level loose particle localization accuracy. Tests on aerospace power supplies and aerospace electronic single-machines show that, the appropriate weight threshold was set to 0.35, 6004 pieces of localization data were filtered from the existing localization data set. The average classification accuracy of 82.85% and the average localization accuracy of 83.33% were achieved by the LPTL model based on the parameter-optimized XGBoost ensemble classifier. In theory, this method can be extended and applied to the loose particle material identification and weight estimation research, and has high reference value. [ABSTRACT FROM AUTHOR]

Coal is considered as an abundant energy source in China and coal-to-methanol chain is an essential routing on account of methanol’s irreplaceable status in chemical industries. However, coal-based methanol production aroused controversy due to its intensive energy consumption and high greenhouse gas emission, compared with other processes by oil or natural gas. Carbon footprint is an improved indicator that evaluates both direct and indirect greenhouse gas emissions in the life cycle perspective and guides policymakers for better industry-chain planning. In this study we proposed the idea of hierarchical attribution management (HAM) to provide a classified method for evaluating carbon footprint of coal-to-methanol chain, combined with life cycle assessment (LCA) and the tool of ASPEN Plus. The results show that the life cycle carbon footprint was 2.971 t CO 2,e /t methanol. By the HAM, it’s concluded that methanol production process was the largest emission contributor in the defined life cycle system with a share of 92.86%, followed by coal mining process with 4.34%. Gasification unit and water-gas shift unit were two major greenhouse gas generators, accounting for 21.26% and 52.80% of life cycle emission, respectively, while methanol synthesis unit showed the potential for CO 2 utilization and emission reduction. Additionally, the results of sensitivity analysis showed that electricity emission factor with a sensitivity factor of 189.11 was the most extensive influence factor on life cycle emission due to its widest application. The discuss on effects of CCS on life cycle emission showed that carbon footprint approximately decreased by 64.9% when the methanol plant was retrofitted with CO 2 capture and compression, indicating that CCS is an effective way to alleviate global warming. [ABSTRACT FROM AUTHOR]

The non-contact nature of electromagnetic acoustic transducers (EMATs) allows a continuous operation at high temperatures without physical coupling. The existing high-temperature EMATs are mainly shear-wave EMATs, and there are few reports on shear-longitudinal wave or longitudinal-wave EMATs due to the low intensity of the horizontal magnetic field. However, a desirable EMAT design characteristic is the possibility of selecting to generate different acoustic wave modes for various engineering applications. In this paper, a high-temperature EMAT with a double-coil configuration on waveform generation in paramagnetic steel was studied. By adjusting the configuration relationship between the electromagnetic coil and the EMAT eddy-current coil, the selective generation of shear-wave, longitudinal-wave, and shear-longitudinal wave modes was realized. According to quantitative analysis of shear and longitudinal waves generated by the double-coil EMAT, the amplitude ratio of shear and longitudinal waves was about 1 when the diameter of the EMAT eddy-current coil was equal to the inner diameter of the electromagnetic coil. Furthermore, shear-wave mode and shear-longitudinal wave mode high-temperature EMATs were designed and fabricated. The EMAT was placed in a high-temperature environment to continuously measure the paramagnetic steel SUS304. The amplitudes of the shear wave and longitudinal wave at 500 °C decreased by 10.2 times and 3.8 times, respectively, compared with that at 25 °C. The designed EMAT can selectively generate and receive different bulk acoustic wave modes at high temperatures. [ABSTRACT FROM AUTHOR]

In this study, the authors combined the research on loose particle signal and component signal identification with the research on loose particle material identification for the first time, providing comprehensive and reliable loose particle detection results. Based on this, a signal detection and material identification method for loose particles inside sealed relays based on fusion classification model is proposed. Due to the limitations of technical means and confidentiality management, the authors made a real sealed relay sample, and took it as the research object. Through the steps of data acquisition, signal processing, feature engineering, and model training, the dedicated component identification feature library and material identification feature library was constructed, respectively, the component identification model and material identification model based on parameter-optimized SVM with linear kernel and XGBoost was trained, respectively. For the seal relay to be tested, through the steps of data acquisition, signal processing and feature engineering, the data set to be tested was created. The component identification model was used to identify component signals with loose particle signals, and the material identification model was used to identify the materials of loose particles. The majority voting process was used to convert the classification results into identification results, resulting in loose particle detection and material identification results. In addition, the general procedure steps of the proposed method for physical testing were given, and the identification accuracy for device-level loose particle detection was newly proposed. The loose particle testing event containing thirty-seven identification tasks shows that, the achieved identification accuracy was 97.30%, and 92.16% of the average classification accuracy was achieved by the component identification model, 80.41% of the average classification accuracy was achieved by the material identification model. This effectively demonstrates the feasibility and practicality of the proposed method in this paper. It is an important supplement to the loose particle detection research, and provides references for signal detection in similar fields. • First combine the loose particle signal detection and material identification study. • First construct the dedicated feature library and perform feature optimization. • Train the optimal component identification model and material identification model. • Newly add majority voting steps to obtain the identification results. • Newly propose the identification accuracy for device-level loose particle detection. [ABSTRACT FROM AUTHOR]

The complex internal structure and heterogeneous composition material of aerospace equipment make it difficult to apply traditional acoustic emission source localization methods to the loose particle localization research. In previous studies, the authors transformed the loose particle localization problem into the multi-classification problem in machine learning, trained the loose particle localization model, but there were problems of low classification accuracy and low practicality. In this paper, the authors first introduced the ensemble learning idea into the loose particle detection field, designed a complete loose particle localization scheme, analyzed the above problems from multiple perspectives, and proposed new algorithms or strategies to enhance the superiority and practicability of the loose particle localization model. Specifically, in view of the low classification accuracy, the authors carried out the research on pulse preprocessing, feature engineering and model training from the four perspectives of signal, feature, data set and classifier, respectively. The zero-pulse-filling pulse matching algorithm and the channel-weighting-based feature selection method was newly proposed, Mel-Frequency Cepstral Coefficients features was newly extracted, feature optimization scheme was designed, and XGBoost ensemble classifier was trained. Thus, high-quality loose particle signals, high-quality localization data sets and high-performance loose particle localization models were obtained, respectively. Test results show that, the classification accuracy achieved by the new loose particle localization model was 96.80%, which was a significant improvement compared to the 83.53% achieved in previous studies. In view of the low practicality, the authors built the loose particle localization experimental system, gave the specific implementation steps of the loose particle localization method, as well as the general procedures for applying the loose particle localization model for physical testing. Meanwhile, taking into account the requirements of aerospace engineering applications, the authors added the majority voting strategy to convert classification results into localization results, thus newly proposed the definition of equipment-level loose particle localization accuracy. Several physical testing results show that, the loose particle localization model achieved a localization accuracy of 90.91%, which effectively verified the feasibility and stability of the proposed method. This study is an important supplement to the loose particle detection research, and of great significance in improving the reliability of aerospace systems. [ABSTRACT FROM AUTHOR]

• Dynamic characteristics testing and evaluation of electromagnetic devices. • A digital modeling approach to evaluate dynamic characterization is proposed. • Multiphysics modeling approach for FEM description of the electromagnetic devices. • The nonlinear effects of temperature on the dynamic characteristics are considered. • Transmission line parallel algorithm improves the efficiency of solving FEM models. Electromagnetic systems of Electromagnetic devices involve multiphysics coupling of electromagnetic fields, electrical circuits, mechanical motion, and temperature fields. When the digital prototype model has a large number of meshes, the commercial finite element software also has the disadvantage of long computation time in the simulation of dynamic characteristics of electrical apparatus. This paper proposes a transmission line method (TLM) based digital modeling method for the electromagnetic-thermal-force coupling to test and evaluate the static and dynamic characteristics of electromagnetic devices more accurately and efficiently. The case study is developed on a direct current (DC) contactor, and the dynamic characteristics testing and modeling in the electromagnetic-thermal-force coupling of the contactor are investigated. The numerical results of the finite element method (FEM) model for the electromagnetic system of the contactor are compared with the commercial FEM software Altair Flux and the experimental results, which demonstrate the exceptional accuracy of the proposed digital modeling method for the multiphysics coupling of the DC contactor. Meanwhile, the dynamic characteristics of the DC contactor at different ambient temperatures are measured and evaluated, and the comparison with the experimental measurements shows the applicability of the proposed digital prototype model at different ambient temperatures. Compared with the conventional Newton-Raphson (NR) iteration, when dividing fine mesh and extremely fine mesh, the TLM parallel computing significantly improves the efficiency of solving the dynamic characteristics of multiphysics coupling by about 35 % and 44 % with the same computational accuracy, respectively. The dynamic characteristics evaluation method combined with digital modeling technology and experimental measurements proposed in this paper demonstrates high accuracy and solving efficiency, making a notable contribution to the field of design and optimization of electromagnetic devices. [ABSTRACT FROM AUTHOR]

Abstract With the voltage increasing in DC power systems, positive bias temperature instability (PBTI) induced gate oxide degradation of commercial n-channel power vertical double diffused MOSFETs (VDMOSFETs) has been greatly enhanced recently. However, it is not mature in some aspects, e.g., parametric models of PBTI and the turn-around phenomenon are rarely studied. Aiming at these disadvantages, the experimental study and the argument are proposed in this paper. First, the simulation was used to identify and verify the threshold voltage as the characteristic parameter and the accelerated experiment test was conducted to collect data for degradation modeling. Then, a Power law-Arrhenius combined model was adopted in the degradation model to take into account the electrical and thermal influence. Besides that, a two-phase PBTI degradation model was presented, which can describe the turn-around phenomenon. Moreover, the comparison of the estimated remaining useful life (RUL) results with the experimental data verified the accuracy of the developed model. Highlights • A two-phase model is developed using threshold voltage to describe the PBTI degradation. • Turn-around appearance point can also be estimated by the falling and rising phase of the model. • A Power law-Arrhenius combined model is adopted in the degradation model to take into account the electrical and thermal influence. • Utilizing the model, a more accurate remaining useful life of power VDMOSFETs can be estimated. [ABSTRACT FROM AUTHOR]

• A new class of multi-stress acceleration models with interaction effects is designed. • A multi-stress accelerated degradation model is further developed by incorporating a generalized Wiener process. • Simulation and a real-world case are performed to validate the effectiveness and practical values of the proposed model. Most products operate under multiple stresses. The influences of multi-stress factors on products are commonly not independent and promote a more violent degradation through interactions, referred to as stress interaction effects. However, few studies consider such effects in multi-stress acceleration models, which may reduce the extrapolation precision and further lead to an inaccurate reliability assessment. In this paper, a new class of multi-stress acceleration models with interaction effects is designed to extrapolate more accurate reliability metrics under multi-stress operating conditions. The main stress effect terms are determined by two criteria: physical stress-based ageing law and statistical correlation. The interaction effects are interpreted as the influences of other stress variables on the main stress effects. In particular, the explicit functions of interaction effects are alternative, which can be identified by adaptive optimization using the maximum likelihood criterion. Furthermore, this multi-stress acceleration model is extended to an accelerated degradation model by integrating a generalized Wiener process with nonlinear time scale functions and random effects. The acceleration factor constant principle is utilized to identify the stress-dependent parameters, facilitating a more appropriate model development. Finally, simulation and a real-world case are performed to validate the effectiveness and practical values of the proposed model. [ABSTRACT FROM AUTHOR]

Random effects are ubiquitous in the degradation processes of multiple performance characteristics (PCs) of a complex system, and these PCs are not commonly independent. Neglecting either of the above features will lead to bias in system reliability estimation. Therefore, this study develops a reliability estimation method for the complex systems with multiple PC degradations considering both of the features. First, a Wiener process-based marginal degradation model with random effects (including the random initial PC, unit-to-unit variability, temporal uncertainty, and measurement errors) is proposed to track the degradation path of each PC. A model based on D-vine copulas is put forward to describe the correlation among multiple PCs. Then, a system reliability function is derived based on the two models. Furthermore, a two-step statistical inference is developed to estimate the unknown parameters in the system reliability function. Finally, using a practical case, the effectiveness of the proposed method is illustrated. • A reliability estimation method is proposed for complex systems. • A marginal Wiener process-based degradation model with random effects is proposed. • The correlation among multiple performance characteristics is modeled by D-vine copulas. • A two-step statistical inference is developed to perform the parameter estimation. [ABSTRACT FROM AUTHOR]

As a key component in power electronic systems, the accurate reliability assessment of metallized film capacitors (MFC) is very critical to assure the system reliability and safety. In most operating conditions, the gradual capacitance decrease induced by dielectric film aging and the accumulation of each sudden capacitance loss by random self-healing events under elevated temperature and voltage are the main degradation mechanisms of MFC. However, most investigations tend to directly fit the MFC degradation data by some statistical means, which ignores the specific degradation mechanisms. Such treatments for degradation data often perform a lower modeling accuracy, which may also bring an obvious deviation of reliability assessment result. In this article, we design a degradation mechanisms-based reliability modeling approach for MFC under temperature and voltage stresses. The dielectric film aging is characterized by Wiener process, where the aging rate is dependent with temperature. The compound Poisson process is developed to capture the random self-healing events, where the arrival rate and each sudden capacitance loss are related to the operating voltage as well as the dielectric film aging rate. Finally, an ADT with temperature and voltage acceleration for MFC is conducted to validate the effectiveness of the proposed approach. • A degradation mechanisms-based reliability model for metallized film capacitor is designed. • The dielectric film aging and self-healing process are quantitatively characterized. • An ADT with temperature and voltage acceleration is conducted to validate the proposed model. [ABSTRACT FROM AUTHOR]

Abstract: Electrical contacts, which are used in aerospace engineering with high reliability requirements, are usually plated in order to prevent corrosion. The wear and fretting corrosion behavior of gold plated copper contacts is studied at different current loads. The degradation of contacts is explained with reference to current to melt metal and breakdown oxide films. The contacts with higher current delay the time before reaching failure. The comparison between the cases with and without current load revealed metal melting as a result of current heating. The observed surface morphology reveals five typical regions within the fretted zone. [Copyright &y& Elsevier]

• A SSADT optimal design method is developed. • A generalized Wiener process-based model considering the distribution feature is proposed. • An optimization criterion for products' degradation distribution is proposed. • Statistical inference of the proposed generalized Wiener process-based model is studied. • A set of metal-film resistor data is applied to illustrate the proposed method The Step-stress accelerated degradation test (SSADT) is an efficient tool to assess the reliability of products. To ensure an accurate evaluation of reliability-related indexes with limited cost, the optimal design for SSADTs is widely applied. Conventional optimal design methods ideally assume the degradation process to be linear and distribution-free, which may cause the designed test plans to be drastically different from the optimal one. To overcome the drawbacks of the unreasonable assumption, this paper develops a new optimal design method for SSADTs, including a generalized Wiener process-based degradation model (GWPDM) considering the distribution feature and a corresponding optimization criterion aiming at accurate prediction of degradation distributions. The GWPDM is first discussed, where the influence of the nonlinearity, unit-to-unit variability, measurement error and stress-related temporal variability on the degradation process is clarified. The statistical inference of the developed GWPDM is conducted to deduce the proposed optimization criterion. Thus, the optimization model is established to determine the optimal SSADT plan. The proposed method is used to design a SSADT plan for the metal-film resistor. Finally, the necessity and the robustness of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Abstract: Surface wave electromagnetic acoustic transducers (EMATs) are becoming preferred sensors of automatic inspection systems for the detection of surface-layer defects in hostile environments. Aiming at establishing a 3-D model for meander-line-coil surface wave EMATs operating on the Lorentz principle, a novel modeling method is proposed by combining numerical calculations and analytical solutions with special attention to the influence of the dynamic magnetic field. Simulation and analysis find that the frequency of the surface waves due to the dynamic magnetic field is twice that due to the static magnetic field; the excitation of surface waves with two frequencies can effectively improve the sensitivity of EMATs to flaws; and the force due to the dynamic magnetic field generates surface waves more efficiently than that due to the static one when the excitation current exceeds 528.9A. The accuracy of the established model is verified by experiment. [Copyright &y& Elsevier]

The life-cycle reliability of high-power DC electromagnetic devices due to multi-source heterogeneous uncertainties in the design, manufacturing, loads, degradation and cumulative damages has become increasingly prominent, life-cycle reliability optimization has attracted extensive attention. The design optimization strategy based on reliability, which combines the static/time-independent hypothesis and random theory, is inapplicable to life-cycle design optimization. Therefore, a time-dependent uncertainty analysis and life-cycle quality reliability optimization method are proposed in this study. Based on the information entropy transformation method, the fuzzy uncertainty is transformed into a probability density function of equivalent random variables, and intervals for the random variables are determined in the standard normal space. In this way, the heterogeneous uncertainty is unified as an interval type. Moreover, the time-dependent characteristics of the uncertainty parameters are transformed into a time-dependent of the characteristics through ellipsoidal model process. A life-cycle expression method based on an orthonormal basis is proposed to form a life-cycle time-dependent non-probabilistic convex model process. Therefore, the analytical expression of out-crossing rate in which there are many non-probabilistic convex model process parameters are derived and the corresponding upper and lower bounds of out-crossing rate can be obtained. Then, a life-cycle reliability design optimization model is constructed and the optimal solution is determined from intelligence algorithm. The effectiveness of proposed method was verified by a high-power DC electromagnetic relay in renewable energy systems. [ABSTRACT FROM AUTHOR]

It remains a great many of challenge to explore time-dependent reliability-based design and optimization problems, owing to the high computational cost of time-dependent reliability assessment and analysis, the difficulties to life-cycle reliability modelling. To address these issues, ADE-T-PCE, a methodology incorporating an adaptive differential evolution algorithm and an enhanced instantaneous time-dependent polynomial chaos expansion is proposed. The polynomial chaos expression (PCE) method was improved based on time-domain extension and principal component selection partial least squares, the life-cycle reliability model is formed. Consequently, dynamic niche Pareto optimal solution selection strategy, particle variations and optimal solution screening strategy, number and strategy of particle variation for the differential evolution algorithm were proposed to meet demands for multi-object time-dependent reliability design optimization. The effectiveness of proposed method in this study was verified through an electromagnetic relay in hybrid vehicle. [ABSTRACT FROM AUTHOR]