Health status assessment of radar systems at aerospace launch sites by fuzzy analytic hierarchy process

The fatigue damage of gear is caused by the coexistence and mutual influence of residual strength degradation and cumulative damage. The residual strength decreases continuously with the change of the loads and the increase of stress cycles. Most of the traditional cumulative damage models ignore the effect of residual strength degradation on cumulative damage. The exponential parameter in the traditional Corten‐Dolan model was defined as exponentials function related to the strength degradation coefficient, and the strength degradation coefficient was deduced by analyzing the residual strength degradation model, and the modified Corten‐Dolan model considering the residual strength degradation was established. Based on the stress‐strength interference model, a gear reliability model considering residual strength degradation was established. The effectiveness of the strength degradation model and the accuracy of the modified Corten‐Dolan model are verified by the analysis of the test data, which provides a new idea for the reliability analysis of gear. The accuracy of the gear reliability model proposed in this paper was verified by Monte‐Carlo (M‐C) simulation, which has practical significance for engineering application.

Section: Introductionmentioning

confidence: 99%

Modified nonlinear cumulative damage model considering residual strength degradation and gear reliability analysis

Ding

Yan

et al. 2023

“…4,5 Aleatory uncertainty is socalled inherent uncertainty while epistemic uncertainty arises from inadequate knowledge, subjectivity, indeterminacy, ambiguity, fragmentary, or dubious information. [6][7][8][9] On the one hand, the low-fidelity model involves multiple imprecise assumptions and expert experience. On the other hand, the experimental data used for model calibration are sparse and faced with random interference during the experiment.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, model calibration inevitably contains aleatory and epistemic uncertainty 4,5 . Aleatory uncertainty is so‐called inherent uncertainty while epistemic uncertainty arises from inadequate knowledge, subjectivity, indeterminacy, ambiguity, fragmentary, or dubious information 6–9 . On the one hand, the low‐fidelity model involves multiple imprecise assumptions and expert experience.…”

Section: Introductionmentioning

confidence: 99%

A modular Bayesian approach to model calibration of low‐fidelity simulation models

2023

Low‐fidelity computational models have been widely used for the computation of complex engineered systems to greatly save the computational time while sacrificing the computational precision. Model calibration can be implemented for low‐fidelity simulation models to remedy the errors of simulation results. This article proposes a modular Bayesian updating framework that considers epistemic uncertainty to achieve model calibration of low‐fidelity simulation models. The proposed framework mainly contains two steps: (1) A model calibration framework based on Bayesian theory is proposed that can simultaneously quantify the model form uncertainty, model parameter uncertainty, and the experimental measurement uncertainty, (2) the proposed method updates a low‐fidelity surrogate model via a Metropolis–Hastings (M–H) algorithm by using high‐fidelity experimental data. The proposed method greatly improves the prediction accuracy of the simulation model and enhances the computational efficiency. A mathematical example is used to testify the accuracy of the developed method, while the aerodynamics simulation of the NACA0012 airfoil is leveraged to demonstrate the engineering application. The results show that the posterior prediction mean is in better agreement with the experimental mean than the prior prediction mean, suggesting that the modular Bayesian updating method improves the prediction accuracy of low‐fidelity simulation models.

“…1,2 Since the 1980s, the third revolution in the power electronic circuit occurred, and electronic components represented by power transistors and switchable thyristors were widely deployed in industrial production due to their low noise, high efficiency, and controlled shutdown. 3 As an economical and efficient converter and control circuit, the fault location, 4,5 reliability assessment, [6][7][8] and health status assessment, 9,10 of the power electronic circuit are of great interests to industry and academia. [11][12][13] Currently, power electronic circuit fault location methods can be classified into three categories: hardware circuitdriven, model-driven, and data-driven methods.…”

Section: Introductionmentioning

confidence: 99%

Sparse convolutional autoencoder‐based fault location for drive circuits in nuclear reactors

Yang,

Yuan,

Wang

et al. 2023

Drive circuit is a critical part of instrumentation and control systems in nuclear reactors, and its performance directly influences the operation of nuclear reactors. However, comparing with the open circuit IGBT faults, soft faults caused by the degradation of electronic components present much slighter fluctuations to the performance of drive circuits. If the two fault modes co‐exist, traditional fault diagnosis models are prone to misclassify soft faults as the normal condition. To improve the accuracy of fault diagnosis of drive circuits, it necessitates to accurately locate the faults of drive circuits, while effectively extracting the distinguishable fault features is one of the critical factors for fault location. In this article, a fault location method combining the empirical modal decomposition (EMD) algorithm and sparse convolutional autoencoder (SCAE) is proposed. The EMD algorithm is applied to decompose the three‐phase current signals of drive circuits. An SCAE‐based feature extractor is constructed to capture high‐dimensional and sparse fault feature data with the aid of the powerful feature autonomic extraction capability of deep learning. A deep classifier is designed to locate faults in the driver circuit. A fault simulation model of the drive circuit is developed and the monitor data is collected. The effectiveness of the proposed method is validated via a real case of drive circuit in nuclear reactors.