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Abstract Healthcare tends to be one of the most complicated sectors, and hospitals exist at the core of healthcare activities. One of the most significant elements in hospitals is service quality level. Moreover, the dependency between factors, dynamic features, as well as objective and subjective uncertainties involved endure challenges to modern decision-making problems. Thus, in this paper, a decision-making approach is developed for hospital service quality assessment, using a Bayesian copula network based on a fuzzy rough set within neighborhood operators as a basis of that to deal with dynamic features as well as objective uncertainties. In the copula Bayesian network model, the Bayesian Network is utilized to illustrate the interrelationships between different factors graphically, while Copula is engaged in obtaining the joint probability distribution. Fuzzy rough set theory within neighborhood operators is employed for the subjective treatment of evidence from decision makers. The efficiency and practicality of the designed method are validated by an analysis of real hospital service quality in Iran. A novel framework for ranking a group of alternatives with consideration of different criteria is proposed by the combination of the Copula Bayesian Network and the extended fuzzy rough set technique. The subjective uncertainty of decision makers’ opinions is dealt with in a novel extension of fuzzy Rough set theory. The results highlighted that the proposed method has merits in reducing uncertainty and assessing the dependency between factors of complicated decision-making problems.

Abstract The two-dimensional (2D) lidar is a ranging optical sensor that can measure the cross-section of the geometric structure of the environment. We propose a robust 2D lidar simultaneous localization and mapping (SLAM) algorithm working in ambiguous environments. To improve the front-end scan-matching module’s accuracy and robustness, we propose performing degeneration analysis, line landmark tracking, and environment coverage analysis. The max-clique selection and odometer verification are introduced to increase the stability of the SLAM algorithm in an ambiguous environment. Moreover, we propose a tightly coupled framework that integrates lidar, wheel odometer, and inertial measurement unit (IMU). The framework achieves the accurate mapping in large-scale environments using a factor graph to model the multi-sensor fusion SLAM problem. The experimental results demonstrate that the proposed method achieves a highly accurate front-end scan-matching module with an error of 3.8% of the existing method. And it can run stably in ambiguous environments where the existing method will be failed. Moreover, it ccan successfully construct a map with an area of more than 250 000 square meters.

Subject to intense high-energy particle irradiation, various effects manifest within a material. Specifically, when high-energy particles collide with lattice atoms in a crystal material, a sequence of interactions is set in motion, initiating irradiation effects. Using GaAs solar cells as an example, this study investigates how potential barriers in different directions of atoms in sphalerite structures affect atomic displacement and establishes a probability model for lattice atomic displacement under proton irradiation. By combining Markov chains, changes in displacement threshold energy with different crystal orientations are described, and a damage model for cascading collision relationships that cause irradiation effects is established. Finally, the new model is compared to classical models, and differences in defects caused by proton impacts at several energies on GaAs crystals are simulated.

Abstract The existing engineering empirical life analysis models are not capable of considering the constitutive behavior of materials under contact loads; as a consequence, these methods may not be accurate to predict fatigue lives of rolling bearings. In addition, the contact stress of bearing in operation is cyclically pulsating, it also means that the bearing undergo non-symmetrical fatigue loadings. Since the mean stress has great effects on fatigue life, in this work, a novel fatigue life prediction model based on the modified SWT mean stress correction is proposed as a basis of which to estimate the fatigue life of rolling bearings, in which, takes sensitivity of materials and mean stress into account. A compensation factor is introduced to overcome the inaccurate predictions resulted from the Smith, Watson, and Topper (SWT) model that considers the mean stress effect and sensitivity while assuming the sensitivity coefficient of all materials to be 0.5. Moreover, the validation of the model is finalized by several practical experimental data and the comparison to the conventional SWT model. The results show the better performance of the proposed model, especially in the accuracy than the existing SWT model. This research will shed light on a new direction for predicting the fatigue life of rolling bearings.

The frame-to-frame scan matching algorithm is the most basic robot localization and mapping module and has a huge impact on the accuracy of localization and mapping tasks. To achieve high-precision localization and mapping, we propose a 2D lidar frame-to-frame scanning matching algorithm based on an attention mechanism called ASM (Attention-based Scan Matching). Inspired by human navigation, we use a heuristic attention selection mechanism that only considers the areas covered by the robot’s attention while ignoring other areas when performing frame-to-frame scan matching tasks to achieve a similar performance as landmark-based localization. The selected landmark is not switched to another one before it becomes invisible; thus, the ASM cannot accumulate errors during the life cycle of a landmark, and the errors will only increase when the landmark switches. Ideally, the errors accumulate every time the robot moves the distance of the lidar sensing range, so the ASM algorithm can achieve high matching accuracy. On the other hand, the number of involved data during scan matching applications is small compared to the total number of data due to the attention mechanism; as a result, the ASM algorithm has high computational efficiency. In order to prove the effectiveness of the ASM algorithm, we conducted experiments on four datasets. The experimental results show that compared to current methods, ASM can achieve higher matching accuracy and speed.

The momentum wheel is a key component of the satellite attitude control system and has a direct impact on the reliability and overall life of the satellite. The momentum wheel has the characteristics of a high reliability, long life, and complex failure mechanics, which leads to expensive maintenance and a low reliability of the test sample. Therefore, it is challenge to implement an accelerated life test. The traditional life data statistical method has great difficulty in solving the reliability analysis of the momentum wheel. A reliability calculation method based on copula function for multi-degradation is proposed. Firstly, the key factors affecting the reliability of the momentum wheel are analyzed, and the lubricant residual quantity and current are selected as the degradation quantity. Secondly, the wiener process is used to model the degradation of a single degradation quantity, and the edge distribution function of the momentum wheel reliability is obtained. Considering that the correlation between multiple degradation quantities has a non-negligible influence on the reliability analysis result, the copula function is introduced to describe the correlation, and the edge distributions are fused to obtain the joint distribution function of the momentum wheel reliability.

With the development of 3D integrated packaging technology, failure analysis is facing more and more challenges. Defect localization in a 3D package is a key step of failure analysis. The complex structure and materials of 3D package devices demand non-destructive defect localization technology for full packages. Magnetic field imaging and three-dimensional X-ray technology are not affected by package material or form. They are effective methods to realize defect localization on 3D packages. In this paper, magnetic field imaging and high-resolution three-dimensional X-ray microscopy were used to localize the open defect in a 3D package with a TSV daisy chain. A two-probe RF method in magnetic field imaging was performed to resolve isolation of the defect difficulties resulting from many different branches of TSV daisy chains. Additionally, a linear decay method was used to target sub-micron resolution at a long working distance. Multiple partition scans from a high-resolution 3D X-ray microscopy with a two-stage magnification structure were used to achieve sub-micron resolution. The open location identified by magnetic field imaging was consistent with that identified by a three-dimensional X-ray microscope. The opening was located on the top metal in the proximity of the fifth via. Physical failure analysis revealed the presence of a crack in the top metal at the opening location.

Hard-and-software integrated systems such as command and control systems (C4ISR systems) are typical systems that are comprised of both software and hardware, the failures of such devices result from complicated common cause failures and common (or shared) signals that make classical reliability analysis methods will be not applicable. To this end, this paper applies the Goal-Oriented (GO) methodology to detailed analyze the reliability of a C4ISR system. The reliability as well as the failure probability of the C4ISR system, are reached based on the GO model constructed. At the component level, the reliability of units of the C4ISR system is computed. Importance analysis of failures of such a system is completed by the qualitative analysis capability of the GO model, by which critical failures of hardware failures like communication module failures and motherboard module failures as well as software failures like network module application software failures and decompression module software failures are ascertained. This method of this paper contributes to the reliability analysis of all hard-and-software integrated systems.

In this article, a new method for fatigue reliability analysis of crack growth life based on the maximum entropy theory and a long crack propagation model is proposed. A modified generalized passivation-lancet model for long fatigue crack propagation rate is presented with explicit physical meaning. Experimental results for turbine disk alloy ZSGH4169 under different strain ratios and temperatures (at 650°C and room temperature) are used to verify the applicability of the new model. Results show that predictions by the proposed model are almost identical to the experimental data. The presented model is better than the other three models to reflect the rapid propagation characteristics of the crack. In order to perform fatigue reliability estimation, the probabilities of failure are calculated using the maximum entropy theory based on the fatigue crack growth life that derived from the proposed modified crack propagation model and the above existing three models. Results have shown that maximum entropy theory is very apt for fatigue reliability analysis of turbine disk under different loading conditions with a limited number of samples because it does not need any distribution assumptions for random variables. The effectiveness and accuracy of the combination of fatigue crack propagation models and maximum entropy method for fatigue reliability analysis are demonstrated with examples.

Fatigue damage accumulation theory is one of the core contents in structure fatigue strength design and life prediction. Among them, the nonlinear damage model can overcome the shortcomings of the linear damage model, which takes the loading sequence effect into account. Besides, the loading interaction cannot be ignored for its profound influence in damage accumulation behavior. In the paper, some commonly-used methods of the linear and nonlinear fatigue damage accumulation theory are investigated. In particular, a modified nonlinear fatigue damage accumulation model which considers the effects of loading sequences as well as loading interactions on fatigue life is developed, and a load interaction parameter is obtained by analyzing damage models which assumes that the load logarithm ratio between adjacent stress levels can characterize this phenomenon. Finally, the modified model is employed to predict the fatigue life of high pressure turbine disc. Moreover, comparison is made between the experimental data as well as the predicted lives using the Miner’s rule, the Ye’s model, and the modified model.

This paper presents a novel algorithm for structural reliability analysis based on the finite step length and Armijo line search to remove the drawbacks of the Hasofer−Lind and Rakwitz−Fiessler (HL-RF) algorithm that may be subjected to non-convergence in the first-order reliability method (FORM). Initially, the sensitivity factor with finite step length is introduced for preventing the iterative process of the algorithm from entering a periodic loop. Subsequently, an optimization method based on the sufficient descent condition with the Armijo line search technique is proposed. With that, the initial step length and adjusting coefficient are optimized to enhance the applicability of the algorithm emphatically for highly nonlinear functions. A comparison analysis is carried out between the proposed algorithm and existing FORM-based algorithms to validate the robustness and efficiency of the proposed algorithm. The results of this demonstrate that the proposed algorithm is superior to the HL-RF algorithm in terms of robustness and surpass the other existing FORM-based algorithms in connection to efficiency.

In this paper the combinations of maximum entropy method and Bayesian inference for reliability assessment of deteriorating system is proposed. Due to various uncertainties, less data and incomplete information, system parameters usually cannot be determined precisely. These uncertainty parameters can be modeled by fuzzy sets theory and the Bayesian inference which have been proved to be useful for deteriorating systems under small sample sizes. The maximum entropy approach can be used to calculate the maximum entropy density function of uncertainty parameters more accurately for it does not need any additional information and assumptions. Finally, two optimization models are presented which can be used to determine the lower and upper bounds of systems probability of failure under vague environment conditions. Two numerical examples are investigated to demonstrate the proposed method.

Fatigue failure evolution is a process of damage accumulation under continued stresses and forces. The mechanical component is always subjected to various loadings and the lifespan is mainly governed by fatigue. The low cycle fatigue (LCF) is a key failure mode of many components. In order to estimate the LCF life under multiaxial loadings in practical design, a modified model is proposed, based on the Fatemi-Socie (FS) and Smith-Watson-Topper (SWT) models, which considers the effects of shear and tensile behaviours. Then a novel judgment criterion is presented to distinguish the mixed-mode loadings and the procedures to employ the proposed model are also presented. Furthermore, two types of materials (TC4 and GH4169) and comparisons with the FS, Wang-Brown (WB) and redefined SWT (Re-SWT) models are employed to verify the accuracy and effectiveness of the proposed model, which has shown more reasonable predictions than the other models.

Corrosion is recognized as one of the most important degradation mechanisms that affect the long-term reliability and integrity of metallic structures. Studying the structural reliability with pitting corrosion damage is useful for risk control and safety operation for the corroded structure. This paper proposed a structure corrosion reliability analysis approach based on the physics-based failure model of pitting corrosion, where the states of pitting growth, pit-to-crack, and cracking propagation are included in failure model. Then different probabilistic analysis methods such as Monte-Carlo Simulation (MCS), First-Order Reliability Method (FORM), Second-Order Reliability Method (SORM), and response surface method are employed to calculate the reliability. At last, an example is presented to demonstrate the capability of the proposed structural reliability model and calculating methods for structural corrosion failure analysis.

This paper introduces a new phenomenological cumulative damage rule to predict damage and fatigue life under variable amplitude loading. The rule combines a residual S-N curve approach and a material memory concept to describe the damage accumulation behavior. The residual S-N curve slope is regarded as a variable with respect to the loading history. The change in slope is then used as a damage measure and quantified by a material memory degeneration parameter. This model improves the traditional linear damage rule by taking the load-level dependence and loading sequence effect into account, which still preserves its superiority. A series of non-uniform fatigue loading protocols are used to demonstrate the effectiveness of the proposed model. The prediction results using the proposed model are more accurate than those using three popular damage models. Moreover, several common characteristics and fundamental properties of the chosen fatigue models are extracted and discussed.

For large scale systems, as a hierarchical multilevel decomposed design optimization method, analytical target cascading coordinates the inconsistency between the assigned targets and response in each level by a weighted-sum formulation. To avoid the problems associated with the weighting coefficients, single objective functions in the hierarchical design optimization are formulated by a bounded target cascading method in this paper. In the BTC method, a single objective optimization problem is formulated in the system level, and two kinds of coordination constraints are added: one is bound constraint for the design points based on the response from each subsystem level and the other is linear equality constraint for the common variables based on their sensitivities with respect to each subsystem. In each subsystem level, the deviation with target for design point is minimized in the objective function, and the common variables are constrained by target bounds. Therefore, in the BTC method, the targets are coordinated based on the optimization iteration information in the hierarchical design problem and the performance of the subsystems, and BTC method will converge to the global optimum efficiently. Finally, comparisons of the results from BTC method and the weighted-sum analytical target cascading method are presented and discussed.

The distributed strategy of Collaborative Optimization (CO) is suitable for large-scale engineering systems. However, it is hard for CO to converge when there is a high level coupled dimension. Furthermore, the discipline objectives cannot be considered in each discipline optimization problem. In this paper, one large-scale systems control strategy, the interaction prediction method (IPM), is introduced to enhance CO. IPM is utilized for controlling subsystems and coordinating the produce process in large-scale systems originally. We combine the strategy of IPM with CO and propose the Interaction Prediction Optimization (IPO) method to solve MDO problems. As a hierarchical strategy, there are a system level and a subsystem level in IPO. The interaction design variables (including shared design variables and linking design variables) are operated at the system level and assigned to the subsystem level as design parameters. Each discipline objective is considered and optimized at the subsystem level simultaneously. The values of design variables are transported between system level and subsystem level. The compatibility constraints are replaced with the enhanced compatibility constraints to reduce the dimension of design variables in compatibility constraints. Two examples are presented to show the potential application of IPO for MDO.

Many structures are subjected to variable amplitude loading in engineering practice. The foundation of fatigue life prediction under variable amplitude loading is how to deal with the fatigue damage accumulation. A nonlinear fatigue damage accumulation model to consider the effects of load sequences was proposed in earlier literature, but the model cannot consider the load interaction effects, and sometimes it makes a major error. A modified nonlinear damage accumulation model is proposed in this paper to account for the load interaction effects. Experimental data of two metallic materials are used to validate the proposed model. The agreement between the model prediction and experimental data is observed, and the predictions by proposed model are more possibly in accordance with experimental data than that by primary model and Miner’s rule. Comparison between the predicted cumulative damage by the proposed model and an existing model shows that the proposed model predictions can meet the accuracy requirement of the engineering project and it can be used to predict the fatigue life of welded aluminum alloy joint of Electric Multiple Units (EMU); meanwhile, the accuracy of approximation can be obtained from the proposed model though more simple computing process and less material parameters calling for extensive testing than the existing model.

Because solder joint interconnections are the weaknesses of microelectronic packaging, their reliability has great influence on the reliability of the entire packaging structure. Based on an accelerated life test the reliability assessment and life prediction of lead-free solder joints using Weibull distribution are investigated. The type-I interval censored lifetime data were collected from a thermal cycling test, which was implemented on microelectronic packaging with lead-free ball grid array (BGA) and fine-pitch ball grid array (FBGA) interconnection structures. The number of cycles to failure of lead-free solder joints is predicted by using a modified Engelmaier fatigue life model and a type-I censored data processing method. Then, the Pan model is employed to calculate the acceleration factor of this test. A comparison of life predictions between the proposed method and the ones calculated directly by Matlab and Minitab is conducted to demonstrate the practicability and effectiveness of the proposed method. At last, failure analysis and microstructure evolution of lead-free solders are carried out to provide useful guidance for the regular maintenance, replacement of substructure, and subsequent processing of electronic products.

Diesel engine is a complex electromechanical system which must operate reliably in harsh working environments. Reliability analysis and prediction play an important role during the design and development of diesel engines. However, in the traditional reliability methods, the analytical result obtained from the conventional failure mode, effects, and criticality analysis (FMECA) is not sufficient, which not only increases the workload of designers in charge of reliability, but also prolongs the product delivery time. This paper performs an in-depth reliability analysis with an emphasis on predicting the lifetime of diesel engine's turbocharger, in which the failure mode and the information of criticality provided by FMECA are fully utilized to carry out the reliability predictions. Meanwhile, to ensure the reliability prediction quality, this paper takes into account the expert knowledge and provides a possibility-based prediction model, in which the fuzzy analytic hierarchy process and the fuzzy comprehensive evaluation are combined to assess the criticality of the FMECA.

Various uncertainties are inevitable in complex engineered systems and must be carefully treated in design activities. Reliability-Based Multidisciplinary Design Optimization (RBMDO) has been receiving increasing attention in the past decades to facilitate designing fully coupled systems but also achieving a desired reliability considering uncertainty. In this paper, a new formulation of multidisciplinary design optimization, namely RFCDV (random/fuzzy/continuous/discrete variables) Multidisciplinary Design Optimization (RFCDV-MDO), is developed within the framework of Sequential Optimization and Reliability Assessment (SORA) to deal with multidisciplinary design problems in which both aleatory and epistemic uncertainties are present. In addition, a hybrid discrete-continuous algorithm is put forth to efficiently solve problems where both discrete and continuous design variables exist. The effectiveness and computational efficiency of the proposed method are demonstrated via a mathematical problem and a pressure vessel design problem.