ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

This article is available in the ASME Digital Collection at .

Planetary gearbox has been widely applied in the mechanical transmission system, and the failure types of planetary gearbox are more and more diversified. The conventional fault diagnosis methods focus on identifying the faults in the fault library, but ignored the faults outside the fault library. However, it is impossible to build a fault library for all failure types. Targeting the problem of identifying the faults outside the fault library, a hierarchical fault diagnosis method for planetary gearbox with shift-invariant dictionary and orthogonal matching pursuit with adaptive noise (OMPAN) is proposed in this paper. By k-means singular value decomposition (K-SVD) dictionary learning method and shift-invariant strategy, a shift-invariant dictionary is constructed so that the normal modulation components of signals can be completed decomposed. OMPAN algorithm is proposed, which uses the white Gaussian noise to improve the solution method of the orthogonal matching pursuit (OMP) algorithm so that it can separate the modulation components in the signal more accurately. The fault feature extraction is developed via shift-invariant dictionary and OMPAN. A hierarchical classifier is proposed with three subclassifiers so that both the faults in the fault library and the faults outside the fault library are identified. The effectiveness of the proposed hierarchical fault diagnosis method is validated by experiments. Result show that the proposed shift-invariant dictionary and OMPAN method has achieved a superior performance in highlighting fault features compared with other two sparse decomposition methods. The proposed hierarchical fault diagnosis approach has achieved a good performance both in classification of the faults in the fault library and identification of the faults outside the fault library. This article is available in the ASME Digital Collection at .

Due to the nature of additive manufacturing (AM), design and manufacturing are deeply coupled. Toolpaths are defined based on the part geometry, and in turn, these toolpaths can influence the bonding between adjacent toolpaths, especially for fused filament fabrication (FFF) process. In FFF, bonding between adjacent rasters is critical to the FFF part mechanical strength. The bonding is driven by factors such as thermal history and a deposition strategy, which are dictated by the geometry of a part and process parameters. In this research, a data-driven physics-based methodology is proposed to predict the mechanical properties of FFF parts using Bayesian inference. In the proposed methodology, geometry and variance in process parameters are used to quantify uncertainties in the mechanical properties. Empirical data derived from the mesostructure of specimens are utilized to generate priors of predictors. Hamilton Monte Carlo is then used to sample the posterior distribution. Subsequently, random draw from posterior predictive distribution is performed, and the results are validated against empirical data to establish the accuracy of the proposed methodology. The proposed methodology can provide more accurate prediction of the mechanical properties by considering the influence of geometry, process parameters and uncertainty in AM process. This article is available in the ASME Digital Collection at .

Surrogate models have become increasingly essential for replacing simulation models in additive manufacturing (AM) process analysis and design, particularly for assessing the impact of microstructural variations and process imperfections (aleatory uncertainty). However, these surrogate models can introduce predictive errors, introducing epistemic uncertainty. The challenge arises when dealing with image input data, which is inherently high-dimensional, making it challenging to apply existing uncertainty quantification (UQ) techniques effectively. To address this challenge, this study develops a new UQ methodology based on an existing concept of combining convolutional neural network (CNN) and Gaussian process (GP) regression (GPR). This CNN-GP method converts both numerical and image inputs into a unified, larger-sized image dataset, enabling direct dimension reduction with CNN. Subsequently, GPR constructs the surrogate model, not only providing predictions but also quantifying the associated model uncertainty. This approach ensures that the surrogate model considers both input-related aleatory uncertainty and model-related epistemic uncertainty when it is used for prediction, enhancing confidence in image-based AM simulations and informed decision-making. Three examples validate the high accuracy and effectiveness of the proposed method. This article is available in the ASME Digital Collection at .

With the blooming of the electric vehicle market and the advancement in the lithium-ion battery industry, silicon anode has shown great potential for the next-generation battery. Using the state-of-the-art additive manufacturing technique (three-dimensional (3D) holographic lithography), researchers have demonstrated that silicon anode can be fabricated as a three-dimensional bicontinuous porous microstructure. However, the volume fluctuation of the silicon anode caused by lithiation during the discharging process causes continuous capacity decay and poor cycling life. Besides, uncertainties are inherent in the manufacturing and usage processes, making it crucial to systematically consider them in the silicon anode design to improve its performance and reliability. To fill the gap between current silicon anode research and future industrial need, this study established a digital twin to investigate the optimal design for silicon anode under the uncertainties of additive manufacturing and battery usage. This study started with developing multiphysics finite element models of the silicon anode lithiation process to investigate the volume fluctuation of silicon. Then, surrogate models were built based on the results from the finite element models to reduce computational cost. The reliability-based design optimization (RBDO) was employed to find the best design point for the silicon anode, in which an outer optimization loop maximized the objective function and an inner loop dedicated to reliability analysis. Finally, the Pareto optimal front of the silicon anode designs was obtained and validated, which shows over 10% improvements in the silicon anode's total capacity and rate capability. This article is available in the ASME Digital Collection at .

The transition to Industry 5.0 begins with the integration of the human aspect into Industry 4.0 technologies. Industry 5.0 is a human-centric design approach that aims to overcome the issues raised by Industry 4.0 and involves collaborating both with humans and robots in a shared working environment. The new idea demonstrates a great connection between technology and people, or “soft” sectors. At this point, the idea of a digital twin (DT), a novel technological innovation, appears. The digital twin is a newly developed technology that is essential for digital transformation and intelligent updates. The fundamental basis of this concept involves the amalgamation of artificial intelligence (AI) with the notion of digital twins, which refer to virtual renditions of tangible entities, systems, or procedures. Therefore, this article focuses on digital twins and the innovative concept of human digital twins (HDTs), with particular emphasis on the technological tools of AI in the usage of mentioned technology. Also, this article conducts a comprehensive political (P), economic (E), social (S), technological (T), legal (L), and environmental (E) (PESTLE) analysis of Industry 5.0, while specifically delving into the concepts of digital twin and human digital twin. This article is available in the ASME Digital Collection at .

Multifidelity surrogate modeling offers a cost-effective approach to reducing extensive evaluations of expensive physics-based simulations for reliability prediction. However, considering spatial uncertainties in multifidelity surrogate modeling remains extremely challenging due to the curse of dimensionality. To address this challenge, this paper introduces a deep learning-based multifidelity surrogate modeling approach that fuses multifidelity datasets for high-dimensional reliability analysis of complex structures. It first involves a heterogeneous dimension transformation approach to bridge the gap in terms of input format between the low-fidelity and high-fidelity domains. Then, an explainable deep convolutional dimension-reduction network (ConvDR) is proposed to effectively reduce the dimensionality of the structural reliability problems. To obtain a meaningful low-dimensional space, a new knowledge reasoning-based loss regularization mechanism is integrated with the covariance matrix adaptation evolution strategy (CMA-ES) to encourage an unbiased linear pattern in the latent space for reliability prediction. Then, the high-fidelity data can be utilized for bias modeling using Gaussian process (GP) regression. Finally, Monte Carlo simulation (MCS) is employed for the propagation of high-dimensional spatial uncertainties. Two structural examples are utilized to validate the effectiveness of the proposed method. This article is available in the ASME Digital Collection at .

An approximate efficient stochastic dynamics technique is developed for determining response statistics of linear systems with frequency-dependent parameters, which are used for modeling wave propagation through rigid porous media subject to stochastic excitation. This is done in conjunction with a filter approximation of the system frequency response function. The technique exhibits the following advantages compared to alternative solution treatments in the literature. First, relying on an input–output relationship in the frequency domain, the response power spectrum matrix is integrated analytically for determining the stationary response covariance matrix, at zero computational cost. Second, the proposed filter approximation facilitates a state-variable formulation of the governing stochastic differential equations in the time domain. This yields a coupled system of deterministic differential equations to be solved numerically for the response covariance matrix. Thus, the nonstationary (transient) response covariance can be computed in the time domain at a relatively low computational cost. Various numerical examples are considered for demonstrating the accuracy and computational efficiency of the herein developed technique. Comparisons with pertinent Monte Carlo simulation (MCS) data are included as well. This article is available in the ASME Digital Collection at .

This work proposes a method using a long short-term memory neural network as a diagnostic tool to detect wind turbine rotor mass imbalance. The method uses the synthetic minority oversampling technique for data augmentation in an unbalanced dataset. For this purpose, a 1.5 MW three-bladed wind turbine model was simulated at Turbsim, FAST, and Matlab Simulink to generate rotor speed data for different scenarios, simulating different wind speeds and creating a mass imbalance by changing the density of the blades in the software. Features extraction and power spectral density were also used to improve the Neural Network results. The results were compared to nine different classifiers with four different combinations of datasets and demonstrated that the technique is promising for mass imbalance detection. This article is available in the ASME Digital Collection at .

The modern idea of risk-informed decision-making (RIDM) is here critically examined for all existing, advanced and, generation-IV nuclear reactor systems. Motivated by the predictive difficulties of probabilistic risk assessment (PRA) in regard to occurred accidents, it is evident that the real (not hypothetical) consequences of nuclear core damage accidents that must be considered and quantified are the financial losses, infrastructure damages, societal disruptions, and adverse political policies, and not solely the traditional exceedance of regulated radiation release or public exposure limits. With this perspective, a new dynamic analysis is presented for estimating the probability of core damage due to extended loss of power and cooling in a modern nuclear reactor, giving results different from current standardized PRA/PSA analyses. Using existing data, we approach the multitude of different events in a new way: whatever the initial event in the finite event set {external flood, fire, hurricane, ice storm, typhoon, earthquake, cyber attack…}, the fundamental concerns are the consequent loss and nonrestoration of power, and the inadequate cooling of the core. The present proposed quantitative evaluation uses applicable and fully “exchangeable” severe event data for nuclear and nonnuclear systems, including active and passive emergency back-up systems for a wide range of power losses that lead to widespread damage and societal disruption. While not design-specific, this new independent “order-of-magnitude” estimate for the probability of core damage is some two to two hundred times larger than that shown or reported in recent modern and formal PSA/PRA for licensing submissions using generic failure rate data rather than actual severe event data. This new finding suggests greater uncertainties exist than presently assumed for risk-informed decision-making (RIDM), and points to the need for a major reconsideration and updating of risk assessment and regulatory risk-informed approaches for nuclear plant safety evaluation. This article is available in the ASME Digital Collection at .

In the early-stage development of sheet metal parts, key design properties of new structures must be specified. As these decisions are made under significant uncertainty regarding drawing configuration changes, they sometimes result in the development of new parts that, at a later design stage, will not be drawable. As a result, there is a need to increase the certainty of experience-driven drawing configuration decisions. Complementing this process with a global sensitivity analysis (GSA) can provide insight into the impact of various changes in drawing configurations on drawability, unveiling cost-effective strategies to ensure the drawability of new parts. However, when quantitative global sensitivity approaches, such as Sobol's method, are utilized, the computational requirements for obtaining Sobol indices can become prohibitive even for small application problems. To circumvent computational limitations, we evaluate the applicability of different surrogate models engaged in computing global design variable sensitivities for the drawability assessment of a deep-drawn component. Here, we show in an exemplary application problem, that both a standard Gaussian process regression (GPR) model and an ensemble model can provide commendable results at a fraction of the computational cost. We compare our surrogate models to existing approaches in the field. Furthermore, by comparing drawability measures we show that the error introduced by the surrogate models is of the same order of magnitude as that from the choice of drawability measure. In consequence, our surrogate models can improve the cost-effective development of a component in the early design phase. This article is available in the ASME Digital Collection at .

Optimal mass transport (OMT) is an optimization problem that determines a method of moving mass or value from one function to another in the most efficiently and optimally. Using the ideas of OMT, this technical note aims to formulate and solve an OMT model for railway track safety. The inputs for the analysis include (a) geometry data, (b) rail defect, (c) subsurface condition, and (d) tonnage. The technical note only presents the steps in achieving a complete OMT analysis. Therefore, the idea of the note is to serve as an illustration of the effectiveness of OMT in railway engineering applications. This article is available in the ASME Digital Collection at .

As the height of telescoping-boom aerial lifts increase, the severity of tip-over accidents obviously increases. To reduce the probability of tip-over accidents, manufacturers use countermeasures such as outriggers and wheel axels that expand in width to provide a more stable base, counterweights to offset the moments generated by the telescoping boom, and controllers that limit the machine configurations to within stable envelopes. The size of stability margins is determined by industry standards that set the approved load capacity of the machine to less than the load that would induce tip-over. This paper investigates the effectiveness of such load-based safety margins for very tall aerial lifts with telescoping-booms. The results indicate that the industry standards result in both inconsistent and often low safety margins. This article is available in the ASME Digital Collection at .