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

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Any national defense is dependent on the efficacy of the available physical infrastructure. Whatever degrades infrastructure, structurally, physically, architecturally, or esthetically, is of interest to the nation. Climate change is now a major significant factor of interest impinging on national critical infrastructure. The devastating effects of climate change have increasing pervasiveness throughout the world. The impact on critical infrastructure is of particular interest to researchers. In consonance with ASCE-ASME's special issue on critical infrastructure protection and resilience, this paper presents a systems-modeling approach for critical infrastructure and predictions in relation to climate change agreements in COP26. The paper addresses high-level critical systems-based assessment of the social, legal, economic, and technical nuances impinging on the viability of COP26 agreements. The paper was written using a multinational collaboration approach. The specific focus of national defense is used as the backdrop for the methodology of the paper. This article is available in the ASME Digital Collection at .

This paper investigates how to improve the performance of a classifier of tightening torque in bolted joints by applying transfer learning. The procedure uses vibration measurements to extract features and to train a classifier using a Gaussian mixture model (GMM). The key to enhancing the surrogate model for torque loss detection is considering the bolted joint structures with more qualitative and quantitative knowledge as the source domain, where labels are known and the classifier is trained. After applying a domain adaptation method, it is possible to reuse this trained classifier for a target domain, i.e., a set of different limited data of bolted joint structures with unknown labels. Four different bolted joint structures are analyzed. The new experimental tests adopt a wide range of torque in the bolts to extract the features with the respective labels under safe or unsafe tightening torque. All combinations of possible source or target domains are considered in the application to demonstrate whether the method can aid the detection of the loss of tightening torque, reducing the learning steps and the training sample. A guidance list is discussed based on this population-based structural health monitoring (SHM) of bolted joint structures. This article is available in the ASME Digital Collection at .

The popularization of Industry 4.0 and its technological pillars has allowed prognostics and health management (PHM) strategies to be applied in complex systems to optimize their performance and extend their useful life by taking advantage of a digitalized, integrated environment. Due to this context, the use of digital twins and digital shadows, which are virtual representations of physical systems that provide real-time monitoring and analysis of the health and performance of the system, has been increasingly used in the application of fault detection, a key component of PHM. Taking that into consideration, this work proposes a framework for fault detection in engineering systems based on the construction and application of a digital shadow. This digital shadow is based on a digital model composed of a system of equations and a continuous, real-time communication process with a supervisory control and data acquisition (SCADA) system. The digital model is generated using monitoring data from the system under study. The proposed method was applied in two case studies, one based on synthetic data and another that uses a simulated database of an operational generating unit of a hydro-electric power plant. The method, in both case studies, was able to detect faults accurately and effectively. Besides, the method provides by-products that can be used in the future in other applications, helping with the PHM in other aspects. This article is available in the ASME Digital Collection at .

Rotating machinery has extensive usage in industrial applications, either as leading equipment (power plants) or as auxiliary equipment (oil and gas exploitation). These highly complex systems demand expensive maintenance programs due to the high costs of eventual shutdown. Consequently, critical fault diagnosis and prognosis are essential during the operation of those systems. Fault identification and classification demand robust verification of codes and calculations and a discerning validation of numerical models used for rotating machinery. Hence, verification and validation (V&V) are an essential initial service for a digital twin (DT), offering some advantages in this application. In this context, the following research question is proposed: Does V&V using DT improve data access and reduce the effort of data exchange? The following objectives are created to address the research question: perform a code verification, conduct the calculation verification, validate the models using two different validation approaches, and demonstrate easy access to asset data. For this study, two identical hydrodynamic bearings and a noncentral disk were considered, representing a laboratory experimental setup. The validation metric requirement is promisingly satisfied for the disk and bearings according to validation Approaches 1 and 2. Furthermore, validation Approach 2 generates even more successful results than approach 1. Accurate estimation and reliable interpretation of the numerical model outcomes encourage the application of DT for future fault diagnosis and prognosis. This article is available in the ASME Digital Collection at .

In any nonlinear system as complex as an urban rail transit network or metrorail network, some incidence of perturbations of its state is inevitable. These perturbations, such as natural hazards, can highly affect the networks' resilience. Increasing the ability of metrorail networks to withstand such perturbations requires robustness and vulnerability assessments as key attributes of resilience and necessary steps toward developing reliable networks. Most models developed for this purpose associate a network's failures to binary representations of the failure of its components without incorporating weight factors. Since ridership is a primary factor to define the metrorail network performance, this paper proposes a general ridership pattern, considers different failure cases, and uses a novel methodology to quantitatively measure the weighted-network resilience attributes incorporating ridership throughout the Washington, DC Metrorail as a case study. The proposed methodology has clear relationships to adjacency and link-weight matrices and defines a new expression for the weighted global network efficiency based on the sum of weights on each geodesic path. Results show that the most vulnerable stations and links hold critical positions in the network topological structure and/or bear larger amounts of ridership. For the case study, the most vulnerable components include transfer stations located in the city center as well as stations and links on the northwest section of the Red Line. The methodology presented herein provides insights for enhancing critical components during the planning and operation of a metrorail by mitigating the risks associated with failure events. This article is available in the ASME Digital Collection at .

Survival signature can be useful for the reliability assessment of critical infrastructures. However, analytical calculation and Monte Carlo Simulation (MCS) are not feasible for approximating the survival signature of large infrastructures, because of the complexity and computational demand due to the large number of components. In this case, efficient and accurate approximations are sought. In this paper we formulate the survival signature approximation problem as a missing data problem. An ensemble of artificial neural networks (ANNs) is trained on a set of survival signatures obtained by MCS. The ensemble of trained ANNs is, then, used to retrieve the missing values of the survival signature. A numerical example is worked out and recommendations are given to design the ensemble of ANNs for large-scale, real-world infrastructures. The electricity grid of Great Britain, the New England power grid (IEEE 39-Bus Case), the reduced Berlin metro system and the approximated American Power System (IEEE 118-Bus Case) are, then, eventually, analyzed as particular case studies. This article is available in the ASME Digital Collection at .

Patient-transfer robot, which can transfer a bedridden care receiver from a bed to a wheelchair or a pedestal pan and back, was not widely used due to inadequate safety and comfort. A human comfort evaluation function based on force analysis was proposed to improve the comfort of a dual-arm transfer robot. First, a human–robot mechanical model was construct by simplifying the structure of human body and the robot. Then, the internal and external forces acting on human body were calculated by the developed human–robot mechanical model. After that, a comfort evaluation function was established through mechanical analysis and a questionnaire investigation method. To assess the validity of the proposed method, first we employed the comfort evaluation function to estimate human comfort, and obtained that the comfort level is proportional to the electromyography (EMG) signal and pressure signal of human. Then we applied the function to a dual-arm patient-transfer robot to optimizing lifting points and transfer posture and found it can effectively reduce the human–robot contact force and the burden of the waist by 44.2%, improving the comfortability of the care receiver. This article is available in the ASME Digital Collection at .