Suhail, SabahMalik, Saif Ur RehmanJurdak, RajaHussain, Rasheed...
16页
查看更多>>摘要：The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate critical infrastructures' operational behaviour and security without affecting the operation of live systems. In this regard, Digital Twins (DTs) provide actionable insights through monitoring, simulating, predicting, and optimizing the state of CPSs. Through the use cases, including system testing and training, detecting system misconfigurations, and security testing, DTs strengthen the security of CPSs throughout the product lifecycle. However, such benefits of DTs depend on an assumption about data integrity and security. Data trustworthiness becomes more critical while integrating multiple components among different DTs owned by various stakeholders to provide an aggregated view of the complex physical system. This article envisions a blockchain-based DT framework as Trusted Twins for Securing Cyber-Physical Systems (TTS-CPS). With the automotive industry as a CPS use case, we demonstrate the viability of the TTS-CPS framework through a proof of concept. To utilize reliable system specification data for building the process knowledge of DTs, we ensure the trustworthiness of data-generating sources through Integrity Checking Mechanisms (ICMs). Additionally, Safety and Security (S&S) rules evaluated during simulation are stored and retrieved from the blockchain, thereby establishing more understanding and confidence in the decisions made by the underlying systems. Finally, we perform formal verification of the TTS-CPS. (c) 2022 The Author(s). Published by Elsevier B.V. CC_BY_4.0
查看更多>>摘要：With the advancement of deep learning, the newly proposed neural networks are growing increasingly complicated to achieve great performance. In this context, we propose a simple but effective neural network called MiniCrack for narrow crack detection. We also propose a lightweight version, MiniCrack-Light, to adapt to scenarios with limited computing resources. MiniCrack and MiniCrack-Light outperform the current state-of-the-art neural networks on all three challenging testing data sets with fewer parameters and achieving stronger robustness. PixelShuffle and PixelUnshuffle designed for image super-resolution are successfully used to the field of image segmentation, which effectively alleviates the problems caused by pooling.
查看更多>>摘要：Modern manufacturing networks consist of cyber-physical systems (CPS) which offer an array of interesting capabilities, ranging from local computation over data generation to communication capabilities. As traditional control approaches fail to fully leverage these capabilities, the last decade has seen a renewed interest in distributed control approaches based on autonomous entities. In this article, we study the synergistic potentials of autonomous control and machine learning in a job-shop setting, addressing challenges of modern manufacturing such as market fluctuation and process time variance, thus leveraging the potentials of CPS in order to flexibly configure manufacturing networks and achieve cost-minimal production. We utilize a multi-agent based discrete-event simulation to compare this novel approach to a traditional heuristic, underlining the potentials and advantages of data-driven control approaches. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：As an important part of the Design for X tools, Design for Quality (DFQ) is used to reduce cost and improve quality of products while maintaining reliability in preliminary design phase. As a powerful tool to reduce and eliminate possible failures, failure mode and effects analysis (FMEA) is broadly applied in detail design phase. However, scholars have criticized the traditional FMEA model for several shortages. In the past decades, nearly all the FMEA methods have been presented to heighten the rationality of ranking results by considering the risk factors (severity (S), occurrence (O) and detection (D)) simultaneously. The simultaneous analysis of risk factors (RFs) may result in the ignorance of impact on failure modes from extreme RFs. In fact, different combinations of RFs may obtain more comprehensive risk information about failure modes. Thus, a novel FMEA classification method is proposed by combining risk factors in pairs (i.e., S&O, S& D and O&D) to conduct risk assessment which can avoid interaction effect caused by simultaneous analysis of risk factors. Specifically, the fuzzy adaptive resonance theory is used to conduct failure modes classification based on the assessment results of S&O, S&D and O&D obtained by the grey relational analysis. Finally, a real case study, i.e., the final assembly process of spark plugs, from an automotive manufacturer in China is adopted to clarify the advantages of the proposed method. (c) 2022 Elsevier B.V. All rights reserved.
查看更多>>摘要：Risk assessment in projects requires the integration of various information on project characteristics as well as external and internal sources of uncertainty and is based on assumptions about future and project vulnerability. Complexity is a major source of uncertainty that decreases the predictability of project outputs. In this research, the aim was to develop a decision-support tool that can estimate the level of risk and required contingency in a project by assessment of complexity factors as well as contextual information such as contract conditions and mitigation strategies. A process model and a tool were developed using the data of 11 mega construction projects. The tool was tested on a real project, and promising results were obtained about its usability. The tool has the potential to support decision-making during bidding in construction projects with its visualization and prediction features. On the other hand, as a limited number of cases and experts were involved in this study, findings on its performance cannot be generalized. The identified complexity and risk factors, proposed process model, and visual representations may help the development of similar decision-support tools according to different company needs. (c) 2022 Elsevier B.V. All rights reserved.
Schwartz, SebastienJimenez, Juan Jose MonteroVingerhoeds, RobSalaun, Michel...
12页
查看更多>>摘要：Predictive maintenance techniques attempt to trigger a maintenance intervention at the right moment by estimating the life expectation. Predictive maintenance is increasingly implemented by automated approaches able to perform diagnostics and prognostics. The main part of recent research in these approaches is focused in machine learning structures whose reasoning is implicit and cannot be easily explained. This poses a problem for their implementation in highly constrained area such as aeronautics. To overcome this constraint, explicit reasoning approaches such as the Similarity-Based Model (SBM) can be implemented. The SBM has been widely used for fault diagnostics and the remaining useful life (RUL) estimation, but the development of SBM includes tasks that often rely on high skilled experts. For instance, data reduction techniques required for SBM are often performed by experts judgment whose outcomes are not always consistent. The produced features from these techniques are used to build the Health Index that can be used to create the degradation trends that serve as a reference for the SBM. To overcome these difficulties, an automatic and unsupervised approach based on the Kernel Principal Component Analysis is proposed to enhance the Health Index creation. It preserves as much of the sensor information as possible improving the similarity-based RUL estimation. Additionally, when estimating the RUL of a system, the most similar degradation trends stored in the SBM library are used to compute individual RULs, the final RUL is obtained by a fusion rule technique that combines all these individual RULs into a consolidated value. For the fusion rule techniques, a self-adaptive method that does not rely on human expertize is proposed. This fusion rule can benefit of the accumulated knowledge over the SBM operation. This unsupervised approach to develop a SBM is validated with promising results against an equivalent and supervised algorithm that came out best in the 2008 prognostic health management challenge. (C) 2022 Elsevier B.V. All rights reserved.
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