Increasing the modularity of system architectures is generally accepted as a good design principle in engineering. In this paper, we explore whether modularity comes at the expense of robustness. To that end, we model three engineering systems as networks and measure the relation between modularity and robustness to random failures. We produced four types of network models of systems—component-component, component-function, component-parameter, and function-parameter—to further test the relation of robustness to the type of system representation, architectural or behavioral. The results show that higher modularity is correlated with lower robustness (p < 0.001) and that the estimated modularity of the system can depend on the type of system representation. The implication is that there is a tradeoff between modularity and robustness, meaning that increasing modularity might not be appropriate for systems for which robustness is critical and modularity estimates differ significantly between the types of system representation.
Recent advances in early stage failure analysis approaches have introduced behavioral network analysis (BNA), which applies a network-based model of a complex engineered system to detect the system-level effect of ‘local’ failures of design variables and parameters. Previous work has shown that changes in microscale network metrics can signify system-level performance degradation. This article introduces a new insight into the influence of the community structure of the behavioral network on the failure tolerance of the system through the role of bridging nodes. Bridging nodes connect a community of nodes in a system to one or more nodes or communities outside of the community. In a study of forty systems, it is found that bridging nodes, under attack, are associated with significantly larger system-level behavioral degradation than non-bridging nodes. This finding indicates that the modularity of the behavioral network could be key to understanding the failure tolerance of the system and that parameters associated with bridging nodes between modules could play a vital role in system degradation.
Managing and referencing design knowledge is a critical activity in the design process. However, reliably retrieving useful knowledge can be a frustrating experience for users of knowledge management systems due to inherent limitations of standard keyword-based searches. In this research, we consider the task of retrieving relevant lessons learned from the NASA Lessons Learned Information System (LLIS). To this end, we apply a state-of-the-art natural language processing (NLP) technique for information retrieval (IR): semantic search with sentence-BERT, which is a modification of a Bidirectional Encoder Representations from Transformers (BERT) model that uses siamese and triplet network architectures to obtain semantically meaningful sentence embeddings. While the pre-trained sBERT model performs well out-of-the-box, we further fine-tune the model on data from the LLIS so that it learns on design engineering-relevant vocabulary. We quantify the improvement in query results using both standard sBERT and fine-tuned sBERT over a keyword search. Our use case throughout the paper is to use queries related to specific requirements from a NASA project. Fine tuning the sBERT model on LLIS data yields a mean average precision (MAP) of 0.807 on queries based on information needs from a real NASA project. Results indicate that applying state-of-the-art natural language processing techniques, especially when fine-tuned using engineering data, to design information retrieval tasks shows significant promise in modernizing design knowledge management systems.
Increasing the modularity of system architectures is generally accepted as a good design principle in engineering. In this paper, we explore whether modularity comes at the expense of robustness. To that end, we model three engineering systems as networks and measure the relation between modularity and robustness to random failures. We produced four types of network models of systems — component, component-function, component-parameter, and function-parameter — to further test the relation of robustness to the type of system representation, architectural or behavioral. The results show that higher modularity is correlated with lower robustness (p < 0.001) and that the estimated modularity of the system can depend on the type of system representation. The implication is that there is a trade-off between modularity and robustness, meaning that increasing modularity might not be appropriate for systems for which robustness is critical and for those whose modularity estimate differs largely between each type of system representation.
Conventional failure analysis ignores a growing challenge in the responsible implementation of novel technologies into engineered systems - unintended consequences, which impact the engineered system itself and other systems including social and environmental systems. In this paper, a theory for unintended consequences is developed. The paper proposes a new definition of unintended consequences as behaviors that are not intentionally designed-into an engineered system yet occur even when a system is operating nominally, that is, not in a failure state as conventionally understood. It is argued that the primary cause for this difference is the bounded rationality of human designers. The formation of unintended consequences is modeled with system dynamics, using a specific real-world example, and bifurcation analysis. The paper develops propositions to guide research in the development of new design methods that could mitigate or control the occurrence and impact of unintended consequences. The end goal of the research is to create a new class of failure analysis tools to manage unintended consequences responsibly to facilitate engineering design for a more sustainable future.
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