Mechanical Engineering Research Works
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- ItemConstruction and Verification of a Bayesian Network for Third-Party Excavation Risk Assessment (BaNTERA)(Probabilistic Safety Assessment and Management (PSAM16), 2022-06-26) Schell, Colin A.; Ruiz-Tagle, Andres; Lewis, Austin D.; Groth, Katrina M.; Groth, Katrina M.According to the Pipeline and Hazardous Material Safety Administration (PHMSA), thirdparty damage is a leading cause of natural gas pipeline accidents. Although the risk of third-party damage has been widely studied in the literature, current models do not capture a sufficiently comprehensive set of up-to-date root cause factors and their dependencies. This limits their ability to achieve an accurate risk assessment that can be traced to meaningful elements of an excavation. This paper presents the construction, verification, and validation of a probabilistic Bayesian network model for third-party excavation risk assessment, BaNTERA. The model was constructed and its performance verified using the best available industry data and previous models from multiple sources. Historical industry data and nationwide statistics were compared with BaNTERA’s damage rate predictions to validate the model. The result of this work is a comprehensive risk model for the third-party damage problem in natural gas pipelines.
- ItemIdentifying Human Failure Events (HFEs) for External Hazard Probabilistic Risk Assessment(2022-06) Al-Douri, Ahmad; Levine, Camille S; Groth, Katrina M; Groth, Katrina MIn recent years, several advancements in nuclear power plant (NPP) probabilistic risk assessment (PRA) have been driven by increased understanding of external hazards, plant response, and uncertainties. However, major sources of uncertainty associated with external hazard PRA remain. One source discussed in this study is the close coupling of physical impacts on plants and overall plant risk under hazard events due to the significant human actions that are carried out to enable plant response and recovery from natural hazards events. This makes human reliability and human-plant interactions important elements in to consider in enhancing PRA to address external hazards. One of the challenges in considering human responses is that most existing human reliability analysis (HRA) models, such as SPAR-H and THERP, were not developed for assessing ex-control room actions and hazard response. To support this new scope for HRA, HRA models will need to be developed or modified to support identification of human activities, causal factors, and uncertainties inherent in external hazard response, thereby providing insights regarding event timing and physical event conditions as they relate to human performance. In this study, the first step of such work is performed by identifying human failure events (HFEs) for human response to flooding hazards. These HFEs are human actions or inactions that are involved in human response to flooding hazards and could contribute to the loss of a critical function for the plant in the scenario being examined. Several resources are used to identify these HFEs, including flooding reports from the Nuclear Regulatory Commission (e.g. NUREG/CR-7256: Effects of Environmental Conditions on Manual Actions for Flood Protection and Mitigation), interviews with experienced PRA and HRA analysts, and tabletop walkdowns of flooding scenarios with a project team. Also, task decomposition analyses using the cognitive-based Phoenix HRA model are also used to identify HFEs. This paper will discuss early results of these analyses.
- ItemLearnable Wavelet Scattering Networks: Applications to Fault Diagnosis of Analog Circuits and Rotating Machinery(MDPI, 2022-02-02) Khemani, Varun; Azarian, Michael H.; Pecht, Michael G.Analog circuits are a critical part of industrial electronics and systems. Estimates in the literature show that, even though analog circuits comprise less than 20% of all circuits, they are responsible for more than 80% of faults. Hence, analog circuit fault diagnosis and isolation can be a valuable means of ensuring the reliability of circuits. This paper introduces a novel technique of learning time–frequency representations, using learnable wavelet scattering networks, for the fault diagnosis of circuits and rotating machinery. Wavelet scattering networks, which are fixed time–frequency representations based on existing wavelets, are modified to be learnable so that they can learn features that are optimal for fault diagnosis. The learnable wavelet scattering networks are developed using the genetic algorithm-based optimization of second-generation wavelet transform operators. The simulation and experimental results for the diagnosis of analog circuit faults demonstrates that the developed diagnosis scheme achieves greater fault diagnosis accuracy than other methods in the literature, even while considering a larger number of fault classes. The performance of the diagnosis scheme on benchmark datasets of bearing faults and gear faults shows that the developed method generalizes well to fault diagnosis in multiple domains and has good transfer learning performance, too.
- Item2021 Annual Report - Center for Engineering Concepts Development(2021-10-01) Anand, Davinder; Hazelwood, DylanCECD is twenty-one years old and continues to be a platform for experimenting with new ideas in engineering research and education with special attention to the impact of engineering on society. I’m pleased to report that we continue to be supported by ARL, NSWC-IHEODTD, the State of Maryland, and the Neilom Foundation. One hundred guests helped us celebrate our twenty years of activities highlighting innovative activities of contemporary interest that benefit the economic welfare of the State of Maryland and the Nation. This report provides a brief overview of those accomplishments as well as ongoing activities that bring great credit to our faculty and students that comprise CECD.
- ItemArtificial Intelligence-Related Research Funding by the U.S. National Science Foundation and the National Natural Science Foundation of China(IEEE, 2020-10-06) Abadi, Hamidreza Habibollahi Najaf; He, Zhou; Pecht, MichaelFor the United States and China, artificial intelligence (AI) algorithms, methods, and applications are considered key to a nation's economic competitiveness and security. This paper investigates funding by the U.S. National Science Foundation and National Natural Science Foundation of China from 2010 to 2019, including the key institutions and universities that received AI awards, and the key AI disciplines and applications of focus in the research. Comparisons between the U.S. National Science Foundation and the National Natural Science Foundation of China, including the number of published papers as a result of the awards, are also presented.