{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Elizabeth Foughty", "hasEmail": "mailto:elizabeth.a.foughty@nasa.gov"}, "description": "DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE\r\nESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING\r\n\r\nSUBHASISH MOHANTY*, ADITI CHATTOPADHYAY*, JOHN N. RAJADAS**, AND CLYDE COELHO*\r\n\r\nAbstract. Fatigue damage and its prediction is one of the foremost concerns of structural integrity\r\nresearch community. The current research in structural health monitoring (SHM) is to\r\nprovide continuous (or on demand) information about the state of a structure. The SHM system\r\ncan be based on either active or passive sensor measurements. Though the current research on\r\nultrasonic wave propagation based active sensing approach has the potential to estimate very small\r\ndamage, it has severe drawbacks in terms of low sensing radius and external power requirements.\r\nTo alleviate these disadvantages passive sensing based SHM techniques can be used. Currently,\r\nfew efforts have been made towards, time-series fatigue damage state estimation over the entire\r\nfatigue life (stage-I, II & III). A majority of the available literature on passive sensing SHM techniques\r\ndemonstrates the clear trend in damage growth during the final failure regime (stage-III\r\nregime) or during when the damage is comparatively large enough. The present paper proposes a\r\npassive sensing technique that demonstrates a clear trend in damage growth almost over the entire\r\nstage-II and III damage growth regime. A strain gauge measurement based passive SHM frameworks\r\nthat can estimate the time-series fatigue damage state under random loading is proposed.\r\nFor this purpose, a Bayesian Gaussian process nonlinear dynamic model is developed to map the\r\nreference condition dynamic strain at a given instant of time. The predicted strains are compared\r\nwith the actual sensor measurements to estimate the corresponding error signals. The error signals\r\nestimated at two different locations are correlated to estimate the corresponding fatigue damage\r\nstate. The approach is demonstrated for an Al-2434 complex cruciform structure applied with\r\nbiaxial random loading.", "distribution": [{"@type": "dcat:Distribution", "description": "DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/Paper_21_.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Paper 21 .pdf"}, {"@type": "dcat:Distribution", "description": "Presentation", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/publication/Paper21_presentation.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Paper21_presentation.pdf"}], "identifier": "DASHLINK_243", "issued": "2010-10-13", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/243/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "DYNAMIC STRAIN MAPPING AND REAL-TIME DAMAGE STATE ESTIMATION UNDER BIAXIAL RANDOM FATIGUE LOADING"}