A Meta-modeling Approach to Failure Prognosis Using Existing Data
Small Business Information
200 Canal View Blvd, Rochester, NY, -
Mgr., Maintenance and Log
Mgr., Maintenance and Log
AbstractABSTRACT: Impact Technologies, LLC, proposes to develop and demonstrate an A Meta-modeling Approach to Failure Prognosis Using Existing Data. Historically, implementation of CBM/PHM algorithms for fault diagnosis and prognosis require the availability of"ground truth"fault progression data derived from seeded fault testing or on-platform data acquisition. Such data are not readily available for most complex systems and fault data acquisition methods require extensive resources to arrive at an appropriate dataset. Introduction of new techniques such as the one proposed, that utilize archived data in combination with CBM/PHM technologies and classical reliability tools are needed to accurately predict the remaining useful life of military and industrial assets. In doing so, these techniques will make possible wide spread, cost effective implementation of CBM/PHM systems where previously not practical due to the absence of fault/failure time series data; thus, greatly reducing the cost and burden associated with maintenance, repair and overhaul practices for aircraft, ground facilities, and other vital equipment. BENEFIT: Implementation of CBM/PHM algorithms for fault diagnosis and prognosis require"ground truth"fault progression data typically derived from seeded fault testing or on-platform data acquisition. Such data are not readily available for most complex systems and fault data acquisition methods require extensive resources to arrive at an appropriate dataset. The Meta-modeling Approach to Prognosis using Existing Data offers a crucial link to the implementation of CBM/PHM strategies where currently not possible due to the absence of ground truth. In this approach, existing data serves as the basis for a prognostic model, circumventing more typical lengthy development paths. In doing so, this enables quicker, more cost effective deployment of CBM/PHM technologies and contribution to transformation to predictive maintenance practices. This approach also serves as the basis for a comprehensive Life Management Framework that includes: data processing/mining, FMECA/RCM methods, data/feature fusion techniques, metamodel design/testing, decision support, performance metrics, and risk assessment/management. Given the potential benefits, this approach to usage based prognostics will be attractive to military and commercial applications, including, but not limited to ground and air vehicle maintenance/repair organizations, aircraft logistics support centers such as Warner Robins, manufacturing, and other construction/repair facilities.
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