Using Data-Driven Prognostic Algorithms for Completing Independent Failure Analysis
Independent Failure Analysis
Identification and Recovery
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AbstractCurrent failure analysis practices use diagnostic technology developed over the past 100 years of designing and manufacturing electrical and mechanical equipment to identify root cause of equipment failure requiring expertise with the equipment under analysis. If the equipment that failed had telemetry embedded, prognostic algorithms can be used to identify the deterministic behavior in completely normal appearing data from fully functional equipment used for identifying which equipment will fail within 1 year of use, can also identify when the presence of deterministic behavior was initiated for any equipment failure.
SponsorsInternational Foundation for Telemetering
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Evaluating the Effects of Heart Failure Clinic Enrollment on Hospital Admission and Readmission Rates: A Retrospective Data AnalysisVeleta, Patricia M. (The University of Arizona., 2016)Heart failure (HF) is a clinical syndrome associated with high morbidity and mortality with a large economic burden, and is the leading cause of hospitalizations among Medicare beneficiaries in the United States. Healthcare reform has focused on strategies to reduce HF readmissions, including outpatient HF clinics. Purpose: The purpose of this DNP Project was to answer the following question: In adult patients diagnosed with HF, how does enrollment in the HF clinic, compared to non-enrollment affect hospital admission and readmission rates? Methods: A retrospective analysis of 767 unique patients and their 1,014 respective admissions and readmissions was conducted. Continuous and categorical data was analyzed and presented as a mean (M), standard deviation (SD), absolute number (N) and percentage (%). A Pearson Chi Square test was used for categorical variables and Analysis of Variance was used for age and ejection fraction (EF). Results: Study sample demographics (N=767); age (M=79.72, SD=7.48); gender (57.6 % male) and EF (M=0.43, SD=0.16) were evaluated. The No HF clinic (No HFC) and HF clinic (HFC) enrollment groups (N=573) were compared for age (M=79.49, SD=7.65) (M=80.39, SD=6.94), male gender (54.6%, 66.5%) and EF (M= 0.44, SD=0.17) (M=0.42, SD=0.15), respectively. Each sample patient had at least one admission for HF during 2015; of which 573 (46.2%) were in the No HFC group and 194 (8.4%) were in the HFC group (p<0.001). There was no difference in all-cause readmissions between the No HFC group [n=95(14.5%)] and the HFC group [n=37(16.2%)] (p=0.534) and no difference in HF-related readmissions between the No HFC group [n=72(11.0%)] and the HFC group [n=23(10.0%)] (p=0.700). Conclusions: This DNP project demonstrated a significant difference in HF admission rates in favor of the HFC group. While no differences were found in all-cause or HF-related readmission rates in No HFC and HFC groups, the rates are less than the national average. Unintended findings were that datasets can be very poorly constructed and populated, resulting in large amounts of unusable data. Recommendations are for more rigor in the organization of datasets to assure accurate comparisons between admission and readmission rates based on enrollment in HF clinics.
Launch Vehicle and Satellite Independent Failure Analysis Using Telemetry Prognostic AlgorithmsLosik, Len; Failure Analysis (International Foundation for Telemetering, 2008-10)Unique vehicle designs encourage the use of the builder to complete its own failure analysis. Current failure analysis practices use telemetry and diagnostic technology developed over the past 100 years to identify root-cause. When telemetry isn't available speculation is used to create a list of prioritized, potential causes. Prognostic technology consists of generic algorithms that identify equipment that has failed and is going to fail while the equipment is still at the factory allowing the equipment to be repaired or replaced while it is still on the ground for any spacecraft, satellite, launch vehicle and missile.