A method comprising: measuring sensor data of at least one physical component having a known operational status during operation of the at least one physical component; generating, by a computing device comprising at least one processor, a plurality of data points from the measured sensor data, each of the plurality of data points representing a measured occurrence of a feature of the measured sensor data; iteratively sampling with replacement, by the computing device, the plurality of data points to generate a plurality of subsets of the plurality of data points; determining, by the computing device within each of the plurality of subsets, a confidence interval having an upper bound and a lower bound that include a defined percentage of the respective subset of the plurality of data points to generate a plurality of confidence intervals having respective upper bounds and lower bounds; and generating, by the computing device, a composite confidence interval having a composite upper bound based on a first central tendency of the upper bounds of the plurality of confidence intervals and a composite lower bound based on a second central tendency of the lower bounds of the plurality of confidence intervals; wherein measuring the sensor data of the at least one physical component having the known operational status comprises measuring vibration data of the at least one physical component using one or more vibration sensors; and wherein the feature of the measured sensor data comprises an amplitude of the vibration data, or wherein measuring the sensor data of the at least one physical component having the known operational status comprises measuring structural response data of the at least one physical component using one or more structural response sensors; and wherein the feature of the measured sensor data comprises an amplitude of the structural response data. measuring sensor data of at least one physical component having a known operational status during operation of the at least one physical component; generating, by a computing device comprising at least one processor, a plurality of data points from the measured sensor data, each of the plurality of data points representing a measured occurrence of a feature of the measured sensor data; iteratively sampling with replacement, by the computing device, the plurality of data points to generate a plurality of subsets of the plurality of data points; determining, by the computing device within each of the plurality of subsets, a confidence interval having an upper bound and a lower bound that include a defined percentage of the respective subset of the plurality of data points to generate a plurality of confidence intervals having respective upper bounds and lower bounds; and generating, by the computing device, a composite confidence interval having a composite upper bound based on a first central tendency of the upper bounds of the plurality of confidence intervals and a composite lower bound based on a second central tendency of the lower bounds of the plurality of confidence intervals; wherein measuring the sensor data of the at least one physical component having the known operational status comprises measuring vibration data of the at least one physical component using one or more vibration sensors; and wherein the feature of the measured sensor data comprises an amplitude of the vibration data, or wherein measuring the sensor data of the at least one physical component having the known operational status comprises measuring structural response data of the at least one physical component using one or more structural response sensors; and wherein the feature of the measured sensor data comprises an amplitude of the structural response data. <EOS>