A method for real-time model based vehicle structural anomaly detection, comprising: receiving a real-time measurement from a plurality of sensors on the vehicle structure corresponding to a location on the vehicle structure during an operation of the vehicle; comparing the real-time measurement to expected operation data for the location of the vehicle structure to provide a modeling error signal; calculating a statistical significance of the modeling error signal to provide an error significance, wherein calculating the statistical significance of the modeling error signal comprises recursively estimating an estimated mean and an estimated variance of the modeling error signal to determine whether the modeling error signal warrants indicating the vehicle structural anomaly; and assessing a probability that the modeling error signal is away from zero by computing the Probability of False Alarm (Pfa) of the modeling error signal for the vehicle structural anomaly detection based on the estimated mean, and the estimated variance to provide the error significance; determining a persistence of the error significance wherein the step of determining the persistence of the error significance further comprises inputting a unity signal into a first order filter when the error significance falls below a user-selected Pfa threshold value; comparing an output of the first order filter to a value close to one; indicating a structural anomaly condition when the output of the first order filter is sufficiently close to one, wherein the persistence is high; and wherein the Pfa threshold value and a filter time constant of the first order filter are tunable parameters that depend on a quality of the modeling error signal and tolerance for false positive indications, and indicating the vehicle structural anomaly, if the persistence exceeds a persistence threshold value. receiving a real-time measurement from a plurality of sensors on the vehicle structure corresponding to a location on the vehicle structure during an operation of the vehicle; comparing the real-time measurement to expected operation data for the location of the vehicle structure to provide a modeling error signal; calculating a statistical significance of the modeling error signal to provide an error significance, wherein calculating the statistical significance of the modeling error signal comprises recursively estimating an estimated mean and an estimated variance of the modeling error signal to determine whether the modeling error signal warrants indicating the vehicle structural anomaly; and assessing a probability that the modeling error signal is away from zero by computing the Probability of False Alarm (Pfa) of the modeling error signal for the vehicle structural anomaly detection based on the estimated mean, and the estimated variance to provide the error significance; determining a persistence of the error significance wherein the step of determining the persistence of the error significance further comprises inputting a unity signal into a first order filter when the error significance falls below a user-selected Pfa threshold value; comparing an output of the first order filter to a value close to one; indicating a structural anomaly condition when the output of the first order filter is sufficiently close to one, wherein the persistence is high; and wherein the Pfa threshold value and a filter time constant of the first order filter are tunable parameters that depend on a quality of the modeling error signal and tolerance for false positive indications, and inputting a unity signal into a first order filter when the error significance falls below a user-selected Pfa threshold value; comparing an output of the first order filter to a value close to one; indicating a structural anomaly condition when the output of the first order filter is sufficiently close to one, wherein the persistence is high; and wherein the Pfa threshold value and a filter time constant of the first order filter are tunable parameters that depend on a quality of the modeling error signal and tolerance for false positive indications, and indicating the vehicle structural anomaly, if the persistence exceeds a persistence threshold value.