A new, multiple model approach for detection and identi cation of structural damage in a rotating helicopter blade is presented. A full-scale rotor analysis using a detailed model of the hingeless blade elastic behavior and dynamics is carried out. Several stiffness damage levels and locations are considered, and a set of Kalman lters is constructed accordingly. The best tting model is determined in a probabilistic manner. Because the new method is model-based, the need for a training stage is eliminated, and a wide range of ight regimes can be handled. Moreover, process and measurement noises are treated inherently, contributing to the superiority of the method over previously published related methods. A Monte Carlo simulation study is used to provide a comprehensive analysis of the statistical nature of the method. Single-blade analysis results demonstrate excellent identi cation capability and good damage detection in the presence of a relatively high level of noise. The case of damage located near the blade's root combined with a sensor near the tip produces a high damage identi cation probability. In less detectable cases, such as damage located in midspan, a simple statistical procedure enables achieving a high detection probability along with a low false alarm rate. NomenclatureA j = j th lter residual covariance matrix a = vector of unknown parameters (fault in uence) B = continuous-time control matrix F = continuous-time state transition matrix G = continuous-time process noise distribution model H = measurement matrix N = number of single-run repetitions n= number of runs where damage was detected n d = predetermined threshold P = lter error covariance matrix P D = overall detection probability P FA = overall false alarm probability p = probability p D = single-run detection probability p FA = single-run false alarm probability Q d = discrete-time process noise covariance matrix q = tness probability R = measurement noise variance matrix r = residual vector u = control vector v = measurement noise vector w = process noise vector w d = equivalent discrete-time process noise vector x = state vector x d = damage location along the blade's span (measured from root) x s = sensor location along the blade's span (measured from root) O x j = j th lter predicted state estimate Z i = measurement history from the rst sample until sample time t i z = measurement vector H = mode shape matrix » = vector of elastic mode participating factors U = state transition matrix W = control matrix
A new health and usage monitoring methodology for detection and identification of damage in a helicopter rotor is presented. A full-scale rotor analysis in forward flight has been carried out using a detailed model of the coupled blade-fuselage behavior. Several rotor component faults, as well as local blade stiffness defects are considered. A set of Kalman filters is constructed, where the calculated blade tip response, in addition to elastic modes, comprises a state vector. In the proposed approach, each filter is based on the assumption that a particular fault has occurred. The best fitting model, according to measurements taken from the truth model, is determined in a probabilistic manner. In the numerical study used to demonstrate the performance of the method, two sets of noisy measurements are generated. The first set is based on blade tip sensors, and the second set consists of non-rotating hub loads. A Monte-Carlo analysis followed by a statistical experiment enables a comprehensive view of the statistical nature of the results. A parametric study is presented and conclusions concerning the detectability of damage in a helicopter rotor and the efficiency of the proposed method are drawn.
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