Estimation of Structural Component Loads in Helicopters: A Review ...

DSTO-TN-0239weight. The study aimed to predict alternating component load (the amplitude of the loadover one main rotor revolution) during a given manoeuvre.The measured data were filtered using deletion, smoothing (via averaging), and binseparation. As noted earlier by Haas [23], coupling of regression parameters (such ascollective and longitudinal stick position) led to an improved model. A set of perturbationparameters (the absolute value of the difference between the control input and the steadyflight value) was also found to give good results when used in the regression analysis. Thehigh-load data points were given a higher weighting (and thus higher influence) in theregression model when compared to low-load data points.Using 24 flight parameters, Tang and Moffat [25] used stepwise multiple regression tomodel the loads experienced by three dynamic helicopter components: pitch housing leadlag lug, main rotor blade, and tail rotor fork. Nineteen flight test conditions were used toformulate the flight load models, with an additional four flights used to validate the model(in a blind test). Three load models were developed for each component: maximum meanload, minimum mean load, and maximum load range. In addition, 10 signal features werealso extracted from each of the 24 flight parameters. These 10 features were the mean, meansquare, maximum, minimum, amplitude, and standard deviation of the time signal, as wellas normalised time duration above 25, 50, 75, and 90% of the peak.The step multiple regression models began with a linear approximation and subsequentlyadded higher order non-linear terms. These terms were either introduced, based on the F-to-enter statistic, or removed on the F-to-remove statistic. The authors cite Montgomeryand Peck [26] for further details. Three flight load models were formulated for eachcomponent: maximum load range (RNG), maximum mean load (MAX), and minimummean load (MIN). The pitch housing lead-lag lug models for the MAX, MIN, and RNG(MMR) were linear and had correlation coefficients of 0.80, 0.96, and 0.96 respectively. Themain rotor blade models for the MMR had correlation coefficients and model types of 0.70and linear, 0.89 and linear, and 0.95 and second order respectively. Finally the tail rotor forkmodels for the MMR had correlation coefficients and model types of 0.92 and second order,0.92 and second order, and 0.96 and non-linear, respectively. The flight test parametersampling rate varied (depending on the parameter) between 60–500 Hz. In contrast, theoperational flight data recorder’s sampling rate varied only between 1–20 Hz. Nodegradation in performance was noted when the sampling rate was reduced from the flighttest rate to the operational flight rate. The models developed were partitioned manuallyinto time windows, varying between 0.5–22 seconds, to coincide with the duration of themanoeuvre. When the data were later partitioned into constant six-second durationwindows, the results of all models were degraded. When the data reduction and a constantsix-second time window were used on the blind data sets, the models were no longeradequate. The authors state that one of the four blind test data sets was spurious, and onceremoved the results improved.11