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

DSTO-TN-0239sigmoidal function to this linear output. The output of all hidden units (on a particularlayer) are weighted and summed to produce a single output. The weights of individualhidden units and the layer of hidden units are determined using a backpropagationtraining scheme. Essentially the backpropagation scheme involves incremental changes ofthe weightings to reduce the error between the estimated output and the desired outputusing error surface gradients. (The authors reference Nelson and Illingworth [31] orLippmann [28] for further neural network details.) The network was configured with fourhidden units and its training tolerance was set to 2% (if the error between the estimated anddesired outputs is less than the training tolerance, no weighting changes are made).The database was generated from a helicopter heavily instrumented (in a separatestructural analysis of 17 safety-critical components and 21 fatigue-sensitive locations) tomonitor flight parameters and loads. The data consisted of time histories of 24 flightparameters and 17 component loads measured during seven standard flight manoeuvres.Each manoeuvre consisted of a step change in control stick position, either 0.5 in or 1.0 in,from a steady forward flight position. Of the 24 flight parameters, eight were chosen asnetwork inputs (based on intuitive understanding of motion and loads). The chosenparameters were pitch, roll, and yaw rates, vertical, lateral, and longitudinal accelerations,and longitudinal and lateral control positions.Only a small segment of data from each manoeuvre (two seconds centred around the stickposition change) was used as network input, since each manoeuvre contained a substantialamount of steady-state data where neither flight parameters nor loads changed. Afrequency analysis revealed two principal cyclic components, 4.75 Hz (main rotorfrequency) and 19 Hz (blade passing frequency).The network was trained to either estimate time-varying mean or time-varying oscillationof the tailboom bending load and pitch link load. This separation into oscillatory and meancomponents was necessary since none of the measured flight parameters varied in asinusoidal fashion that was directly correlated to the main rotor blade motion. Trainingincluded data from six manoeuvres, while testing was conducted on data from the seventhmanoeuvre. This procedure was repeated on each of the seven manoeuvres to test theestimation accuracy. Cook et al. state that randomising the time-history input speeds up theevaluation of network coefficients (they reference Nelson and Illingworth [31]), but warnthat this procedure reduces the network’s ability to estimate temporally varyingcharacteristic information.The mean scaled percentage errors of the 0.5 in and the 1.0 in manoeuvres wererespectively: 6.9% and 9.3% for the mean tailboom bending load, 2.3% and 5.2% for theoscillatory component of the same load, 4.3% and 6.5% for the mean pitch link load, and3.2% and 6.8% for the oscillatory component of the same load. The higher error in the 1.0 inmanoeuvres is explained as a result of estimation extrapolation (instead of interpolation) ofthe training data set.14