Abstract
This paper presents a novel approach of principal component analysis- (PCA-) assisted back propagation (BP) neural networks for the problem of rotor blade load prediction. 86.5 hours of real flight data were collected from many steady-state and transient flight maneuvers at different altitudes and airspeeds. Prediction of the blade loads was determined by the PCA-BP model from 16 flight parameters measured and monitored by the flight control computer already present in the helicopter. PCA was applied to reduce the dimension of the flight parameters influencing the component load and eliminate the correlation among flight parameters. Thus, obtained principal components were used as input vectors of the BP neural network. The combined PCA-BP neural network model was trained and tested by real flight data. Comparison of this model and to a BP neural network model as well as to a multiple linear regression (MLR) model was also done. The results of comparison demonstrate that the PCA-BP model has higher prediction precision with an average error of 2.46%, while 4.49% for BP and 10.20% for MLR. The results also reveal that the PCA-BP model has a shorter convergence path than the BP model. This method not only is useful in establishing the load spectra of helicopter rotor in-service where installation of strain gauges is impractical but also can reduce the cost of installation and maintenance measured by strain gauges.
Highlights
The fatigue life of helicopter structures plays an essential role in helicopter-maintenance work, as components of a helicopter could be fatigue-damaged under various working conditions with loads
In the principal component analysis (PCA) stage of the model, four independent factors were extracted as principal components from the sixteen original factors influencing the blade loads
A back propagation (BP) neural network model was built with the 4 principal components as input variables
Summary
The fatigue life of helicopter structures plays an essential role in helicopter-maintenance work, as components of a helicopter could be fatigue-damaged under various working conditions with loads. A common method of obtaining the component loads is to directly measure by installing strain gauges. Prediction of the component loads is determined by a linear regression or neural network model from the roll, pitch, airspeed, and other flight parameters measured by the flight control computer on the helicopter [8]. Cooper and DiMaio predicted the static load on a wing rib of aircraft using an artificial neural network This was achieved by using strain values obtained from the static test as an input parameter [7]. Calculate the outputs of all neurons in the hidden layer according to the following steps:. Where ωjk is the weighted value from the jth neuron node in the hidden layer to the pth node in the output layer and f o is the activation function, and it is a linear function generally. They are modified according to the learning samples traditionally by the delta rule [19,20,21,22]
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