The aircraft engine lubricating oil monitoring is essential in terms of the flight safety and also for reduction of the maintenance cost. The concentration of metal elements in the lubricating oil includes a large amount of information about the health condition of the aircraft engine. By monitoring the lubricating oil, maintenance engineers can judge the performance deterioration of the aircraft engine and can find the latent mechanical faults in the aircraft engine in advance. But it is difficult for traditional methods to predict the tendency of the mental elements concentration in the lubricating oil. In this paper, a time series prediction method based on process neural network (PNN) is proposed to solve this problem. The inputs and the connection weights of the PNN are time-varied functions. A corresponding learning algorithm is developed. To simplify the learning algorithm, a set of appropriate orthogonal basis functions are introduced to expand the input functions and the connection weight functions of the PNN. The effectiveness of the proposed method is proved by the Mackey-Glass time series prediction. Finally, the proposed method is utilized to predict the Fe concentration in the aircraft engine lubricating oil monitoring, and the test results indicate that the proposed model seems to perform well and appears suitable for using as a predictive maintenance tool.
Time series prediction plays an important role in engineering applications. Artificial neural networks seem to be a useful tool to solve these problems. However, in real engineering, the inputs and outputs of many complicated systems are time-varied functions. Conventional artificial neural networks are not suitable to predicting time series in these systems directly. In order to overcome this limitation, a parallel feedforward process neural network (PFPNN) is proposed. The inputs and outputs of the PFPNN are time-varied functions, which makes it possible to predict time series directly. A corresponding learning algorithm for the PFPNN is developed. To simplify this learning algorithm, appropriate orthogonal basis functions are selected to expand the input functions, output functions and network weight functions. The effectiveness of the PFPNN and its learning algorithm is proved by the Mackey-Glass time series prediction. Finally, the PFPNN is utilized to predict exhaust gas temperature time series in aircraft engine condition monitoring, and the simulation test results also indicate that the PFPNN has a faster convergence speed and higher accuracy than the same scale multilayer feedforward process neural network.