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.
In this paper, a continuous wavelet process neural network (CWPNN) model is proposed based on the wavelet theory and process neural network model. The network offers good compromise between robust implementations resulting from the redundancy characteristic of non-orthogonal wavelets, and efficient functional representations that build on the time-frequency localization property of wavelets. Moreover, the network can deal with continuous input signals directly. The corresponding learning algorithm is given and the network is used to solve the problems of aeroengine condition monitoring. The simulation test results indicate that the CWPNN has a faster convergence speed and higher accuracy than the same scale process neural network (PNN) and BP neural network. This provided an effective way for the problems of aeroengine condition monitoring.
A convolution sum discrete process neural network (CSDPNN) is proposed. CSDPNN utilizes discrete samples as inputs directly and employs convolution sum to simulate the process inputs so as to deal with the time accumulation existing in many time series. Without the procedures of fitting the discrete samples into continuous functions to generate inputs and then to expand the input functions by basis functions, CSDPNN is better understandable and is with less precision reduction compared with process neural network (PNN) with function inputs. The approximation capacity of CSDPNN is analyzed in this paper, and it proved that CSDPNN can approximate PNN and has approximation capacity not worse than traditional artificial neural network (ANN). Finally, CSDPNN, PNN and ANN are utilized to predict the Logistic chaos time series and the iron concentration in the lubrication oil of aircraft engine, and the application test results indicate that CSDPNN performs better than PNN and ANN given the same conditions.