title: [Artificial neural network forecasting method in monitoring technique by spectrometric oil analysis].
abstract: The spectrometric oil analysis (SOA) is an important technique for machine state monitoring and fault diagnosis, and forecasting machine state through SOA results has an advantage of finding out machine system wear fault early. Because Artificial Neural Network (ANN) possesses obvious advantages over traditional forecasting models for identifyingnon-linear model and forecasting non-even signal, the ANN forecasting approach was applied to monitoring technique by SOA, and the monitoringtechnique by SOA based on ANN forecasting was put forward. In the forecasting model, a 3-layer BP network structure was adopted.Aiming at the problem that ANN structure has a great effect on forecasting precision, the authors utilized the Genetic Algorithm (GA) to optimize the node number of input layer, the node number of hidden layer, and MSE (Mean of Squared Error) target value which was required for ANN training, and obtained the optimum forecasting model of ANN. Finally, the practical SOA data of some engine was analyzed and forecasted by ANN, and the forecasting result was compared with that of traditional ARMA model. The result fully showsthe superiority and effectivity of the new method.
authors.affiliation: Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
authors.forename: Yu-wei
authors.initials: YW
authors.lastname: Yang
authors.forename: Guo
authors.initials: G
authors.lastname: Chen
authors.forename: Yu-wei
authors.initials: YW
authors.lastname: Yang
authors.forename: Guo
authors.initials: G
authors.lastname: Chen
mesh_terms: D000465:Algorithms; D007501:Iron / Q000032:analysis; D008670:Metals / Q000032:analysis; D015233:Models, Statistical; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D013057:Spectrum Analysis / Q000379:methods*
chemical_list: D008670:Metals; D007501:Iron