Equipment Pattern Recognition of Unbalanced Fuel Consumption Data Based on Grouping Multi-BP Neural Network

Abstract

Artificial intelligence technology provides an unprecedented opportunity to assess the state of large-scale equipment with oil monitoring data. One of the key challenges in analyzing HFC (Hydraulic Fluid Composition) data is constructing a small sample classification, identifying abnormal equipment subgroups, and finding the significant impact indicators in unbalanced equipment. We propose GMBPN, a monitoring framework to identify the abnormal state and the order of influence index through multiple BP neural networks with group sampling. In order to improve the accuracy of small sample classification caused by the unbalanced number of samples, the classification model of small sample training is established by the quantitative grouping index. For the optimal classification model, the contribution order of each feature is compared by increased information gain. When GMBPN is applied to HFC data, it successfully captures the representative characteristics of abnormal equipment and impactors and shows its advantages over classical K-means and BP neural models in accuracy, classification consistency, and sampling methods.

Application of a Modified BPS Neural Network Based on Three-Way Decision Theory in an Effectiveness Evaluation for a Remote Sensing Satellite Cluster

“…The architecture design of the neural network model varies according to the different problems to be solved. The architecture design of the neural network often determines the overall network performance [28][29][30]. Moreover, the hyperparameters such as the number of hidden layers, the number of nodes per layer, and the type of activation function have a direct impact on the training accuracy and generalization ability of the neural network model, and its importance is self-evident.…”