关键词: 航空发动机;滑油监测;磨损;神经网络;数据监测;数据采集;特征提取

Abstract: Aiming at the defect that the traditional data feature extraction method is difficult to extract the effective features of monitoring data of Electrical capacitance tomography （ECT） aviation engine lubricating oil, a single-channel network model MSCNN-LSTM-BP based on multi-scales convolutional neural network (MSCNN), long short-term memory (LSTM) neural network and BP network is proposed. Integrating multi-scale learning into CNN, MSCNN and LSTM extract the two-dimensional features of data in spatial and temporal dimensions in a serial mode. The experimental results show that the classification accuracy of the 3 scales MSCNN-LSTM-BP for data samples reached 98.2%, single set of capacitor data acquisition test time is only 2.1986ms, the score of F₁ reached 98.57%. Overall performance is superior to CNN, LSTM and traditional multi-scale feature extraction methods. MSCNN-LSTM-BP meets the requirements for real-time and accuracy of aero-engine lubricating oil monitoring and is provided with good applicability.

Key words: Aeroengine;Oil lubricating monitoring;Wear;Neural network;Fault detection;Data acquisition;Feature extraction