Abstract
Deep learning techniques have outstanding performance in feature extraction and model fitting. In the field of aero-engine fault diagnosis, the introduction of deep learning technology is of great significance. The aero-engine is the heart of the aircraft, and its stable operation is the primary guarantee of the aircraft. In order to ensure the normal operation of the aircraft, it is necessary to study and diagnose the faults of the aero-engine. Among the many engine failures, the one that occurs more frequently and is more hazardous is the wheeze, which often poses a great threat to flight safety. On the basis of analyzing the mechanism of aero-engine surge, an aero-engine surge fault diagnosis method based on deep learning technology is proposed. In this paper, key sensor data are obtained by analyzing different engine sensor data. An aero-engine surge dataset acquisition algorithm (ASDA) is proposed to sample the fault and normal points to generate the training set, validation set and test set. Based on neural network models such as one-dimensional convolutional neural network (1D-CNN), convolutional neural network (RNN), and long-short memory neural network (LSTM), different neural network optimization algorithms are selected to achieve fault diagnosis and classification. The experimental results show that the deep learning technique has good effect in aero-engine surge fault diagnosis. The aero-engine surge fault diagnosis network (ASFDN) proposed in this paper achieves better results. Through training, the network achieves more than 99% classification accuracy for the test set.
Highlights
In recent years, the aviation industry has developed rapidly, and the safety of aviation aircraft has attracted widespread attention
The left picture shows the change curve of loss value, and the right picture shows the change curve of accuracy value. It can be seen from the results that the loss value of the aero-engine surge fault diagnosis network (ASFDN) model decreases rapidly and tends to be stable, and the accuracy is rapidly improved to reach a stable state, which shows the effectiveness of the model for the classification of aero-engine surge faults
Since this article mainly studies the classification of aero-engine surge faults, it focuses more on the fault identification
Summary
The aviation industry has developed rapidly, and the safety of aviation aircraft has attracted widespread attention. The reverse flow of the flame in the combustion chamber is a more dangerous situation Even if it lasts for a few tenths of a second, the high-temperature flames rushing are enough to burn all the blades of the compressor. This situation is because the compressor surges too violently, causing the high-temperature gas in the combustion chamber at the back to flow back into the compressor. Earlier, even if this process only lasted a few tenths of a second, the rushing hightemperature flame was enough to burn out all the blades of the compressor and cause the engine to be scrapped [3]. In order to ensure the safety of the aero engine, it is necessary to carry out a failure test on the aero engine, and to identify and classify the failure signal
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