Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN

Abstract

A fault diagnosis model for aviation bearing is proposed to tackle the challenge of feature extraction from bearing vibration signals amidst noise. This model combines a long short-term memory (LSTM) network with an improved deep residual shrinkage network (IDRSN) based on semi-soft threshold optimization. The LSTM module first extracts temporal features from the original one-dimensional vibration signals, followed by deep feature extraction via the IDRSN. A fully connected layer with a SoftMax activation function is then used to classify faults in the training set. The model is then validated through different fault test sets. Experimental results show that the LSTM-IDRSN model outperforms the traditional deep residual shrinkage network (DRSN) model, achieving a 4.98% increase in classification accuracy, reaching 96.43%. Additionally, the model maintains high precision and stability even under noise interference, outperforming other methods.

Keywords

• Bearing fault diagnosis
• deep residual shrinkage network
• long short-term memory network
• noise interference