Degradation Prediction Method Based on Quantum Attention Recurrent Encoder-Decoder Neural Network

Abstract

The present invention discloses a degradation prediction method based on quantum attention cyclic encoding and decoding neural network (QAREDNN). QAREDNN is used in it, and a quantum attention mechanism is introduced to reconstruct the encoder and decoder at the same time so that QAREDNN can fully mine and Emphasize important information while suppressing the interference of redundant information to obtain better nonlinear approximation capabilities; use quantum neurons to construct activity values ​​and weights, and replace encoders and decoders with quantum threshold recurrent units (QGRU) replaced by quantum rotation matrices The traditional recurrent unit in the device can improve the generalization ability and response speed of QAREDNN; in the training process of QAREDNN, the LM algorithm is introduced to realize the rapid update of the rotation angle and attention parameters of the quantum rotation matrix. Due to the advantages of QAREDNN in terms of nonlinear approximation ability, generalization ability, response and training speed, etc., the degradation prediction method based on quantum attention loop encoding and decoding neural network can obtain higher prediction accuracy and computational efficiency.

Description

technical field [0001] The invention belongs to the technical field of mechanical state monitoring, and in particular relates to a degradation prediction method based on a quantum attention loop encoding and decoding neural network. Background technique [0002] Rotating machinery is widely used in various critical equipment such as gas turbines, aero engines and wind turbines. Its state directly determines whether the equipment can operate safely and reliably for a long time. During the entire service process, rotating machinery components will undergo a series of different state degradation stages, and rotating machinery components will experience a series of different state degradation stages. The research on the method of predicting the state degradation trend of rotating machinery components will help to avoid key equipment failures The catastrophic accidents brought about reduce the maintenance cost of equipment and improve the efficiency of equipment. With the devel...

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Problems solved by technology

Therefore, the nonlinear approximation ability and generalization ability of the neural network are not ideal, and the prediction accuracy is not high.

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