Trend prediction method based on depth quantum neural network

Abstract

The invention provides a trend prediction method based on a depth quantum neural network, which comprises the following steps: 1. constructing an initial depth quantum neural network; 2, periodicallycollecting vibration signals of the bearing, and carrying out feature mining on the vibration signals by using wavelet packet decomposition; 3, training the depth quantum neural network model by usingthe train set data, and evaluating the performance of the model by using the verification set data; preprocessing the collected signals and dividing the processed feature parameters into training data set and testing data set. 4, adjusting the parameters of the depth quantum neural network model, and selecting an optimal prediction model for performance evaluation through continuously training the model; 5, predicting the trend of the bearing by using the prediction model. Through the above steps, the trained depth quantum gods network can realize the trend prediction of the bearing, and thebearing can be maintained in time through the trend prediction of the bearing, the repair time can be shortened, the repair cost can be reduced, and the mechanical failure problem caused by the bearing maintenance delay can be solved.

Description

Technical field: [0001] The invention proposes a trend prediction method based on a deep quantum neural network, which belongs to the field of trend prediction. Background technique: [0002] Bearings are an important part of mechanical equipment. With the development of large-scale, complex and integrated mechanical equipment, the requirements for bearings are getting higher and higher. However, due to the complex and harsh working environment such as heavy load, fatigue, corrosion, high temperature, etc., it is easily damaged and affects the reliability of mechanical equipment. According to relevant statistics, the damage of bearings accounts for about 30% of the faults of rotating machinery. Therefore, it is very important to carry out fault diagnosis, condition monitoring and trend prediction of bearings. [0003] In recent years, bearing trend prediction has received extensive attention. Scholars at home and abroad have also done a lot of research and achieved certain...

AI Technical Summary

Problems solved by technology

[0004] With the popularity of neural networks, artificial neural networks have been widely used in these prediction algorithms. Although artificial neural networks have good results in bearing trend prediction, the widely used artificial neural networks have slow convergence speed and difficult Due to the shortcomings of multi-layer expansion, it is necessary to improve the neural network and propose a new prediction method

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