A health assessment method based on deep quantum learning

Abstract

The present invention provides a health assessment method based on deep quantum learning, the steps are as follows: 1: construct an initial deep quantum neural network model; 2: regularly collect bearing vibration signals, and extract characteristic parameters for the vibration signals; 3: divide the data into training sets and verification set, use the training set data to train the deep quantum neural network model, and evaluate the performance of the model through the verification set data; preprocess the collected signal, and divide the processed characteristic parameters into a training data set and a test data set; Four: Adjust the parameters of the deep quantum neural network model, and select the model with the best performance evaluation by continuously training the model; Five: Use the model to evaluate the health of the bearing; through the above steps, the trained deep quantum neural network realizes the health of the bearing Evaluation, through the health assessment of the bearings, prevents and reduces the occurrence of equipment failures, ensures the safe operation of the equipment and obtains the maximum equipment availability and economic benefits with the minimum maintenance cost.

Description

Technical field: [0001] The invention proposes a health assessment method based on deep quantum learning, which belongs to the field of health assessment. Background technique: [0002] According to relevant statistics, the problems caused by bearings account for more than 40% of all mechanical failures. Therefore, the research on bearings has attracted widespread attention from industry and academia. Bearings are typical rotating mechanical equipment, and their operating status plays a vital role in their use efficiency, maintenance costs, economic losses caused by equipment failures, and personal safety. At the same time, bearings are also the most widely used mechanical parts in machinery, aerospace and some military industrial sectors, and they are also one of the more vulnerable parts in mechanical equipment. [0003] The performance degradation of bearings is the main factor affecting the normal use of bearings, and the grasp of the health status of bearings is extrem...

AI Technical Summary

Problems solved by technology

Traditional health assessment methods are still lacking in the accuracy of data collection and assessment, so it is necessary to propose a new method for bearing health assessment

Images