Health assessment method based on deep quantum learning

Abstract

The invention provides a health assessment method based on depth quantum learning, which comprises the following steps: 1. Constructing an initial depth quantum neural network model; 2, periodically collect vibration signals of that bear and extracting characteristic parameters from the vibration signals; 3, divide that data into a training set and a verification set, training a depth quantum neural network model by use the data of the training set, and evaluating the performance of the model by using the data of the verification set; The collected signals are preprocessed and the processed feature parameters are divided into training data set and testing data set. 4, adjust that parameters of the depth quantum neural network model, and selecte an optimal model for performance evaluation through continuously training the model; 5. Health assessment of bearing by using the model; Through the above steps, the trained deep quantum gods will realize the health evaluation of the bearing, prevent and reduce the occurrence of the equipment failure through the health evaluation of the bearing, minimize the maintenance cost, ensure the safe operation of the equipment and obtain the maximumequipment availability and economic benefits.

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

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