Fault prediction and health management method applied to automatic production line

Abstract

The invention belongs to the technical field of automatic production lines, and particularly relates to a fault prediction and health management method applied to an automatic production line. The method comprises the following steps of preparing a plurality of automatic production lines to be tested, carrying out heating aging and vibration testing on the bearing equipment in automatic productionlines to be tested to obtain training data and storing the training data in a database, carrying out FMECA analysis to obtain a training sample, training a neural network by using a particle filtering algorithm and placing into a test chip, monitoring the bearing equipment in the automatic production lines in the working state in real time by using a testing chip, and calculating residual servicelife and conducting health management. The running state of the bearing equipment in an automatic production line can be monitored in real time, and the fault time is predicted. The occurrence probability of sudden faults is reduced, and a plurality of potential safety hazards are avoided when the sudden faults occur, so that properties can be protected, and the maintenance cost is reduced.

Description

technical field [0001] The invention belongs to the technical field of automated production lines, and in particular relates to a fault prediction and health management method applied to automated production lines. Background technique [0002] With the development of science and technology, the integration and complexity of modern automated production line equipment are increasing day by day, and the maintenance cost and difficulty are also rising sharply; the traditional manual maintenance method with low efficiency and high cost is faced with a large number of complex The current automated production line equipment is no longer applicable. The failure prediction and health management system (PHM) aims to reduce the cost of manual maintenance, to analyze the failure model on the automated production line to determine its health status, so that self-assessment and failure warning can be performed in an unattended situation, and at the same time, health Management, repairin...

AI Technical Summary

Problems solved by technology

This method is currently mainly used to study the failure mechanism of the bearing equipment in the automated production line, but the fault diagnosis and prediction for the purpose of reducing manual maintenance has rarely appeared.

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