Stacker predictive maintenance method and system based on deep learning

Abstract

The invention discloses a stacker predictive maintenance method and system based on deep learning, and the method comprises the steps: collecting the historical fault data of a stacker, and enabling the historical fault data to comprise a fault state parameter data set in a period of time before a fault happens; establishing a basic model for analyzing the failure rate of the stacking machine according to the historical failure data; collecting state parameters of the stacking machine during working in real time, inputting the state parameters into the basic model, analyzing the state parameters, collected in real time, of the stacking machine based on the basic model, and outputting a stacking machine failure rate value; and when the failure rate value of the stacking machine exceeds a set threshold value, giving out a failure alarm. The method is based on a basic model of the failure rate of the stacking machine, analyzes and processes state parameters when the stacking machine worksand outputs a stacking machine failure rate value. Whether the stacking machine breaks down or not is judged according to the fault rate value so that maintenance can be conducted when faults are found in the first time, and therefore economic losses and potential safety hazards are reduced.

Description

technical field [0001] The invention relates to the field of computer technology, in particular to a deep learning-based predictive maintenance method and system for a stacker crane. Background technique [0002] Stacker is a commonly used logistics storage equipment, which is widely used in the pick-and-place of various elevated warehouses. It is the core key equipment in the intelligent storage system, so it is also very important for the maintenance of the stacker. It is necessary to ensure The stacker can be maintained at the first time in the event of a failure, reducing the economic loss on the production line caused by the sudden failure of the stacker, and even potential safety hazards. [0003] Most of the spare parts of the stacker equipment are expensive and have a long lead time, which brings many difficulties to the maintenance of the stacker. However, in the existing situation, because the stacker cranes work frequently and are widely used in unmanned warehous...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a method and system for predictive maintenance of stacker cranes based on deep learning, aiming at solving the problem that maintenance personnel are difficult to find the fault at the first time when the existing stacker machine breaks down in operation Maintenance, resulting in economic losses and safety hazards

Images