Method and device for monitoring state of AGV, edge equipment and storage medium

Abstract

The invention relates to a method and device for monitoring the state of an AGV, computing equipment and a storage medium. The method comprises the following steps: a first signal acquisition step: acquiring first signal data from an AGV at a first sampling rate, the first signal data at least comprising a vibration signal; a feature extraction step: extracting features from the first signal data;an abnormal state judgment step: determining whether the AGV is in an abnormal state or not based on the extracted features, executing a storage step under the condition that the AGV is determined tobe in a normal state, and executing a second signal acquisition step under the condition that the AGV is determined to be in an abnormal state; a second signal acquisition step: acquiring second signal data from the AGV at a second sampling rate, the second sampling rate being greater than the first sampling rate, and the number of types of the second signal data being equal to or greater than the number of types of the first signal data; and a storage step: storing the collected first signal data or second signal data.

Description

technical field [0001] The present disclosure generally relates to the technical field of factory digitization, and more specifically, relates to a method, device, computing device and storage medium for monitoring the state of an AGV. Background technique [0002] Currently, with the trend of flexible production, AGV (Automatic Guided Vehicle) is widely used. Almost all AGVs have a control system that makes it easy to get error codes in the event of an electronic failure. But most AGVs cannot diagnose mechanical failures on their own. According to the requirements of equipment safety and reliability, it is necessary to monitor and analyze the health status of AGV in real time. Some commercial solutions are to set up an edge computing device on the AGV to collect relevant data and analyze it. Due to the limitation of edge device performance, and in order to ensure real-time diagnosis, the data sampling rate is low, and some simple algorithms are more suitable. Therefore,...

AI Technical Summary

Problems solved by technology

It will be a huge challenge for edge devices to process and store all the data

[0006] 2. Using insufficient data and simple algorithms, it is difficult to obtain accurate results

If some complex algorithms are used, such as deep learning, a large amount of data storage and better edge device performance are required, which will greatly increase the cost

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