| dc.description.abstract | The production of TFT-LCD factory is fully automated 24 hours non-stop production, and the automatic transmission equipment is an important lifeline connecting all production processes. Every time the automatic transmission equipment is unexpectedly stopped, the impact is production suspension, and will result in huge capacity loss of TFT-LCD factory. Generally, according to the rule of thumb, equipment damage is judged by judging equipment vibration and noise, This study uses PYTHON for vibration digital signal processing. Through experiments adjusting equipment at different speeds, basic data of automatic warehouse lifting mechanisms in time domain, amplitude domain, and frequency domain are established, collected, and analyzed. By comparing the differences before and after equipment maintenance and adjustment, the characteristic signals obtained from the analysis of the differences before and after are used for automatic warehouse lifting mechanism fault warning monitoring.
The entire monitoring system utilizes Python programs for data collection, data analysis, data cutting, and digital signal processing to analyze in both the time and frequency domains. It can effectively predict the usage of the automatic storage system′s lifting reducer, report through the warning system, and effectively avoid production interruptions caused by unexpected downtime after maintenance and adjustment in the process. | en_US |