振動訊號分析於自動倉儲升降機構之故障預警監測

NCUIR > Engineering College > Executive Master of Mechanical Engineering > Electronic Thesis & Dissertation > Item 987654321/93580

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/93580

Title:	振動訊號分析於自動倉儲升降機構之故障預警監測
Authors:	楊文亞;YANG, WEN-YA
Contributors:	機械工程學系在職專班
Keywords:	振動;故障監控;故障預警;vibration;Fault monitoring;Early warning of failures
Date:	2023-07-03
Issue Date:	2024-09-19 17:20:31 (UTC+8)
Publisher:	國立中央大學
Abstract:	TFT-LCD工廠的生產都是全自動化24小時不間斷生產，而自動傳送設備是串起所有生產製程的重要命脈，每一次自動化傳送設備不預期停機，造成的影響就是生產中止，且會成TFT-LCD工廠巨大的產能損失，一般是依照經驗法則，透過判別設備振動和噪音的方式，來判別設備損壞的情況，本研究是利用PYTHON進行振動數位訊號處理，透過調整設備不同速度的實驗，建立、收集、分析，自動倉儲升降機構在時域、幅域、頻域上各項基本數據，再透過比較設備保養、調整的前後差異，將前後差異分析取得的特徵訊號，用於自動倉儲升降機構故障預警監測。整個監測系統利用python程式進行數據收集，數據分析，數據切割，並透過數位訊號處理，在時域與頻域上進行分析，可以有效預測自動倉儲系統升降減速機使用情況，透過預警系統通報，以及工程上維修調整後，能有效避免不預期停機造成生產的中斷。;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.
Appears in Collections:	[Executive Master of Mechanical Engineering] Electronic Thesis & Dissertation

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