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林柏翰
Azure + IoT 之故障偵測
Azure + IoT 之故障偵測
Azure + IoT 之故障偵測
By
林柏翰
資深軟體工程師
・
New Taipei, Banqiao District, New Taipei City, Taiwan
Connect
Azure AI + IoT 之故障偵測
目標設備:電子產業的板件投收板機(6軸機械手臂)
專案功能:
資料收集
收集部署的感測器(振動感測器)
收集機械手臂物理資料(電流)
收集 PLC 資料(氣壓、溫度等)
資料前處理
資料上傳到 Azure 後透過 Python 進行資料格式轉換,便於模型運用
資料預測
隨機森林進行資料預測(分類任務)判別設備資料是否健康(是否異常)。
資料視覺化
設備狀態視覺化
機械手臂狀態視覺化
異常統計視覺化
工廠設備長期營運,透過定期保養保持設備的穩定性,難免遇到設備突發狀況,藉此為了能夠提早監測到設備狀態異常,透過資料結合機器學習,使其提早預測設備異常狀態。 由於設備狀態檢測必須透過感測器,來取得設備物理狀態,在大量部署的感測器所產生的大量資料,正常的PC 難以負荷、高階電腦需要高額費用、專人維護等問題,期望透過雲端解決此問題。 此專案,是利用公有雲 Azure 的 IoT 服務,作為感測器管理、資料收集、機器學習預測的平台。 並利用 Grafana 進行視覺化,使決策人可以快速反應處理。
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Post
林柏翰
Connect
Published: Apr 15th 2022
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Tools
Python
C#
Docker
PostgreSQL
Azure
Python
PaaS
Grafana
Docker
IoT
PostgreSQL
Azure
機器學習
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