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運用震動與聲音訊號深度學習模型於設備狀態檢測系統開發與實現 = = De...

葉彧承

運用震動與聲音訊號深度學習模型於設備狀態檢測系統開發與實現 = = Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	運用震動與聲音訊號深度學習模型於設備狀態檢測系統開發與實現 =/ 葉彧承.
Reminder of title:	Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models /
remainder title:	Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models.
Author:	葉彧承
Published:	雲林縣 :國立虎尾科技大學 , : 民113.07.,
Description:	[11], 83面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 李政道.
Subject:	System Integration. -
Online resource:	電子資源

運用震動與聲音訊號深度學習模型於設備狀態檢測系統開發與實現 = = Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models /
葉彧承

運用震動與聲音訊號深度學習模型於設備狀態檢測系統開發與實現 =Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models /Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models.葉彧承. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.07. - [11], 83面 :圖, 表 ;30公分.

指導教授: 李政道.

碩士論文--國立虎尾科技大學自動化工程系碩士班.

含參考書目.

傳統的設備故障往往都是成品在加工過程中出現問題或是人員發現設備異常或聽到噪音時才被發現，如今科技日新月異的發展上，工業4.0與和人工智慧已是智慧工廠發展重點，工廠的運作過程中，設備的資訊傳遞尤其重要，透過資訊傳遞與資訊分析，可以即時回傳設備訊息和即早發現潛在的故障。因此，本論文開發設備分級系統，以抽水馬達和氣壓管的設備分級實驗為例，使用震動感測器與麥克風收取檢測設備之震動、音訊資料，將收集資料儲存於MySQL資料庫以便資料保存與切割，並用訓練於時間卷積網絡、長短期記憶等深度學習模型，以此檢測設備在使用時的狀態，透過深度學習模型辨識的結果，比較設備分級中震動與音訊檢測不同方法比較其準確性，同時在震動與音訊檢測中時間卷積網絡、長短期記憶的訓練模型比較其準確性，通過以上對比出設備分級系統在訊號與深度學習模型上的優勢。在本論文的具AI氣壓管狀態檢測之標號牌自動分類工作站中，將故障分級系統整合至的操作系統中，用Node-RED設計UI介面，以便能即時顯示設備狀態，操作人員能在第一時間了解設備的狀態並進行排查，減少設備發生更嚴重的故障、減少不良率產生、避免人員傷亡。.

(平裝)Subjects--Topical Terms:

1450140
System Integration.

運用震動與聲音訊號深度學習模型於設備狀態檢測系統開發與實現 = = Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models /
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245 1 0 $a 運用震動與聲音訊號深度學習模型於設備狀態檢測系統開發與實現 = $b Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models / $c 葉彧承.
246 1 1 $a Development and Implementation of Equipment Status Detection System Using Vibration and Acoustic Signal Deep Learning Models.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.07.
300 $a [11], 83面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 李政道.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學自動化工程系碩士班.
504 $a 含參考書目.
520 3 $a 傳統的設備故障往往都是成品在加工過程中出現問題或是人員發現設備異常或聽到噪音時才被發現，如今科技日新月異的發展上，工業4.0與和人工智慧已是智慧工廠發展重點，工廠的運作過程中，設備的資訊傳遞尤其重要，透過資訊傳遞與資訊分析，可以即時回傳設備訊息和即早發現潛在的故障。因此，本論文開發設備分級系統，以抽水馬達和氣壓管的設備分級實驗為例，使用震動感測器與麥克風收取檢測設備之震動、音訊資料，將收集資料儲存於MySQL資料庫以便資料保存與切割，並用訓練於時間卷積網絡、長短期記憶等深度學習模型，以此檢測設備在使用時的狀態，透過深度學習模型辨識的結果，比較設備分級中震動與音訊檢測不同方法比較其準確性，同時在震動與音訊檢測中時間卷積網絡、長短期記憶的訓練模型比較其準確性，通過以上對比出設備分級系統在訊號與深度學習模型上的優勢。在本論文的具AI氣壓管狀態檢測之標號牌自動分類工作站中，將故障分級系統整合至的操作系統中，用Node-RED設計UI介面，以便能即時顯示設備狀態，操作人員能在第一時間了解設備的狀態並進行排查，減少設備發生更嚴重的故障、減少不良率產生、避免人員傷亡。.
520 3 $a Traditional equipment failures are often only detected when problems arise during the production process, when personnel notice abnormalities, or when unusual noises are heard. However, with the rapid development of technology, Industry 4.0 and artificial intelligence have become key focuses in developing smart factories. In the operation of factories, the transmission of equipment information is significant. Through information transmission and analysis, it is possible to instantly relay equipment status and detect potential failures early on. Therefore, this thesis develops an equipment grading system, taking the grading experiments of water pumps and pneumatic pipes as examples. Vibration sensors and microphones collect vibration and audio data from the equipment being tested. The collected data is stored in a MySQL database for data preservation and segmentation. Deep learning models such as TCN (Temporal Convolutional Network) and LSTM (Long Short-Term Memory) are then trained to detect the operational status of the equipment. By comparing the accuracy of different methods for detecting equipment conditions based on vibration and audio data, and by comparing the accuracy of the TCN and LSTM models in vibration and audio detection, the advantages of the equipment grading system in terms of signal detection and deep learning models are highlighted. In the AI labeling workstation developed in this thesis, the fault grading system is integrated into the operating system. A UI interface is designed using Node-RED, allowing real-time display of equipment status. This enables operators to understand the equipment status at the earliest opportunity, conduct troubleshooting, reduce the occurrence of more severe failures, decrease defect rates, and prevent casualties..
563 $a (平裝)
650 # 4 $a System Integration. $3 1450140
650 # 4 $a Equipment Classification. $3 1450139
650 # 4 $a Deep Learning. $3 1127423
650 # 4 $a LSTM. $3 1248405
650 # 4 $a TCN. $3 1450138
650 # 4 $a 系統整合. $3 1127166
650 # 4 $a 設備分級. $3 1450137
650 # 4 $a 深度學習. $3 1127425
856 7 # $u https://handle.ncl.edu.tw/11296/8j56zb $z 電子資源 $2 http