Complex Industrial Machinery Health Diagnosis Challenges and Strategies

Hsiao Yu Wang*, Ching Hua Hung

*此作品的通信作者

研究成果: Conference contribution同行評審

1 引文 斯高帕斯(Scopus)

摘要

This study is dedicated to addressing a spectrum of pivotal challenges and predicting their potential ramifications. Specifically, its objectives encompass the detection of tool breakage in milling-turning composite machinery, the assessment of the service life of punching machine heads, and the evaluation of mold longevity in forging apparatus, among other intricacies. The overarching objective is the establishment of an equipment health diagnosis system tailored for intricate industrial setups. It is evident from our interactions with the industry that the rationale for monitoring strategies and threshold values are contingent upon the idiosyncratic attributes of the equipment and the sector. While the metal processing sector has been trailing behind the semiconductor industry in the realm of intelligent monitoring by an approximate span of a decade, it faces an analogous array of challenges. These encompass dwindling demographics, leading to an increased reliance on external labor for shifts, elevated personnel turnover rates thereby limiting the availability of experienced personnel for tasks such as tool changes, mold replacements, and maintenance. Additionally, the necessity to uphold traceability standards for mold and punching head usage history, notably in the context of aerospace industry compliance, compounds these challenges. Consequently, the industry aspires to achieve two paramount objectives for vital production equipment: first, the execution of failure diagnostics to appraise tool or mold longevity and assess product quality. Second, the transition from time-based to condition-based maintenance practices, even under conditions that necessitate frequent mold substitutions to cater to diverse product manufacturing needs.

原文English
主出版物標題Smart Grid and Internet of Things - 7th EAI International Conference, SGIoT 2023, Proceedings
編輯Der-Jiunn Deng, Jyh-Cheng Chen
發行者Springer Science and Business Media Deutschland GmbH
頁面130-140
頁數11
ISBN(列印)9783031559754
DOIs
出版狀態Published - 2024
事件7th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2023 - TaiChung, 台灣
持續時間: 18 11月 202319 11月 2023

出版系列

名字Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
557 LNICST
ISSN(列印)1867-8211
ISSN(電子)1867-822X

Conference

Conference7th EAI International Conference on Smart Grid and Internet of Things, SGIoT 2023
國家/地區台灣
城市TaiChung
期間18/11/2319/11/23

指紋

深入研究「Complex Industrial Machinery Health Diagnosis Challenges and Strategies」主題。共同形成了獨特的指紋。

引用此