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Smart Grid and Internet of Things. 7th EAI International Conference, SGIoT 2023, TaiChung, Taiwan, November 18-19, 2023, Proceedings

Research Article

Complex Industrial Machinery Health Diagnosis Challenges and Strategies

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-55976-1_13,
        author={Hsiao-Yu Wang and Ching-Hua Hung},
        title={Complex Industrial Machinery Health Diagnosis Challenges and Strategies},
        proceedings={Smart Grid and Internet of Things. 7th EAI International Conference, SGIoT 2023, TaiChung, Taiwan, November 18-19, 2023, Proceedings},
        proceedings_a={SGIOT},
        year={2024},
        month={3},
        keywords={Root Mean Square Statistical overlap factor Ensemble Empirical Mode Decomposition Bayesian regularization},
        doi={10.1007/978-3-031-55976-1_13}
    }
    
  • Hsiao-Yu Wang
    Ching-Hua Hung
    Year: 2024
    Complex Industrial Machinery Health Diagnosis Challenges and Strategies
    SGIOT
    Springer
    DOI: 10.1007/978-3-031-55976-1_13
Hsiao-Yu Wang1,*, Ching-Hua Hung1
  • 1: Department of Mechanical Engineering, National Yang Ming Chiao Tung University
*Contact email: shon0808@gmail.com

Abstract

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.

Keywords
Root Mean Square Statistical overlap factor Ensemble Empirical Mode Decomposition Bayesian regularization
Published
2024-03-15
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-55976-1_13
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