Digital twin-driven fault diagnosis service of rotating machinery

Wang, Jinjiang; Li, Yilin; Huang, Zuguang; Qiao, Qianzhe

doi:10.1016/b978-0-323-91300-3.00004-8

Cited by 4 publications

(2 citation statements)

References 11 publications

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“…A digital twin is a virtual counterpart of a physical object, system, or process. It can be utilized for various applications, such as predictive maintenance, performance optimization, and decision support [ 3 , 4 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Digital Twin of a Gear Root Crack Prognosis

Matania,

Bechhoefer,

Bortman

2023

Sensors

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Digital twins play a significant role in Industry 4.0, offering the potential to revolutionize machinery maintenance. In this paper, we introduce a new digital twin designed to address the open problem of predicting gear root crack propagation. This digital twin uses signal processing and model fitting to continuously monitor the condition of the root crack and successfully estimate the remaining time until immediate maintenance is required for the physical asset. The functionality of this new digital twin is demonstrated through the experimental data obtained from a planetary gear, where comparisons are made between the actual and estimated severity of the fault, as well as the remaining time until maintenance. It is shown that the digital twin addresses the open problem of predicting gear root crack propagation.

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Section: Theoretical Backgroundmentioning

confidence: 99%

Digital Twin of a Gear Root Crack Prognosis

Matania,

Bechhoefer,

Bortman

2023

Sensors

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show abstract

“…In addition, the number of faulty signals is generally limited. As an alternative, physics-based methods (PBM) and even their digital twin models have been primarily developed to detect and localize unbalance and crack defects [5,6]. PBM provides credible fault detection and diagnostics by capturing and analyzing parameter or system behavior changes [7], which include finite element models (FEM) and other mechanism modeling methods allowing simulation of normal and fault state responses.…”

Section: Introductionmentioning

confidence: 99%