A Digital Twin-Based State Monitoring Method of Gear Test Bench

Li, Jubo; Wang, Songlin; Yang, Jianjun; Zhang, Huijie; Zhao, Hengbo

doi:10.3390/app13053291

Cited by 6 publications

(4 citation statements)

References 47 publications

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“…To enhance the present framework, a thorough, exhaustive study on the coupling of digital and physical systems is required to create a digital twin model. Future research will necessitate the construction of a digital twin of the gear test bench [57] and the simulation of friction and wear behavior on pin-on-disk [58] for establishing, calibrating, diagnosing, predicting, and taking into consideration various length scales, etc.…”

Section: Online and Offline Wear Debris Analysismentioning

confidence: 99%

Experimental Analysis of Chemically Degraded Lubricant’s Impact on Spur Gear Wear

2023

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The impact of lubricating oil degradation, which can happen during both storage and use, on spur gear wear is thoroughly examined in this paper at all scales, from macro to nano. A thorough structure was created by combining the results of testing performed at the macroscale on a test rig and at the nanoscale using tools such as FESEM analysis. Using a single-stage spur transmission under two different working conditions ('40Nm, 1200rpm, 198 hours' and '50Nm, 500rpm, 90 minutes'), macroscale experiments were carried out using both conventional and artificially degraded lubricant oils. Aqueous hydrochloric acid (36.46% v/v concentration) was added to the lubricating lubricant to induce artificial degradation. Wear development and oil degradation were monitored in real time using tools such as metallic wear detritus sensors and oil sensor suites. Offline methods, such as total acid number (TAN) and pH value readings, were used with periodic lubricant oil samples. It was discovered through both online and offline monitoring methods that the spur gear experienced substantial wear in lubricant-degraded conditions. By demonstrating elevated levels of oxidative degradation products and the formation of new oxidative compounds, such as sulphates and aromatic groups, the attenuated total reflection-Fourier transform infrared spectroscopy (ATR-FTIR) analysis of the oil degradation at the nanoscale verified this. In addition, water creation was noted. It is interesting to note that the pH meter has become a viable option for detecting oil degradation and offers a useful way to keep track of lubricant quality. Overall, this study shows that there is a strong connection between macroscale and nanoscale phenomena in the complete method created for assessing the tribological performance of gear lubricants.

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Section: Online and Offline Wear Debris Analysismentioning

confidence: 99%

Experimental Analysis of Chemically Degraded Lubricant’s Impact on Spur Gear Wear

2023

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“…Liu et al [ 50 ] highlighted the process of offline data collection for a ship structure bearing monitoring system but did not realize real-time monitoring and diagnosis capabilities. Li et al [ 51 ] presented a condition monitoring method for a gear test bench-based DT using real-time data; however, this method only realizes condition monitoring based on real-time data, and it cannot complete further data mining analysis.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for Reducers Based on a Digital Twin

Liu,

Han,

Zheng

et al. 2024

Sensors

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A new method based on a digital twin is proposed for fault diagnosis, in order to compensate for the shortcomings of the existing methods for fault diagnosis modeling, including the single fault type, low similarity, and poor visual effect of state monitoring. First, a fault diagnosis test platform is established to analyze faults under constant and variable speed conditions. Then, the obtained data are integrated into the Unity3D platform to realize online diagnosis and updated with real-time working status data. Finally, an industrial test of the digital twin model is conducted, allowing for its comparison with other advanced methods in order to verify its accuracy and application feasibility. It was found that the accuracy of the proposed method for the entire reducer was 99.5%, higher than that of other methods based on individual components (e.g., 93.5% for bearings, 96.3% for gear shafts, and 92.6% for shells).

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“…Monitoring systems play a crucial role in today's industrial landscape, contributing to safety and sustainable development. [1] By using digital twins to monitor gear transmissions, it was discovered that the proposed monitoring system can simulate realworld gear performance and provide high-precision monitoring. [2]The use of vibration indicators for assessing gear wear revealed the ability to quickly detect gear damage.…”

Section: Introductionmentioning

confidence: 99%

Development of Defect Detection Models for Standard and Modified Gear Tooth Surfaces based on FFT/DWT

Huang,

Wu,

Lai

2024

Preprint

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Gears play a pivotal role in power transmission systems. Over prolonged periods of high-load transmission, the issue of gear lifespan has become a challenging one to address. Monitoring systems are vital in today's smart manufacturing landscape, contributing to safety and sustainable development. Presently, there is a dearth of research focused on the vibration of gears with surface modification. Consequently, this study has devised a novel defect detection model for surface modification gears. It employs time-domain, frequency-domain, and discrete wavelet transform (DWT) analysis in combination with deep neural network (DNN) deep learning to validate the effectiveness of this approach. The study deals with gears in two states: undamaged and wear. Vibration data was sampled for 800 seconds at a rate of 1024 Hz. Leveraging Artificial Intelligence, this research opens the door to innovative applications and services in the field of gear manufacturing. Notably, DWT exhibits a significant reduction in the amplitude of vibrations between surface modification helical gears and surface modification spur gears. Furthermore, from the DNN training and accuracy, it is evident that there is no substantial difference in accuracy between epochs 30 and epochs 100.

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A Digital Twin-Based State Monitoring Method of Gear Test Bench

Cited by 6 publications

References 47 publications

Experimental Analysis of Chemically Degraded Lubricant’s Impact on Spur Gear Wear

Experimental Analysis of Chemically Degraded Lubricant’s Impact on Spur Gear Wear

Fault Diagnosis for Reducers Based on a Digital Twin

Development of Defect Detection Models for Standard and Modified Gear Tooth Surfaces based on FFT/DWT

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