Application of Hidden Markov Models in Ball Mill Gearbox for Fault Diagnosis

Zheng, Rong; Dong, Xin; Hao, Wang Sheng; Li, Yan; Wang, Rui Xin

doi:10.4028/www.scientific.net/amr.842.401

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…The machine learning method [122] for fault diagnosis was based on HMM to model the progress of mechanical faults, to obtain the wear status of gear and classify them. Zheng et al [123] took the ball mill gear reducer as the research object, based on the HMM model. A method for fault diagnosis of HMM was studied, and five kinds of performance degradation model diagnosis experiments were carried out.…”

Section: Hidden Markov Model For Prediction Of Gear Remainingmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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As a transmission component, the gear has been obtained widespread attention. The remaining useful life (RUL) prediction of gear is critical to the prognostics health management (PHM) of gear transmission systems. The digital twin (DT) provides support for gear RUL prediction with the advantages of rich health information data and accurate health indicators (HI). This paper reviews digital twin-driven RUL prediction methods for gear performance degradation, from the view of digital twin-driven physical model-based and virtual model-based prediction method. From the view of physical model-based one, it includes prediction model based on gear crack, gear fatigue, gear surface scratch, gear tooth breakage, and gear permanent deformation. From the view of digital twin-driven virtual model-based one, it includes non-deep learning methods, and deep learning methods. Non-deep learning methods include wiener process, gamma process, hidden Markov model (HMM), regression-based model, proportional hazard model. Deep learning methods include deep neural networks (DNN), deep belief networks (DBN), convolutional neural networks (CNN), and recurrent neural networks (RNN), etc. It mainly summarizes the performance degradation and life test of various models in gear, and evaluates the advantages and disadvantages of various methods. In addition, it encourages future works.

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Section: Hidden Markov Model For Prediction Of Gear Remainingmentioning

confidence: 99%

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Liu

Dong

2021

Journal of Computing and Information Science in Engineering

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Fault Diagnosis for Body-in-White Welding Robot Based on Multi-Layer Belief Rule Base

et al. 2023

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Fault diagnosis for body-in-white (BIW) welding robots is important for ensuring the efficient production of the welding assembly line. As a result of the complex mechanism of the body-in-white welding robot, its strong correlation of components, and the many types of faults, it is difficult to establish a complete fault diagnosis model. Therefore, a fault diagnosis model for a BIW-welding robot based on a multi-layer belief rule base (BRB) was proposed. This model can effectively integrate monitoring data and expert knowledge to achieve an accurate fault diagnosis and facilitate traceability. First, according to the established fault tree, a fault mechanism was determined. Second, based on the multi-layer relationship of a fault tree, we established a multi-layer BRB model. Meanwhile, in order to improve the accuracy of the model parameters, the projection covariance matrix adaptive evolutionary strategy (P-CMA-ES) algorithm was used to optimize and update the parameters of the fault diagnosis model. Finally, the validity of the proposed model was verified by a simulation experiment for the BIW-welding robot.

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Application of Hidden Markov Models in Ball Mill Gearbox for Fault Diagnosis

Cited by 2 publications

References 3 publications

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Digital Twin-Driven Remaining Useful Life Prediction for Gear Performance Degradation: A Review

Fault Diagnosis for Body-in-White Welding Robot Based on Multi-Layer Belief Rule Base

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