Multi-branch Hidden semi-Markov modeling for RUL prognosis

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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“…In other fields, there have been considerable studies using this method to evaluate life, which can be referred to Refs. [129][130][131].…”

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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“…21,22 Recently, it has recently been introduced for the CBM framework. 23,24 An example of a left-right MB-HMM model is illustrated in Figure 2.…”

Section: General Principlesmentioning

confidence: 99%

Multi-branch hidden Markov models for remaining useful life estimation of systems under multiple deterioration modes

Chatelain

Bérenguer

2016

Proceedings of the Institution of Mechanical Engineers, Part O:

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International audienceRemaining Useful Life (RUL) estimation plays an important role in implementing a condition-based maintenance (CBM) program, since it could provide sufficient time for maintenance crew to act before an actual system failure. This prognostic task becomes harder when several deterioration mechanisms co-exist within the same system due to the variability and dynamics of its operating environment, since the RUL obviously depends on the mode that the system is following. In this paper, we propose a multi-branch modeling framework to deal with such problems. The proposed model consists of several branches in which each one represents a deterioration mode and is considered as a hidden Markov model. The system’s conditions are modeled by several discrete meaningful states, such as “good”, “minor defect”, “maintenance required” and “failure”, which would be easy to interpret for maintenance personnel. Furthermore, these states are considered to be “hidden” and can only be revealed through observations. These observations are the condition monitoring information in the CBM context. The performance of the proposed model is evaluated through numerical studies. The results show that the multi-branch model can outperform the standard one-branch HMM model in RUL estimation, especially when the “distance” between the deterioration modes is considerable

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“…Although, in Le et al 25 the RUL prognostic offers a multi-branch HMM in which different models are used to represent different operating conditions. This is a motivational work, but there are some limitations.…”

Section: Introductionmentioning

confidence: 99%

RUL management by production reference loopback

Shahin

Simon

Weber

2022

Proceedings of the Institution of Mechanical Engineers, Part O:

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Online remaining useful life (RUL) assessment is a significant asset in prognostic and health management (PHM) in many industrial domains where safety, reliability, and cost reduction are of high importance. It is not easy to predict the breakdown state of a system when it operates under multiple operating conditions, because system degradation varies with the dynamics of the operations. This paper presents an Input-Output Hidden Markov Model (IOHMM) that estimates the RUL in real time based on available measurements. The model learns the impact of the operating condition on the RUL and allows to manage the system RUL by changing the corresponding operating conditions. A reference managing algorithm is presented to match the estimated RUL to a given target RUL. In addition, well-known algorithms are adapted from HMM to IOHMM and are used for model training and health state diagnostics. A numerical application is proposed to show the importance of obtaining good predictions from a limited amount of data sequences. Specifically, since degradation is a slow process, it is difficult to have a large amount of data sequences in order to predict the RUL more accurately until the failure. Therefore, the bootstrap method with data resampling and replacement is used to train the IOHMM model to improve estimation accuracy.

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Multi-branch Hidden semi-Markov modeling for RUL prognosis

Cited by 13 publications

References 19 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

Multi-branch hidden Markov models for remaining useful life estimation of systems under multiple deterioration modes

RUL management by production reference loopback

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