Remaining useful life prediction for bivariate deteriorating systems under dynamic operational conditions

Sun, Fuchun; Guo, Hongxuan; Wang, Ning; Zhang, Wei

doi:10.1002/qre.3023

Cited by 6 publications

(6 citation statements)

References 41 publications

(88 reference statements)

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“…These models can track the degradation stage and adaptively update model parameters, thereby improving the accuracy of RUL prediction. Sun et al [22] used a covariance model to fit the degradation process and implemented joint RUL prediction for binary systems based on the Copula function. Li et al [23] established an improved stochastic degradation model that adapts to the variability of indicator degradation trends by updating model parameters.…”

Section: Introductionmentioning

confidence: 99%

The RUL prediction based on improved Wiener degradation model for wet friction components

Wu,

Li,

Wang

et al. 2024

Meas. Sci. Technol.

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The wet clutch is a critical component of the comprehensive transmission system, and its reliability determines the stability of heavy equipment operation. As the core component of the wet clutch, the life of the wet friction components directly affects the reliability of the wet clutch. To accurately predict the wet friction components remaining useful life (RUL), an improved Wiener degradation model is established. Compared to traditional models, it sets drift parameters as random parameters to consider the randomness of degradation process. The life and degradation data of wet friction components are obtained through accelerated life test, and the composite degradation indicator is constructed by screening and fusing degradation data. Innovatively, we fuse life and degradation data to reduce early errors in RUL prediction of wet friction components. The result shows that the RUL prediction average absolute error of the improved Wiener degradation model is 0.31h in the accelerated life range of 80h to 110h, which verifies the accuracy of the model.

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Section: Introductionmentioning

confidence: 99%

The RUL prediction based on improved Wiener degradation model for wet friction components

Wu,

Li,

Wang

et al. 2024

Meas. Sci. Technol.

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“…Nevertheless, most existing studies have been exclusively concerned with the HI extraction problem for static systems, that is, the monitored data changes slowly in the fault free case 17,18 . In many practical situations, it is quite common for measurements to vary dynamically over time, causing difficulties in both defining HIs and extracting them 19–21 …”

Section: Introductionmentioning

confidence: 99%

“…17,18 In many practical situations, it is quite common for measurements to vary dynamically over time, causing difficulties in both defining HIs and extracting them. [19][20][21] Unfortunately, to the best of our knowledge, research on HI extraction for dynamic systems, such as linear stochastic systems, is still lacking. This gap is the main motivation for this paper.…”

Section: Introductionmentioning

confidence: 99%

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

Huai,

Gao,

Liu

et al. 2024

Quality & Reliability Eng

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In this paper, the problem of fault prognosis is investigated for linear stochastic systems with intermittent faults (IFs) and strong noise. Different from permanent faults, the degradation state of IFs are related to the fault duration time, leading to difficulties in the first step of fault prognosis, that is, the health indicator (HI) extraction. Generally, HI extraction of IFs is achieved by fault detection techniques. Therefore, the proposed fault prognosis approach mainly covers the following steps. First, the HI and degradation model are introduced for linear stochastic systems with IFs. Then, considering dynamic characteristic of systems and influence of strong noise, an IF detection algorithm under strong noise is proposed using weighted moving average methods to extract the HI. Moreover, an alternative HI extraction approach is given by an HI estimation way, which avoids using fault detection techniques. Subsequently, the degradation model is fitted by virtue of HIs and the least square method, and the remaining useful life is predicted with the aid of the obtained model. Finally, an experiment concerning the rotary steerable drilling tool system is provided to verify the effectiveness of the derived results.

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“…Sun et al. proposed a bivariate degradation modeling method considering the influence of the dynamic operating conditions, which based on the Bayesian dynamic model and a joint RUL distribution prediction method 16 . Zhou and Gebraeel proposed a nonparametric model that estimates the evolution of degradation signals under different environments based on the flexibility of the cubic B‐spline basis 17 .…”

Section: Introductionmentioning

confidence: 99%

“…15 Sun et al proposed a bivariate degradation modeling method considering the influence of the dynamic operating conditions, which based on the Bayesian dynamic model and a joint RUL distribution prediction method. 16 Zhou and Gebraeel proposed a nonparametric model that estimates the evolution of degradation signals under different environments based on the flexibility of the cubic B-spline basis. 17 Giesecke et al established a Bayesian confidence network to predict blade life loss in combination with engine service parameters and atmospheric environment conditions in the operating area to assist in decision-making blade repair levels.…”

Section: Introductionmentioning

confidence: 99%

A cumulative exposure model for remaining useful life prediction of pitot tube based on dynamic operating data

Wang

Sun

Chen

et al. 2023

Quality & Reliability Eng

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The data measured by the pitot tube (PT) is related to airspeed indication and flight safety. However, there are few methods to solve the problem of failure prediction and predictive maintenance of PT. This study proposes a method for predicting the remaining useful life (RUL) of the PT based on dynamic operating data. First, an exposure index (EI) characterizing the severity level of operating conditions is proposed based on multiple dynamic operating and environmental parameters, then a cumulative exposure model (CEM) is developed to calculate the cumulative exposure. Dynamic covariate information is incorporated into the time-to-failure distribution of PT through the CEM, the RUL distribution of the individual PT is then obtained by Monte Carlo simulation. A case study is carried out based on actual data of a commercial aircraft fleet and shows that the proposed CEM can effectively use historical aircraft environmental information and truncated failure data of the components. The relative error of the remaining life prediction is substantially improved compared to the traditional reliability analysis methods, which means that the proposed model can provide more reliable RUL results based on historical operating conditions, providing better support for PT risk assessment and condition-based maintenance decisions.

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Remaining useful life prediction for bivariate deteriorating systems under dynamic operational conditions

Cited by 6 publications

References 41 publications

The RUL prediction based on improved Wiener degradation model for wet friction components

The RUL prediction based on improved Wiener degradation model for wet friction components

Fault prognosis for linear stochastic systems with intermittent fault and strong noise via health indicator extraction approach

A cumulative exposure model for remaining useful life prediction of pitot tube based on dynamic operating data

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