Remaining useful life prediction of degrading systems subjected to imperfect maintenance: Application to draught fans

Wang, Zhao-Qiang; Hu, Changhua; Si, Xiaosheng; Zio, Enrico

doi:10.1016/j.ymssp.2017.08.016

Cited by 52 publications

(21 citation statements)

References 50 publications

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“…A comparison of Equations (19) and (1) finds that Y (t) follows a nonlinear Wiener process. Thus, based on the fundamental properties of a nonlinear Wiener process [21], a logarithmic likelihood function of µ α , σ α , β, σ B with respect to the extent of performance degradation (Y ) can be obtained as shown in Equation 20.…”

Section: Estimation Of the Parameter µ α σ α β σ Bmentioning

confidence: 99%

“…To facilitate analysis, the optimal maintenance decision method based on RUL predictions for the equipment subject to IM proposed in this study is denoted by M0. The RUL prediction method proposed in [21] is introduced to the maintenance decision model proposed in this study, and the resulting maintenance decision method is denoted by M1. Further analysis finds that the main difference between M0 and M1 lies in that a nonhomogeneous Poisson process and a homogeneous Poisson process are used to depict IM actions performed on the equipment, respectively.…”

Section: Practical Case Analysismentioning

confidence: 99%

“…Additionally, Zheng et al did not analyze system maintenance decisions. In view of the above problems, assuming that the PvM threshold is unknown, Wang et al [21] modeled the cumulative effect of IM actions using a nonlinear Wiener process and a homogeneous Poisson process and thus improved RUL prediction accuracy. Similarly, this method did not study the maintenance decision of the equipment.…”

Section: Introductionmentioning

confidence: 99%

“…In view of the problems in the available studies on optimal maintenance decisions based on RUL predictions for the equipment subject to IM, in this study, based on the RUL prediction method proposed by Wang et al [21], IM actions are described using a nonhomogeneous Poisson process. Additionally, by introducing an upper limit constraint for the number of IM activities, the degradation pattern of the equipment subject to IM is depicted accurately, and RUL prediction accuracy for the equipment subject to IM is effectively improved.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

2020

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Focusing on the fact that the existing research on optimal maintenance decision for remaining useful lifetime (RUL) prediction and imperfect maintenance has low accuracy of RUL prediction and rationality of decision results, an optimal maintenance decision method based on RUL prediction for the equipment subject to imperfect maintenance is proposed in this paper. Firstly, the nonlinear Wiener process is used to characterize the degradation law of the equipment. Secondly, the imperfect maintenance model that meets the upper limit of the maintenance number is established based on the nonhomogeneous Poisson process. Then, based on the concept of the first hitting time, the probability density function (PDF) of the RUL is derived. Finally, based on the RUL prediction results, the optimal maintenance decision model for the equipment subject imperfect maintenance is constructed. Through the example verification and cost parameter sensitivity analysis, the proposed method can effectively improve the accuracy of the RUL prediction and the scientific of maintenance decision results, which has engineering application value.

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Section: Estimation Of the Parameter µ α σ α β σ Bmentioning

confidence: 99%

Section: Practical Case Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

2020

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“…The stochastic process model can describe the stochastic fluctuation of various factors in the degradation process, which accords with the actual operation of the equipment. It not only improves the accuracy of life prediction, but also provides a guarantee for the formulation of later maintenance strategies, therefore, it is widely adopted in the life prediction and maintenance decision-making [8]- [10]. As a monotone stochastic process with independent increments, Inverse Gaussian (IG) process is introduced into the degradation modeling by Wang and Xu [11].…”

Section: Introductionmentioning

confidence: 99%

A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process

Guo

Axita

et al. 2020

IEEE Access

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Condition-based maintenance has been widely used in the maintenance strategy of equipment or systems. Aiming at the maintenance decision-making of the equipment with dynamic degradation characteristics, a dynamic condition-based maintenance model is proposed based on Inverse Gaussian process in this paper. Firstly, an Inverse Gaussian process with stochastic parameter is proposed to describe the change of the equipment degradation characteristics during operation, and the important stochastic properties related to condition-based maintenance is deduced. Secondly, the dynamic maintenance threshold function is proposed, and its different values at different degradation stages can reduce the early failure risk of the equipment while ensuring a lower expected cost ratio. On this basis, a dynamic maintenance decision-making model with multi-objectives is established. A numerical example is illustrated to verify the correctness and practicability of the proposed method, and the sensitivity analysis results of the related parameters prove the necessity of considering the dynamic degradation characteristics of equipment. The comparison result proves that the method proposed in this paper can obtain better safety and economy of maintenance than the method with fixed thresholds. INDEX TERMS Condition-based maintenance, dynamic degradation characteristics, inverse Gaussian process, dynamic maintenance threshold function, dynamic maintenance.

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A novel equipment remaining useful life prediction approach considering dynamic maintenance threshold

Ren,

Li,

et al. 2024

Quality & Reliability Eng

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In conventional remaining useful life (RUL) prediction approaches grounded on maintenance, the maintenance threshold is typically established as a stationary value. However, the actual maintenance threshold may exceed its preset value due to the uncertainty of degradation and other factors. Therefore, it is necessary to consider the dynamic maintenance threshold to improve the precision of remaining useful life prediction. By considering the Wiener process, the maintenance threshold error is introduced to reflect the dynamic nature of the maintenance threshold. The influence of maintenance on degradation amount, degradation rate, and degradation path are comprehensively considered to establish a multi‐stage maintenance‐affected degradation process model. The RUL formula of the equipment is derived using the first hitting time (FHT). The maximum likelihood estimation (MLE) approach and Bayesian theory are employed to estimate the model's parameters. The proposed approach is validated using simulation data and gyroscope degradation data. The outcomes reveal that the proposed approach can significantly enhance the precision of life prediction for the equipment.

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Remaining useful life prediction of degrading systems subjected to imperfect maintenance: Application to draught fans

Cited by 52 publications

References 50 publications

Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

Optimal Maintenance Decision Based on Remaining Useful Lifetime Prediction for the Equipment Subject to Imperfect Maintenance

A Dynamic Condition-Based Maintenance Model Using Inverse Gaussian Process

A novel equipment remaining useful life prediction approach considering dynamic maintenance threshold

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