A new remaining useful life estimation method for equipment subjected to intervention of imperfect maintenance activities

Hu, Changhua; Pei, Hong; Wang, Zhaoqiang; Si, Xiaosheng; Zhang, Zhengxin

doi:10.1016/j.cja.2018.01.009

Cited by 53 publications

(32 citation statements)

References 25 publications

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“…Y (t) represents the equivalent extent of performance degradation of the equipment. 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%

“…This reduces RUL prediction accuracy and affects the scientific basis of strategies. To further improve RUL prediction accuracy for the equipment subject to IM, Hu et al [19] constructed a degradation model for the equipment subject to IM actions based on a nonlinear Wiener diffusion process and analyzed the effect of IM on both the extent and rate of degradation. On this basis, they expanded the scope of application of the method and improved prediction accuracy.…”

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: Introductionmentioning

confidence: 99%

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

2020

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“…First, to model the efficiency of past-dependent imperfect repairs, they have based on the repair number-based approach rather than on the deterioration-level-based approach (see also section ''Introduction''). Second, the main aim of Hu and colleagues 13,14 is to estimate the remaining useful life of condition-based maintained systems, while our aim is to evaluate and optimize the CBIM model.…”

Section: Practicality Of the Proposed Condition-based Maintained Systemmentioning

confidence: 99%

A condition-based maintenance model with past-dependent imperfect preventive repairs for continuously deteriorating systems

Huynh

Grall

2019

Proceedings of the Institution of Mechanical Engineers, Part O:

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Most condition-based imperfect maintenance models developed over the last few decades are memoryless in the sense that maintenance efficiency is completely [Formula: see text]-independent of previous interventions. However, many maintenance activities exhibit their past dependency in engineering practice, and this significant property should not be ignored in maintenance modeling. In this spirit, our aim is to develop a condition-based maintenance model for continuously deteriorating systems subject to a special kind of past-dependent imperfect repairs. Such a repair can put the system back to a deterioration level better than the one at just before the current repair, but worse than the one reached at the last repair. Besides, inspection and replacement are memoryless actions available for the system. They result in different effects on the system deterioration and incur different costs. To achieve high economic performances in the long term, these actions are coordinated into a control-limit deterioration-based maintenance policy. Its long-run maintenance cost rate is analytically evaluated using the semi-regenerative process theory. Numerous sensitivity studies to maintenance costs and to system characteristics give a thorough understanding about the policy behavior. Furthermore, comparisons with more classical policies justify the importance of incorporating the past dependency in maintenance modeling.

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“…A typical deep learning framework consists of four phases: data acquisition and processing, feature extraction and calculation, learning model building, and prediction. In today's big data era, the premise of accurate bearing RUL prediction is to extract as much effective information as possible from massive amounts of monitoring data [24]. However, the data are increasingly complicated and high-dimensional.…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

Tian

Wang

2020

Applied Sciences

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The prediction of the remaining life of a bearing plays a vital role in reducing the accident-related maintenance costs of machinery and in improving the reliability of machinery and equipment. To predict bearing remaining useful life (RUL), the abilities of statistical characteristics to reflect the bearing degradation state differ, and the single prediction model has low generalization ability and a poor prediction effect. An ensemble robust prediction method is proposed here to predict bearing RUL based on the construction of a bearing degradation indicator set: the initial bearing degradation indicator subsets were constructed using the Fast Correlation-Based Filter with Approximate Markov Blankets (FCBF-AMB) and Maximal Information Coefficient (MIC) selection methods. Through the cross-operation of the obtained subsets, we obtained a set of robust degradation indicators. These selected degradation indicators were fed into the long short-term memory (LSTM) neural network prediction model enhanced by the AdaBoost algorithm. We found through calculation that the average prediction accuracy of the proposed method is 91.40%, 92.04%, and 93.25% at 2100, 2250, and 2400 rpm, respectively. Compared with other methods, the proposed method improves the prediction accuracy by 1.8% to 14.87% at most. Therefore, the method proposed in this paper is more accurate than the other methods in terms of RUL prediction.

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A new remaining useful life estimation method for equipment subjected to intervention of imperfect maintenance activities

Cited by 53 publications

References 25 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 condition-based maintenance model with past-dependent imperfect preventive repairs for continuously deteriorating systems

An Ensemble Learning and RUL Prediction Method Based on Bearings Degradation Indicator Construction

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