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

Chen, Yunxiang; Wang, Zezhou; Cai, Zhongyi

doi:10.1109/access.2019.2963765

Cited by 14 publications

(4 citation statements)

References 21 publications

(25 reference statements)

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“…Increasing the budget and reducing the transportation volume requirement mitigate the diminishing effect. Chen et al 57 proposes an optimal maintenance decision method based on remaining useful lifetime (RUL) prediction for equipment undergoing imperfect maintenance. The degradation law is characterized using the nonlinear Wiener process, and an imperfect maintenance model is established.…”

Section: Strategic Choice Of Maintenancementioning

confidence: 99%

Strategical selection of maintenance type under different conditions

Hamasha,

Bani-Irshid,

Al Mashaqbeh

et al. 2023

Sci Rep

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Selecting the appropriate maintenance type is a challenging task that involves multiple criteria working together. This decision has a significant impact on the organization and its overall market sustainability. The primary categorization of maintenance consists of two main types: corrective maintenance and preventive maintenance. All other classifications are encompassed within these two categories. For instance, preventive maintenance can be further classified as either predictive maintenance or periodic maintenance. Given the importance of this decision, this paper discusses the optimal maintenance type under different conditions. The scale of the business, the cost of machine failure, the effect of machine failure on the production schedule, the effect of machine failure on worker safety and the workplace environment, the availability of spare parts, the lifespan of the machine, and the manufacturing process are some of the factors that are covered in this paper. This paper primarily aims to present a comprehensive literature review concerning the strategic decision-making process for selecting the appropriate maintenance type under varying conditions. Additionally, the paper incorporates various models and visual aids within its content to facilitate and guide the decision-making procedure. Corrective maintenance is usually necessary in the case of small companies, significant impact on business or production plans due to failures, potential risks to public safety, ready availability of spare parts, and when production processes are not interdependent. If these parameters are not met, preventive maintenance can be a better option. Since these circumstances frequently do not occur simultaneously, it is imperative for the business to give them significant consideration.

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Section: Strategic Choice Of Maintenancementioning

confidence: 99%

Strategical selection of maintenance type under different conditions

Hamasha,

Bani-Irshid,

Al Mashaqbeh

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Network lifetime is a key design consideration for battery-powered U-WSNs. There exist number of research focus on the lifetime maximization issues [49,50] or on the prediction of the remaining lifetime [51,52]. Other research focus on the energy-efficiency issues so as to extend the network lifetime [53][54][55][56], and the trajectory planning of the UAV is one of the most consideration among these schemes [30,57,58].…”

Section: Network Lifetimementioning

confidence: 99%

Non-Terrestrial Networks-Enabled Internet of Things: UAV-Centric Architectures, Applications, and Open Issues

Kacimi

Liu³

et al. 2022

Drones

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Although Unmanned Aerial Vehicles (UAVs)-aided wireless sensor networks (WSNs) have gained many applications, it is not for long that research works have been produced to define effective algorithms and protocols. In this article, we address the UAV-enabled WSN (U-WSN), explore the performance and the capability of the UAV, define the UAV functionalities as a communication node, and describe the architectures and the relevant typical technologies that emerge from this new paradigm. Furthermore, this article also identifies the main factors which influence the U-WSN design and analyzes the open issues and challenges in U-WSN. These insights may serve as motivations and guidelines for future designs of UAV-enabled WSNs.

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“…In order to enhance the management and maintenance capability of the railway administration, the Predictive Maintenance (PdM) concept [13], which has been investigated by many researchers and industrial manufacturers, offers an effective solution to deploy maintenance more cost effectively. Great effort has been made to develop novel fault detection, reliability prediction, and Remaining Useful Life (RUL) identification algorithms [14][15][16][17][18]. A conventional measure to realize PdM is the involvement of additional sensors and monitoring functions to comprehensively and timely evaluate the fault probability and trend, which has been successfully applied in many industrial fields, as in [19] and [20].…”

Section: Introductionmentioning

confidence: 99%

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

Liu

Cai

et al. 2023

Machines

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On-board train control equipment is an important component of the Train Control System (TCS) of railway trains. In order to guarantee the safe and efficient operation of the railway system, Predictive Maintenance (PdM) is significantly required. The operation data of the on-board equipment allow us to build fault prediction models using a data-driven approach. However, the problem of unbalanced fault samples makes it difficult to achieve the expected modeling performance. In this paper, a Conditional Generative Adversarial Network (CGAN) is adopted to solve the unbalancing problem by generating synthetic samples corresponding to specific fault labels that belong to the minority classes. With this basis, a CGAN-enhanced eXtreme Gradient Boosting (XGBoost) solution is presented for training the fault prediction models. From the pre-processing to the field data, artificial fault samples are generated and integrated into the training sample sets, and the XGBoost models can be derived with multiple decision trees. Both the feature importance sequence list and the knowledge graph are derived to describe the characteristics obtained by the models. Filed data sets from practical operation are utilized to validate the proposed solution. By comparison with conventional machine learning algorithms, it can be found that higher accuracy, precision, recall, and F1 scores, which are up to 99.76%, can be achieved by the proposed solution. By involving the CGAN strategy, the maximum enhancement to the F1 score with the XGBoost approach reaches 6.13%. The advantages of the proposed solution show great potential in implementing equipment health management and intelligent condition-based maintenance.

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

Cited by 14 publications

References 21 publications

Strategical selection of maintenance type under different conditions

Strategical selection of maintenance type under different conditions

Non-Terrestrial Networks-Enabled Internet of Things: UAV-Centric Architectures, Applications, and Open Issues

Fault Prediction of On-Board Train Control Equipment Using a CGAN-Enhanced XGBoost Method with Unbalanced Samples

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