Fault prediction of drag system using artificial neural network for prevention of dragline failure

Sahu, Ashok Kumar; Palei, Sanjay Kumar

doi:10.1016/j.engfailanal.2020.104542

Cited by 14 publications

(16 citation statements)

References 49 publications

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“…Predictive maintenance is a type of condition-based maintenance carried out based on predictions derived by collecting results from repeated analyses on significant parameters related to the wearing process of items (BS EN 13306:2010). As underlined by Sahu and Palei [59] , including an effective fault prediction process in the preventive maintenance policy can be strategic to significantly reduce failures and downtimes of systems. Opportunistic maintenance.…”

Section: Literature Reviewmentioning

confidence: 99%

A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm

Carpitella

Mzougui

Benítez

et al. 2021

Reliability Engineering & System Safety

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Section: Literature Reviewmentioning

confidence: 99%

A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm

Carpitella

Mzougui

Benítez

et al. 2021

Reliability Engineering & System Safety

View full text Add to dashboard Cite

show abstract

“…Therefore, fault prediction is the key to realizing the transformation of support mode from planned maintenance to predictive maintenance, which can perform the warning before the failure of equipment occurs. Also, more and more attention has been focused on fault prediction for equipment [11][12][13][14][15][16][17][18][19][20][21][22], such as fault prediction for the vehicle [11,12], the watercraft [13][14][15], the aircraft engine [16][17][18], the power supply system [19,20], and the track circuit [21][22][23]. Due to the complexity of watercraft structure and the diversity of the marine environment, it is challenging and difficult to study the fault prediction for watercraft equipment.…”

Section: Introductionmentioning

confidence: 99%

Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

Feng

Chen²,

Guan³

et al. 2022

Front. Phys.

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In this study, we aim to investigate the fault prediction for watercraft equipment by using grey relational analysis. At first, the healthy degree model of watercraft equipment is proposed, and then two main theorems are derived to determine the health condition criteria for equipment. Lastly, the relevant simulation results are provided to verify the validity and accuracy of the healthy degree model. Current results can be helpful to effectively design the supporting mode of watercraft equipment and realize the transformation of watercraft equipment support from planned maintenance to predictive maintenance.

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“…The latter are increasingly successful and prove their efficiency in several fields such as signal processing, process control, fault estimation and detection. ANNs remain a very promising research topic for researchers who wish to improve the performance of these networks and extend their field of application (Mirghaderi, 2020; Sahu and Palei, 2020; Bekkari and Zeddouri, 2019). In the latter case, examples of the use of ANNs in defect classification are presented in numerous research studies where ANN-based classifiers have been successfully used (Loukil et al , 2013; Xie et al , 2010).…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study

Chebira

Benkeblia

Boughaba

et al. 2020

IJQRM

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PurposeThe increasing complexity of industrial systems is at the heart of the development of many fault diagnosis methods. The artificial neural networks (ANNs), which are part of these methods, are widely used in fault diagnosis due to their flexibility and diversification which makes them one of the most appropriate fault diagnosis methods. The purpose of this paper is to detect and locate in real time any parameter deviations that can affect the operation of the blowout preventer (BOP) system using ANNs.Design/methodology/approachThe starting data are extracted from the tables of the HAZOP (HAZard and OPerability) method where the deviations of the parameters of normal BOP operating (pressure, flow, level and temperature) are associated with an initial rule base for establishing cause and effect of relationships between the causes of deviations and their consequences; these data are used as a database for the neural network. Three ANNs were used, the multi-layer perceptron network (MLPN), radial basis functions network (RBFN) and generalized regression neural networks (GRNN). These models were trained and tested, then, their comparative performances were presented. The respective performances of these models are highlighted following their application to the BOP system.FindingsThe performances of the models are evaluated using determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) statistics and time execution. The results of this study show that the RMSE, MAE and R2 values of the GRNN model are better than those corresponding to the RBFN and MLPN models. The GRNN model can be applied with better performance, to establish a diagnostic model that can detect and to identify the different causes of deviations in the parameters of the BOP system.Originality/valueThe performance of the trained network is found to be satisfactory for the real-time fault diagnosis. Therefore, future studies on modeling the BOP system with soft computing techniques can be concentrated on the ANNs. Consequently, with the use of these techniques, the performance of the BOP system can be ensured performing only a limited number of monitoring operations, thus saving engineering effort, time and funds.

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Fault prediction of drag system using artificial neural network for prevention of dragline failure

Cited by 14 publications

References 49 publications

A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm

A risk evaluation framework for the best maintenance strategy: The case of a marine salt manufacture firm

Grey Relational Analysis-Based Fault Prediction for Watercraft Equipment

Fault diagnosis of blowout preventer system using artificial neural networks: a comparative study

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