A one-class SVM based approach for condition-based maintenance of a naval propulsion plant with limited labeled data

Tan, Yulin; Niu, Chunyang; Tian, Hui; Hou, Liangsheng; Zhang, Jundong

doi:10.1016/j.oceaneng.2019.106592

Cited by 30 publications

(12 citation statements)

References 19 publications

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“…(2016). Moreover, CBM method research works on a dataset similar to the one mentioned in this paper are done by Tan et al. (2019) and Cipollini et al.…”

Section: Naval Propulsion System Dataset and Maintenance Methodsmentioning

confidence: 99%

DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

Gao

Dong

Cui

et al. 2022

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PurposeThe purpose of this paper is to propose a new deep learning-based model to carry out better maintenance for naval propulsion system.Design/methodology/approachThis model is constructed by integrating different deep learning algorithms. The basic idea is to change the connection structure of the deep neural network by introducing a residual module, to limit the prediction output to a reasonable range. Then, connect the Deep Residual Network (DRN) with a Generative Adversarial Network (GAN), which helps achieve data expansion during the training process to improve the accuracy of the assessment model.FindingsStudy results show that the proposed model achieves a better prediction effect on the dataset. The average performance and accuracy of the proposed model outperform the traditional models and the basic deep learning models tested in the paper.Originality/valueThe proposed model proved to be better performed naval propulsion system maintenance than the traditional models and the basic deep learning models. Therefore, our model may provide better maintenance advice for the naval propulsion system and will lead to a more reliable environment for offshore operations.

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“…(2016). Moreover, CBM method research works on a dataset similar to the one mentioned in this paper are done by Tan et al. (2019) and Cipollini et al.…”

Section: Naval Propulsion System Dataset and Maintenance Methodsmentioning

confidence: 99%

DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

Gao

Dong

Cui

et al. 2022

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“…It is also noteworthy that some studies develop the effect of missing data on their predictions due to data collection on this type of platform [55][56][57]. One class of SVM models [58,59] is of special relevance and application in naval assets PdM.…”

Section: State Of the Artmentioning

confidence: 99%

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

et al. 2021

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Predictive maintenance has lately proved to be a useful tool for optimizing costs, performance and systems availability. Furthermore, the greater and more complex the system, the higher the benefit but also the less applied: Architectural, computational and complexity limitations have historically ballasted the adoption of predictive maintenance on the biggest systems. This has been especially true in military systems where the security and criticality of the operations do not accept uncertainty. This paper describes the work conducted in addressing these challenges, aiming to evaluate its applicability in a real scenario: It presents a specific design and development for an actual big and diverse ecosystem of equipment, proposing an semi-unsupervised predictive maintenance system. In addition, it depicts the solution deployment, test and technological adoption of real-world military operative environments and validates the applicability.

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“…Tan et al (2019) show the use of a single class support vector machine to assess the need for the maintenance based on condition of CODLAG propulsion system. Their research demonstrates the ability to use a machine learning algorithm to determine the need for maintenance of such a system [17]. attempt to determine the decay coefficients of the CODLAG propulsion system in the same manner using a MLP.…”

Section: State Of the Artmentioning

confidence: 99%

Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

Baressi Šegota,

Štifanić,

Ohkura

et al. 2020

JMTS

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An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99.

show abstract

A one-class SVM based approach for condition-based maintenance of a naval propulsion plant with limited labeled data

Cited by 30 publications

References 19 publications

DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

DRN-GAN: an integrated deep learning-based health degradation assessment model for naval propulsion system

SOPRENE: Assessment of the Spanish Armada’s Predictive Maintenance Tool for Naval Assets

Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

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