Method for predicting depth-averaged current velocities of underwater gliders based on data feature analysis

Zhou, Yaojian; Liu, Shijie; Zhang, Yonglai; Tian, Baoqiang

doi:10.1063/5.0058318

Cited by 3 publications

(1 citation statement)

References 26 publications

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“…Random forest regression can be regarded as a strong predictor integrating many weak predictors. Since the three common construction methods for the random forest, including ID3, CART, and C4.5, are top-down greedy algorithms [25], one of the most important bases for establishing a random forest model is the minimum mean square deviation.…”

Section: Random Forest Algorithmmentioning

confidence: 99%

Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm

et al. 2021

Sensors

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Owing to the fact that large-scale peak-load-regulation nuclear power turbine units’ thermal signal is greatly influenced by background noise and has non-stationary and nonlinear characteristics, this paper proposes a new fault diagnosis method for thermal sensors based on an improved independent component analysis (Improved-ICA) algorithm and random forest (RF) algorithm. This method is based on independent component analysis (ICA), which is not capable of extracting components independently. Therefore, we propose the use of the maximum approximate information negative entropy optimization model in order to improve the ICA algorithm’s independent principal component extraction ability and obtain better non-Gaussian physical source signal separation results. The improved ICA algorithm is used for the blind source separation of the thermal parameters of peak-load-regulation nuclear power units. A series of stationary physical source functions and a series of non-stationary noise signals are obtained. Then, according to the specific signal format and data volume of the nuclear power parameter signal, the network parameters of the random forest algorithm are determined, giving rise to the fault diagnosis model. Finally, the real-time operation data of an 1121 MW nuclear power unit are used to complete the training and fault diagnosis of the random forest network and analyze the diagnosis results. The results indicate that the model can effectively mine the abnormal sample points of thermal parameters and classify the fault type of the thermal sensor during peak load operation of the nuclear power unit. The accuracy rate is found to be at the threshold of 99%.

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Section: Random Forest Algorithmmentioning

confidence: 99%

Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm

et al. 2021

Sensors

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A data-driven approach for predicting depth-averaged velocities in the early stages of underwater glider navigation

Li,

Zhou,

Zhao

et al. 2024

Ocean Engineering

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Data-driven depth-averaged current prediction methods for underwater gliders with sailing parameters

2023

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The study of depth-averaged currents is of great significance for the application of underwater gliders. In order to solve the problem of low prediction accuracy of the time series-based depth-averaged current prediction method, the factors affecting the prediction of depth-averaged currents are analyzed and a data-driven prediction method for depth-averaged currents of an underwater glider with sailing parameters is proposed in this paper. First, depth-averaged currents of the underwater glider’s historical profile period and navigation parameters of the underwater glider are taken as inputs to construct multi-input and double-output characteristics. Then, based on the two sets of the real sea trial data and two groups of the generic set of evaluation criteria, five different data-driven methods are used to predict depth-averaged currents. Experimental results show that the prediction result of depth-averaged currents of an underwater glider driven by data with sailing parameters is better than that based on time series, and the prediction accuracy of depth-averaged currents of a future profile period is improved.

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Method for predicting depth-averaged current velocities of underwater gliders based on data feature analysis

Cited by 3 publications

References 26 publications

Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm

Peak-Load-Regulation Nuclear Power Unit Fault Diagnosis Using Thermal Sensors Combined with Improved ICA-RF Algorithm

A data-driven approach for predicting depth-averaged velocities in the early stages of underwater glider navigation

Data-driven depth-averaged current prediction methods for underwater gliders with sailing parameters

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