Research on Intelligent Diagnosis Method for Large-Scale Ship Engine Fault in Non-Deterministic Environment

Feng, Donghua; Li, Yahong

doi:10.1515/pomr-2017-0123

Cited by 8 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When sparrows realize that their environment is dangerous, they will engage in anti-predatory behavior and want to leave their current position. The formula for updating the position of the entire sparrow population is shown in (4).…”

Section: B Model Of the Ssamentioning

confidence: 99%

“…With the development of intelligent ships, algorithms such as Convolutional neural network (CNN) [1], Backpropagation neural network (BPNN) [2], Deep learning algorithm [3], Artificial neural network (ANN) [4], and Extreme learning machine (ELM) [5] have produced positive results in the field of fault diagnosis. Among them, support vector machine (SVM) is rooted in statistical learning theory and optimization methods, with a simple structure that can effectively solve problems such as overfitting, local optima, and low convergence rate, greatly reducing the computational complexity of subsequent parameter optimization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

Wang,

Cao,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

The key to reducing operation and maintenance costs and improving reliability is to evaluate the condition and fault detection methods of ship ballast water systems. To reduce the impact of support vector machine (SVM) parameter uncertainty and improve the accuracy of ship ballast water system fault diagnosis models, this paper proposes a multi-strategy collaborative improved sparrow search algorithm (ISSA) to optimize the parameters of SVM for fault diagnosis. First, ISSA initializes the population using tent chaotic maps to improve spatial distribution uniformity and stabilize the population quality. Second, the discoverer in the algorithm adopts an adaptive weighting strategy to avoid falling into local optima and accelerate the convergence speed. Finally, the follower position introduces mutation strategies and chaotic perturbations to enhance local evolution capabilities and improve the utilization of search areas. The experimental results show that compared with sparrow search algorithm (SSA), particle swarm optimization algorithm (PSO), Genetic Algorithm (GA), whale optimization algorithm (WOA), and grey wolf optimization algorithm (GWO), ISSA can obtain better optimal solutions at the fastest convergence rate on the benchmark test function. On the dataset of the ship ballast water system, the ISSA-SVM fault diagnosis model proposed in this paper has the best classification performance, with an average accuracy of 97.44%. Compared with the other two models, ISSA-SVM is 7.55% and 0.83% higher than SVM's 89.89% and SSA-SVM's 96.61%.

show abstract

Section: B Model Of the Ssamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

Wang,

Cao,

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In recent years, with the development of artificial intelligence, big data, and the Internet-of-things technology, intelligent ship [ 1 ] has become an inevitable trend in the field of ship manufacturing and shipping industry. The development of intelligent ship is closely related to the intellectualization of main engine [ 2 , 3 ], generator diesel engine [ 4 ], air compressor [ 5 ], and other important machinery of ships. At the same time, the basis of intellectualization of the machinery is continuous and accurate working condition monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring

Zuo

Dong

et al. 2021

Micromachines

View full text Add to dashboard Cite

With the development of intelligent ship, types of advanced sensors are in great demand for monitoring the work conditions of ship machinery. In the present work, a self-powered and highly accurate vibration sensor based on bouncing-ball triboelectric nanogenerator (BB-TENG) is proposed and investigated. The BB-TENG sensor consists of two copper electrode layers and one 3D-printed frame filled with polytetrafluoroethylene (PTFE) balls. When the sensor is installed on a vibration exciter, the PTFE balls will continuously bounce between the two electrodes, generating a periodically fluctuating electrical signals whose frequency can be easily measured through fast Fourier transform. Experiments have demonstrated that the BB-TENG sensor has a high signal-to-noise ratio of 34.5 dB with mean error less than 0.05% at the vibration frequency of 10 Hz to 50 Hz which covers the most vibration range of the machinery on ship. In addition, the BB-TENG can power 30 LEDs and a temperature sensor by converting vibration energy into electricity. Therefore, the BB-TENG sensor can be utilized as a self-powered and highly accurate vibration sensor for condition monitoring of intelligent ship machinery.

show abstract

“…Meanwhile, there are some references for predicting the possible fault locations and causes, and providing maintenance decisions and suggestions, so as to take effective measures to quickly troubleshoot. For instance, Feng and Li [6] aimed at the inaccurate and time-consuming problems of the fault diagnosis method for a large-scale ship engine, and proposed an intelligent diagnosis method for large-scale ship engine fault in a non-deterministic environment based on neural network. Tang et al [7] proposed a fault diagnosis system of ship power plants based on a C/S (Client/Server) and B/S (Browser/Server) hybrid structure for the ship power plant fault diagnosis system, which provided a good solution for the development of intelligent ships.…”

Section: Introductionmentioning

confidence: 99%

The Temperature Forecast of Ship Propulsion Devices from Sensor Data

Hua

Yin

2019

Information

View full text Add to dashboard Cite

The big data from various sensors installed on-board for monitoring the status of ship devices is very critical for improving the efficiency and safety of ship operations and reducing the cost of operation and maintenance. However, how to utilize these data is a key issue. The temperature change of the ship propulsion devices can often reflect whether the devices are faulty or not. Therefore, this paper aims to forecast the temperature of the ship propulsion devices by data-driven methods, where potential faults can be further identified automatically. The proposed forecasting process is composed of preprocessing, feature selection, and prediction, including an autoregressive distributed lag time series model (ARDL), stepwise regression (SR) model, neural network (NN) model, and deep neural network (DNN) model. Finally, the proposed forecasting process is applied on a naval ship, and the results show that the ARDL model has higher accuracy than the three other models.

show abstract

Research on Intelligent Diagnosis Method for Large-Scale Ship Engine Fault in Non-Deterministic Environment

Cited by 8 publications

References 14 publications

Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

Fault Diagnosis of Ship Ballast Water System Based on Support Vector Machine Optimized by Improved Sparrow Search Algorithm

A Self-Powered and Highly Accurate Vibration Sensor Based on Bouncing-Ball Triboelectric Nanogenerator for Intelligent Ship Machinery Monitoring

The Temperature Forecast of Ship Propulsion Devices from Sensor Data

Contact Info

Product

Resources

About