Review of condition monitoring and fault diagnosis for marine power systems

Abstract The use of condition monitoring and fault diagnosis (CMFD) in marine power systems significantly influences ship safety. This study divides the development of CMFD for marine power systems into three periods and reviews the content, state and limitations of CMFD research for each period. According to the research achievements and engineering experience of the authors’ team, typical application cases are introduced for CMFD in ships, including CMFD platforms on engineering ships, salvage ships, container ships and ro-ro ships powered by solar photovoltaic systems. Finally, prospective research directions are proposed for CMFD in marine power systems, considering the research status of CMFD and the trend toward intelligent and eco-friendly ships.

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Condition monitoring and fault diagnosis in solar photovoltaic systems

10.14264/uql.2019.604 ◽

2019 ◽

Author(s):

Amit Dhoke

Keyword(s):

Fault Diagnosis ◽

Condition Monitoring ◽

Photovoltaic Systems ◽

Solar Photovoltaic

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Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

Mathematical Problems in Engineering ◽

10.1155/2014/280520 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Kuo-Nan Yu ◽

Her-Terng Yau ◽

Jian-Yu Li

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Photovoltaic System ◽

Photovoltaic Systems ◽

Solar Photovoltaic ◽

Error Back Propagation ◽

Point Tracking ◽

Diagnosis Method

At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT) for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

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Research of Condition Monitoring and Fault Diagnosis Techniques for Wind Turbine Gearbox

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.197.206 ◽

2012 ◽

Vol 197 ◽

pp. 206-210 ◽

Cited By ~ 2

Author(s):

Xian You Zhong ◽

Liang Cai Zeng ◽

Chun Hua Zhao ◽

Jin Zhang ◽

Shi Qing Wan

Keyword(s):

Fault Diagnosis ◽

Wind Turbine ◽

Condition Monitoring ◽

Future Research ◽

Wind Turbine Gearbox ◽

Research Directions ◽

Long Time ◽

Future Research Directions ◽

Wind Power Industry ◽

The Cost

Wind power industry enormously expanded during the last several years. However, wind turbines are subjected to different sorts of failures, which lead to the increasement of the cost. The wind turbine gearbox is the most critical component in terms of high failure rates and long time to repair. This paper described common failures and root causes of wind turbine gearboxes. Then it focused on fault diagnosis and monitoring techniques for the wind turbine gearbox. The challenges and future research directions were presented, and the simulator rig of wind turbine gearbox was designed to develop condition monitoring and fault diagnosis techniques for wind turbine gearbox.

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