A ship power machinery monitoring system can help ship personnel to keep abreast of the operating rules of ship power machinery and equipment and carry out initial fault diagnosis, which is important for improving economic and social benefits. The purpose of this paper is to study the system development and design of ship power machinery in the context of artificial intelligence. The importance of ship power machinery monitoring system and the relevant theory of virtual instrumentation technology are explained, and the feasibility of applying virtual instrumentation technology in ship power machinery monitoring system is analysed and discussed; on the basis of the study of ship power machinery system, the overall design scheme of ship energy machinery monitoring system is proposed in conjunction with the demand analysis of the monitoring system; finally, the parameter configuration and modification are used to determine the alarm scheme of the monitoring system is verified that the design method can improve the rationality of the monitoring system design and can meet the requirements of the system designer, system user and system administrator as far as possible.

Garg Julius. System Development and Design of Ship Power Machinery in the Context of Artificial Intelligence. Kinetic Mechanical Engineering (2020), Vol. 1, Issue 1: 35-42. https://doi.org/10.38007/KME.2020.010105.

[1] Trauthwein G . Power Playes. Martime reporter and engineering news, 2019, 81(5):44-51.

[2] Iannotta B . Glimpsing the future. The Motor ship, 2018, 99(1163):4-4.

[3] Bakalov I . A Contemporary Concept in Troubleshooting and Fixing Malfunctions Using an Engine Room Simulator in Augmented Reality Environment. Universal Journal of Mechanical Engineering, 2019, 7(2):33-36. https://doi.org/10.13189/ujme.2019.070201

[4] Gnacinski P , Tarasiuk T , Mindykowski J , et al. Power Quality and Energy-Efficient Operation of Marine Induction Motors. IEEE Access, 2020, PP(99):1-1. https://doi.org/10.1109/ACCESS.2020.3017133

[5] Balabin V P , Kamnev M A , Kuchin N L , et al. Efficiency of emergency depressurization systems for marine nuclear reactor containments. Transactions Of The Krylov State Research Centre, 2018, 4(386):107-116. https://doi.org/10.24937/2542-2324-2018-4-386-107-116

[6] Soni T , Dutt J K , Das A S . Magnetic Bearings for Marine Rotor Systems - Effect of Standard Ship Maneuver. IEEE Transactions on Industrial Electronics, 2020, PP(99):1-1.

[7] Singh J , Dahiya R , Saini L M . Single DC source H-bridge topology as a low cost multilevel inverter for marine electric systems. Indian Journal of Marine Sciences, 2018, 47(6):1199-1207.

[8] Javaid U . MVDC Supply Technologies for Marine Electrical Distribution Systems. Cpss Transactions on Power Electronics & Applications, 2018, 3(1):65-76. https://doi.org/10.24295/CPSSTPEA.2018.00007

[9] Demir H S , Christen J B , Ozev S . Energy-Efficient Image Recognition System for Marine Life. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020, 39(11):3458-3466. https://doi.org/10.1109/TCAD.2020.3012745

[10] Landryova L . Simulation Modelling of Integrated Systems in Marine Technology - ScienceDirect. IFAC-PapersOnLine, 2018, 51(11):490-495. https://doi.org/10.1016/j.ifacol.2018.08.366

[11] Irfan S . Development of trainer sensor as media learning control systems for engine cadets at Merchant Marine Polytechnic Surabaya. Ukrainian Journal of Educational Studies and Information Technology, 2020, 8(2):55-70. https://doi.org/10.32919/uesit.2020.02.05

[12] Mademlis G , Liu Y , Chen P , et al. Design of Maximum Power Point Tracking for Dynamic Power Response of Tidal Undersea Kite Systems. IEEE Transactions on Industry Applications, 2020, PP(99):1-1.

[13] Soliman M A , Hasanien H M , Alkuhayli A . Marine Predators Algorithm for Parameters Identification of Triple-Diode Photovoltaic Models. IEEE Access, 2020, PP(99):1-1. https://doi.org/10.1109/ACCESS.2020.3019244

[14] Thrall J H , Li X , Li Q , et al. Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. Journal of the American College of Radiology, 2018, 15( 3):504-508. https://doi.org/10.1016/j.jacr.2017.12.026

[15] Polina M , Lucy O , Yury Y , et al. Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare. Oncotarget, 2018, 9(5):5665-5690. https://doi.org/10.18632/oncotarget.22345

[16] Syam N , Sharma A . Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 2018, 69(FEB.):135-146. https://doi.org/10.1016/j.indmarman.2017.12.019

[17] Camerer C F . Artificial Intelligence and Behavioral Economics. NBER Chapters, 2018, 24(18):867-71. https://doi.org/10.1016/j.cub.2014.07.040

[18] Kinoshita F , Okamoto T , Yamashita T , et al. Artificial intelligence-derived gut microbiome as a predictive biomarker for therapeutic response to immunotherapy in lung cancer: protocol for a multicentre, prospective, observational study. BMJ Open, 2020, 12(6):1823-33. https://doi.org/10.1136/bmjopen-2020-061674