Research of Marine Diesel Engine Condition Detecting Base on BP Neural Network and Spectrometric Analysis

In order to detect and diagnose abnormal conditions of marine diesel engine and ensure its normal functioning, the present study adopts the BP neural network and related algorithms to determine the remote fault diagnosis process. Taking the design of exhaust gas temperature remote monitoring sub-system as an example, MATLAB programming was used for data simulation and verification. The applying of the system on board a real ship shows that it has a high working rate, a reliable and safe storage mode and a self- adaptive process.

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Research of Marine Diesel Engine’s State Prediction Based on Evolutionary Neural Network and Spectrometric Analysis

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.346.339 ◽

2011 ◽

Vol 346 ◽

pp. 339-345 ◽

Cited By ~ 4

Author(s):

Hai Jun Wei ◽

Guo You Wang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Genetic Algorithms ◽

Diesel Engine ◽

Back Propagation ◽

Back Propagation Neural Network ◽

Lubricating Oil ◽

Spectrometric Analysis ◽

Marine Diesel Engine ◽

Marine Diesel

In this paper, an evolutionary neural networks model is proposed to predict the content of metal elements contained in marine diesel engine lubricating oil, by fusing genetic algorithms (GAs) and error back propagation neural network (BPNN) to offset the demerits of one paradigm by the merits of another. The input data of metal content was detected by spectrometric analysis. Genetic algorithms are used to globally optimize the weights and threshold of BP neural networks. Moreover, one case study was presented to illustrate the proposed method. The prediction accuracy of the novel method is compared with that of only BPNN method to illustrate the feasibility and effectiveness of the proposed method. The relative error on average of results is 1.52%, it can meet the precision request of state detecting in marine diesel engine.

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Research on the Speed of Diesel Engine Based on Improved BP Neural Network Controller

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.281.105 ◽

2013 ◽

Vol 281 ◽

pp. 105-111 ◽

Cited By ~ 2

Author(s):

Yong Shi ◽

Lian Yu Zhang ◽

Jun Sun ◽

Hong Guang Zhang

Keyword(s):

Neural Network ◽

Neural Networks ◽

Diesel Engine ◽

Bp Neural Network ◽

Pid Controller ◽

Adaptive Controller ◽

Regulation System ◽

Marine Diesel Engine ◽

Neural Network Controller ◽

Marine Diesel

Marine diesel engine is of characteristics of non-linear and time-invariant, so it is difficult to be controlled with traditional PID controller. An adaptive controller based on back-propagation (BP) neural networks was put forwarded for marine diesel engine speed control system, where two neural networks are proposed to control the position loop and speed loop. The adaptive controller was improved was improved via introducing relative error in target evaluation function of the BP neural network, and obtain sensitivity function of diesel engine output with respect to its input using a differential equation. The controller has self-learning and adaptive capacity. It can also optimize the PID controller parameters online. The controller was experimentally evaluated on rack position actuator of marine diesel engine simulated based on a diesel hardware-in-loop system of dSPACE. Finally, tests on a diesel engine demonstrated that the controller can satisfy the transient and steady demands of speed regulation system.

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