Application of improved DBD algorithm based bp neural network on fault diagnosis for fuel supply system in a certain diesel engine

2011 ◽  
Author(s):  
Fuzhou Feng ◽  
Aiwei Si ◽  
Wei Xing
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Bp Neural Network ◽  
Supply System ◽  
Fuel Supply ◽  
Fuel Supply System

The Tentative Study on Exhausting Performance of Diesel Engine Burning Ethanol-Diesel Mixture

2013 ◽  
Vol 448-453 ◽  
pp. 3532-3536
Author(s):  
Yan Hua Jia ◽  
Gui Fu Wu ◽  
Wen Wu Fan ◽  
Ying Shan Zhen ◽  
Yi Lin Wang
Keyword(s):  
Diesel Engine ◽  
Supply System ◽  
Nox Emission ◽  
Fuel Supply ◽  
Fuel Supply System

Advancements were made to the fuel supply system on the basis of the original X195 Diesel Engine correlating its structure features, and we designed exhausting performance experiments on the injection diesel engine burning the mixture of ethanol and diesel. The results show that: the exhaust smoke and the NOx emission could be significantly reduced by ameliorating the engine.

Applied Research of BP Neural Network in Remote Marine Diesel Engine Fault Diagnosis System

2012 ◽  
Vol 548 ◽  
pp. 444-449 ◽  
Author(s):  
Xin Gang Song ◽  
Yu Na Miao ◽  
Qiang Ma ◽  
Xiao Jie Guo
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
Bp Neural Network ◽  
Marine Diesel Engine ◽  
Gas Temperature ◽  
Data Simulation ◽  
Marine Diesel ◽  
Exhaust Gas Temperature ◽  
Matlab Programming

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.

scholarly journals Fault Diagnosis of High-Power Tractor Engine Based on Competitive Multiswarm Cooperative Particle Swarm Optimizer Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Maohua Xiao ◽  
Weichen Wang ◽  
Kaixin Wang ◽  
Wei Zhang ◽  
Hengtong Zhang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Diesel Engine ◽  
High Power ◽  
Bp Neural Network ◽  
Information Exchange ◽  
Rapid Development ◽  
Particle Swarm ◽  
Particle Swarm Optimizer ◽  
Under Five

With the rapid development of high-power tractor, the fault diagnosis of high-power tractor has become more and more important for ensuring the operating safety and efficiency. PSO is an iterative optimization evolutionary algorithm, which can iterate through different particles to find the optimal solution. However, there is only one population in the standard PSO algorithm, and the information exchange between the populations is relatively single, which can easily lead to the stagnation of the development of the population. In this paper, due to high-power tractor diesel engine fault complexity, fault correlation, and multifault concurrency, a multigroup coevolution particle swarm optimization BP neural network for diesel engine fault diagnosis method was proposed. First, the USB-CAN device was used to collect data of 8 items of the diesel engine under five different working conditions, and the data was parsed through the SAE J1939 protocol; then, the BP neural network was reconstructed, and a competitive multiswarm cooperative particle swarm optimizer algorithm (COM-MCPSO) was used to optimize its structure and weights. Finally, the data of optimized neural network under five different fault conditions show that, compared with BP neural network and PSO optimized BP neural network, the fault diagnosis of COM-MCPSO optimized BP neural network not only improves the network training speed, but also enhances generalization ability and improves recognition accuracy.

Fault Diagnosis and Prognosis for Fuel Supply System in Gas Turbine Engines

2005 ◽  
Author(s):  
Kyusung Kim ◽  
Onder Uluyol ◽  
Charles Ball
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Supply System ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Fuel Supply ◽  
Diagnosis And Prognosis ◽  
Fuzzy Clustering Method ◽  
Fuel Supply System ◽  
Fault Diagnosis And Prognosis

A fault diagnosis and prognosis method is developed for the fuel supply system in gas turbine engines. The engine startup profiles of the core speed (N2) and the exhaust gas temperature (EGT) collected with high speed sampling rate are extracted and processed into a more compact data set. The fuzzy clustering method is applied to the smaller number of parameters and the fault is detected by differentiating the clusters matching the failures. In this work, the actual flight data collected in the field is used to develop and validate the system, and the results are shown for the test on nine engines that experienced fuel supply system failure. The developed fault diagnosis system detects the failure successfully for all nine cases. For the earliest detection cases, the alarms start to trigger 26 days before the system completely fails and 7 days in advance for the last detection.

Fault diagnosis and prognosis for fuel supply systems in gas turbine engines

2008 ◽  
Vol 223 (3) ◽  
pp. 757-768 ◽  
Author(s):  
K Kim
Keyword(s):  
Fault Diagnosis ◽  
Gas Turbine ◽  
Supply System ◽  
Turbine Engines ◽  
Gas Turbine Engines ◽  
Fuel Supply ◽  
Diagnosis And Prognosis ◽  
Fuzzy Clustering Method ◽  
Fuel Supply System ◽  
Fault Diagnosis And Prognosis

This paper introduces a feature-extraction method to characterize gas turbine engine dynamics. The extracted features are used to develop a fault diagnosis and prognosis method for the fuel supply system in gas turbine engines. The engine start-up profiles of the core speed (N2) and the exhaust gas temperature collected with high-speed sampling rate are obtained and processed into a more compact data set by identifying critical-to-characterization instances. The fuzzy-clustering method is applied to the smaller number of parameters, and the fault is detected by differentiating the clusters matching the failures. In this work, the actual flight data collected in the field was used to develop and validate the system, and the results are shown for the test on nine engines that experienced fuel supply system failure. The developed fault diagnosis system detected the failure successfully in all nine cases. For the earliest detection cases, the alarms start to trigger 26 days before the system completely fails and 7 days in advance for the last detection.

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