Fault Diagnosis for the Oil System of Aviation Piston Engine Based on BP Neural Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 1185-1188
Author(s):  
Yan Feng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Mean Squared Error ◽  
Piston Engine ◽  
Squared Error ◽  
Training Samples ◽  
The Mean ◽  
The Given

Introduced the composition and the principle of operation of the oil system of aviation piston engine. Analysed common faults of the oil system including high oil pressure indication,low oil pressure indication, high oil temperature indication and excessive oil consumption.Failure causes for above faults were analysed separately.Symbols were stood for failure modes and failure causes. Constructed the BP neural network.Symbols of failure modes were inputs of the BP neural network,and symbols of failure causes were outputs of the BP neural network.Builded a mapping relationship between failure modes and failure causes by training samples studying.Four training samples were selected based on common faults and fault effects.A given mode was as a input of the network,and by adjusting connection weights and the threshold of every neuron,an ideal result could be gotten.Then other mode was as a input of the network which carried on studying until the epochs was 369,and the mean squared error fast converged and the value of mean squared error was.The failure causes for the given failure mode can be confirmed by this BP neural network.By engineering verification, the BP neural network is applicable to fault diagnosis for oil system of aviation piston engine.

Fault Diagnosis of Traction Inverter Based on Wavelet Analysis and Neural Network

2014 ◽  
Vol 1061-1062 ◽  
pp. 1025-1030
Author(s):  
Ya Fei Wang ◽  
Wen Ming Zhang ◽  
Xing Lai Ge ◽  
Yang Lu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Wavelet Analysis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Open Circuit ◽  
Phase Current ◽  
Three Phase ◽  
Simulation Results

Due to IGBT open-circuit fault of CRH2 EMU’s traction inverter, a method of its fault diagnosis with the three-phase current signals as detection objects is conducted. By applying the wavelet analysis, three-phase current signals are decomposed for four times. With the coefficients of each layer obtained, the energy values of layers are calculated as well as the vectors corresponding to failure modes. According to the vectors regarded as input and the expected output, a BP neural network is established. Through training the network, the parameters of network can be defined. In addition, to test and evaluate the performance of network, certain noise is added to the three-phase current signals. Simulation results show it is feasible for the fault diagnosis of traction inverter.

BP Neural Network Based Animation Production Prediction

2014 ◽  
Vol 539 ◽  
pp. 475-478
Author(s):  
Ran Tao ◽  
Da Chao Yuan ◽  
Gang Yi Hu
Keyword(s):  
Neural Network ◽  
Relative Error ◽  
Bp Neural Network ◽  
Prediction Method ◽  
Coefficient Of Determination ◽  
Mean Relative Error ◽  
Training Samples ◽  
The Mean ◽  
Production Prediction

In order to research the basic condition of animation production, this article chooses BP Neural Network to predict the animation production. We select 13 test samples, selected nine of them randomly as training samples, and the remaining four as the test samples. The coefficient of determination is 0.99839 and the mean relative error is 0.186125. The result shows that BP Neural Network is an effective prediction method.

scholarly journals Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Indikator Utama Ekonomi Dunia

2018 ◽  
Vol 2 (2) ◽  
pp. 169
Author(s):  
Alan Boy Sandy Damanik ◽  
Agung Bimantoro
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Economic Indicator ◽  
Squared Error ◽  
The World ◽  
Artificial Neural Network Method ◽  
World Economic ◽  
Architectural Models ◽  
The Mean

Economics is one of the most important aspects in the world. Economics greatly determines the progress and development of a country. However, there are still many countries with low economic levels. Therefore the aim of this study is to predict and determine the level of the main indicators of the world economy as one of the anticipatory steps to further increase the level of the country's economy. World Economic Indicator Data to be used is sourced from Bloomberg and Bank Indonesia. To find out further developments, it is necessary to research the existing data. The algorithm used is Backpropagatian Neural Network. Data analysis was carried out using artificial neural network method using Matlab R2011b software. The study uses 5 architectural models. The best network architecture produced is 3-43-1 with an accuracy rate of 86% and the Mean Squared Error (MSE) value is 1.336593.

scholarly journals DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Cédric Arisdakessian ◽  
Olivier Poirion ◽  
Breck Yunits ◽  
Xun Zhu ◽  
Lana X. Garmire
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Single Cells ◽  
Rna Seq ◽  
Squared Error ◽  
Study Gene Expression ◽  
Network Method ◽  
The Mean

Abstract Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson’s correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at https://github.com/lanagarmire/DeepImpute.

scholarly journals Perceptual Image Super Resolution Using Deep Learning and Super Resolution Convolution Neural Networks (SRCNN)

2020 ◽  
Author(s):  
Nagaraj P ◽  
Muthamilsudar K ◽  
Naga Nehanth S ◽  
Mohammed Shahid R ◽  
Sujith Kumar V
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Convolution Neural Network ◽  
Resolution Image ◽  
Squared Error ◽  
Image Super Resolution ◽  
The Mean ◽  
State Of Art

The main objective of Perceptual Image Super Resolution is to obtain a high resoluted image from a normal low resolution image. The task is very simple that we just want to make a Low firmness appearance into a extraordinary resolution image. To perform this task we have various methods like Classical Approach in which we try to maximize the mean squared error, evaluate by PSNR(Peak-Signal-to-Noise-Ratio). The first method used to perform this operation was SRCNN (Super Resolution Convolution Neural Network) and these days many of them use DRCN and VDSR which are slightly upgraded methods. Another technique used for the purpose of upscaling to get a high resoluted image from normal little resolution image is the state of art by PSNR. This method was a quite simple one in which we take a low determination image as input and place in a convolution neural network(CNN) and produce a high resolution image as the output. In this technique the edges will be clearly defined, but the whole image will be blurred. This method is unable to produce good-looking textures.

Improved PSO-BP neural network for power transformer fault diagnosis

2010 ◽  
Vol 30 (3) ◽  
pp. 783-785 ◽  
Author(s):  
Zhong-yang XIONG ◽  
Qing-bo YANG ◽  
Yu-fang ZHANG
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Transformer Fault ◽  
Improved Pso

PNN and BP Neural Network Fault Diagnosis Research on Electronic Accelerator Pedal Detection

2020 ◽  
Vol 14 (2) ◽  
pp. 205-220
Author(s):  
Yuxiu Jiang ◽  
Xiaohuan Zhao
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Fault Detection ◽  
Lower Limit ◽  
Bp Neural Network ◽  
Accelerator Pedal ◽  
Electronic Throttle ◽  
Measured Voltage ◽  
Upper Limit ◽  
Network Fault

Background: The working state of electronic accelerator pedal directly affects the safety of vehicles and drivers. Effective fault detection and judgment for the working state of the accelerator pedal can prevent accidents. Methods: Aiming at different working conditions of electronic accelerator pedal, this paper used PNN and BP diagnosis model to detect the state of electronic accelerator pedal according to the principle and characteristics of PNN and BP neural network. The fault diagnosis test experiment of electronic accelerator pedal was carried out to get the data acquisition. Results: After the patents for electronic accelerator pedals are queried and used, the first measured voltage, the upper limit of first voltage, the first voltage lower limit, the second measured voltage, the upper limit of second voltage and the second voltage lower limit are tested to build up the data samples. Then the PNN and BP fault diagnosis models of electronic accelerator pedal are established. Six fault samples are defined through the design of electronic accelerator pedal fault classifier and the fault diagnosis processes are executed to test. Conclusion: The fault diagnosis results were analyzed and the comparisons between the PNN and the BP research results show that BP neural network is an effective method for fault detection of electronic throttle pedal, which is obviously superior to PNN neural network based on the experiment data.

Research of Fault Diagnosis of Belt Conveyor Based on Fuzzy Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 916-921
Author(s):  
Yuan Yuan ◽  
Wenjun Meng ◽  
Xiaoxia Sun
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Fault Location ◽  
Fuzzy Neural Network ◽  
Fuzzy Theory ◽  
Belt Conveyor ◽  
Fuzzy Neural ◽  
Neural Network Algorithm ◽  
Network Fault

To address deficiencies in the process of fault diagnosis of belt conveyor, this study uses a BP neural network algorithm combined with fuzzy theory to provide an intelligent fault diagnosis method for belt conveyor and to establish a BP neural network fault diagnosis model with a predictive function. Matlab is used to simulate the fuzzy BP neural network fault diagnosis of the belt conveyor. Results show that the fuzzy neural network can filter out unnecessary information; save time and space; and improve the fault diagnosis recognition, classification, and fault location capabilities of belt conveyor. The proposed model has high practical value for engineering.

Sign in / Sign up

Export Citation Format

Share Document