Application Overview of Neural Network in Fault Diagnosis

2014 ◽  
Vol 697 ◽  
pp. 419-424
Author(s):  
Ze Fan Cai ◽  
Dao Ping Huang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Development Trend ◽  
System Structure ◽  
Bp Network ◽  
Rbf Network ◽  
The Third ◽  
The Neural Network ◽  
The Development Trend

This paper introduces the system structure of neural network in fault diagnosis, and summarizes some applications of neural network in fault diagnosis. The most commonly used neural network in fault diagnosis is BP network. The second is RBF network and the third is ART. For each neural network, the paper will discuss the neural network, and the introduce some applications. It also introduces the combination of neural networks and other techniques. In the last part, this paper points out the development trend of the neural network in fault diagnosis.

scholarly journals Enterprise Network Marketing Prediction Using the Optimized GA-BP Neural Network

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ruyi Yang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Time Series Prediction ◽  
Nonlinear Function ◽  
Development Trend ◽  
Production Volume ◽  
Network Marketing ◽  
Bp Network ◽  
Enterprise Network ◽  
The Neural Network

As a brand-new marketing method, network marketing has gradually become one of the main ways and means for enterprises to improve profitability and competitiveness with its unique advantages. Using these marketing data to build a model can dig out useful information that the business is concerned about, and the company can then formulate marketing strategies based on this information. Sales forecasting is to speculate on the future based on historical sales. It is a tool for companies to determine production volume and ensure the balance of product supply and sales. It can help companies make correct business decisions to maximize profits. The neural network can approximate the nonlinear function with arbitrary precision, and the time series prediction model based on the neural network can well reflect the nonlinear development trend of information. Based on the analysis of the shortcomings of the traditional BP network, this paper uses a genetic algorithm with good global search capabilities to improve the neural network. The thought and theory of optimizing the initial weight and threshold of the neural network of the GA algorithm are discussed in detail. While expounding the forecasting method, it uses specific examples to analyze the performance and characteristics of the GA-BP network in the enterprise network marketing forecasting. The results show that the GA-BP neural network is higher than the traditional BP neural network in terms of prediction accuracy and adaptability.

Fault Diagnosis of CNC Machine Using Hybrid Neural Network

2011 ◽  
Vol 128-129 ◽  
pp. 865-869
Author(s):  
Juan Du ◽  
Xian Guo Yan ◽  
Na Sha Wei
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
First Grade ◽  
Second Grade ◽  
Cnc Machine ◽  
Bp Network ◽  
Hybrid Neural Network ◽  
Neural Network System ◽  
Diagnosis Method

This paper proposed a technology of fault diagnosis for CNC machine based on hybrid neural network. Before the fault diagnosis was made, the fault patterns were firstly obtained from technical manuals and field experience to build a sample set, and then they were classified and coded according to the rule. In order to improve diagnosis speed, the fault diagnosis system was designed as a hybrid neural network system which consists of two-grade neural networks. When the fault pattern was input the system, the fault was first classified by the first-grade BP network, and according to the fault type, the corresponding second-grade ART network was activated to perform fault diagnosis. In this paper, the train algorithms of two kinds of neural networks were programmed by MATLAB. Comparing with the traditional diagnosis method, the presented technology possesses advantages of automatic fault diagnosis and ability for self-learning and self-organization.

A methodology for fault diagnosis in robotic systems using neural networks

Robotica ◽  
2004 ◽  
Vol 22 (4) ◽  
pp. 419-438 ◽  
Author(s):  
Arun T. Vemuri ◽  
Marios M. Polycarpou
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Robotic Manipulators ◽  
Robotic Systems ◽  
Rigid Link ◽  
The Neural Network ◽  
Modeling Uncertainties ◽  
Analytical Redundancy ◽  
Key Issues

Fault diagnosis plays an important role in the operation of modern robotic systems. A number of researchers have proposed fault diagnosis architectures for robotic manipulators using the model-based analytical redundancy approach. One of the key issues in the design of such fault diagnosis schemes is the effect of modeling uncertainties on their performance. This paper investigates the problem of fault diagnosis in rigid-link robotic manipulators with modeling uncertainties. A learning architecture with sigmoidal neural networks is used to monitor the robotic system for off-nominal behavior due to faults. The robustness, sensitivity, missed detection and stability properties of the fault diagnosis scheme are rigorously established. Simulation examples are presented to illustrate the ability of the neural network based robust fault diagnosis scheme to detect and accommodate faults in a two-link robotic manipulator.

Artificial Neural Network Based Fault Diagnosis of IC Engines

2012 ◽  
Vol 518 ◽  
pp. 47-56 ◽  
Author(s):  
Jian Chen ◽  
Robert Randall ◽  
Bart Peeters ◽  
Wim Desmet ◽  
Herman Van der Auweraer
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Diagnosis ◽  
Diagnostic System ◽  
The Neural Network ◽  
Detection Stage ◽  
Mechanical Faults ◽  
The Neural Networks ◽  
Ic Engines ◽  
Artificial Neural

Fault diagnosis is important to avoid unforeseen failures of IC engines, but normally requires an expert to interpret analysis results. Artificial Neural Networks are potential tools for the automated fault diagnosis of IC engines, as they can learn the patterns corresponding to various faults. Most engine faults can be classified into two categories: combustion faults and mechanical faults. Misfire is a typical combustion fault; piston slap and big end bearing knock are common mechanical faults. The automated diagnostic system proposed in this paper has three main stages, each stage including three neural networks. The first stage is the fault detection stage, where the neural networks detect whether there are faults in the engine and if so which kind. In the second stage, based on the detection results, the severity of the faults was identified. In the third stage, the neural networks localize which cylinder has a fault. The critical thing for a neural network is its input feature vector, and a previous study had indicated a number of features that should differentiate between the different faults and their location, based on advanced signal processing of the vibration signals measured for different normal and fault conditions. In this study, an advanced feature selection technology was employed to select the significant features as the inputs to networks. The input vectors were separated into two groups, one for training the network, and the other for its validation. Finally it has been demonstrated that the neural network based system can automatically differentiate and diagnose a number of engine faults, including location and severity.

scholarly journals Neural Network Optimization and Data Fusion Recognition Method for Intelligent Mechanical Fault Diagnosis

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ying Chen
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Data Fusion ◽  
Bp Neural Network ◽  
Hot Spot ◽  
Diagnostic Errors ◽  
Mechanical Equipment ◽  
Bp Network ◽  
Vibration Signals ◽  
Rbf Network

With the improvement of mechanical equipment complexity and automation level, the importance of mechanical equipment fault diagnosis is more and more prominent, and the choice of appropriate diagnosis method is crucial to the accuracy of the diagnosis results. Wavelet analysis and neural network technology, as the hot spot and frontier of research, are also important research contents in the development of intelligent diagnosis of mechanical fault. Data fusion can process multisource information to obtain more accurate and reliable methods. At the same time, because of its good nonlinearity, adaptability, and fault tolerance, neural network has become the preferred method of mechanical fault diagnosis. This paper first describes the research content and significance of fault diagnosis technology and introduces the main methods and steps of fault diagnosis, and through the introduction of mechanical fault vibration signals, vibration signals were analyzed in time domain and frequency domain. Secondly, the definition and classification of data I fusion and RBF neural network are introduced in detail and compared with BP neural network. Because the prediction accuracy of the RBF network is higher than that of the BP neural network and the training time of the RBF network is obviously shorter than that of the BP network, the RBF network has significant advantages over diagnostic errors. In this paper, six valve signals were collected under normal conditions and errors, and by analyzing and comparing different theoretical foundations, the 4-second network crisis time was effectively reduced, which provided the basis for teaching monitoring.

Study about Forecasting of Vegetable Prices Based on Neural Network

2011 ◽  
Vol 58-60 ◽  
pp. 23-27
Author(s):  
Jin Shan Zhang ◽  
Xiang Tian Xie
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fault Tolerance ◽  
Bp Neural Network ◽  
Supply And Demand ◽  
Development Trend ◽  
High Noise ◽  
Price Forecast ◽  
Self Study ◽  
The Development Trend

In the study of vegetable price forecast, as the price is subject to various uncertain factors (weather, supply and demand, etc.), it has attributes such as high nonlinear, randomness and high noise, which would lead to the difficulty in forecasting. But grasping the law of price development and understanding the development trend of price, would help farmers grow the vegetable reasonably, and reduce unbalanced supply and demand. Therefore, we will make use of the characteristics of neural networks such as self-adapt，self-study and high fault tolerance, to build up the model of BP neural network with the training function of L-M for forecasting the vegetable prices. Finally, numerical example proves that the method is effective.

Garbage Crusher Fault Diagnosis Based on RBF Neural Network

2009 ◽  
Vol 16-19 ◽  
pp. 971-975
Author(s):  
Yong Hou Sun ◽  
Cong Li ◽  
Mei Fa Huang ◽  
Hui Jing
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Structure And Properties ◽  
Automatic Equipment ◽  
Rbf Network ◽  
The Neural Network ◽  
Diagnosis Model ◽  
Simulation Results ◽  
Training Simulation

The garbage crusher is a new kind of crusher for garbage crushing when processing Municipal Solid Waste (MSW). With the development of automatic equipment and the complication of structure and properties of the garbage crusher, the fault diagnosis of garbage crusher is very important. In this paper, according to the fault symptoms and parameters, Radial Basis Function Neural Network (RBF NN) is used for fault diagnosis of the garbage crusher. The structure and inference of RBF NN are discussed in detail. The garbage crusher fault diagnosis model is established based on RBF network. At last, the fault of mechanical system is taken as an example of garbage crusher fault diagnosis. Training simulation results of the neural network are given base on MATLAB software. The result shows the RBF NN is suitable for fault diagnosis of garbage crusher.

The Fault Diagnosis of Electric Power Metering System Based on Momentum BP Neural Network

2014 ◽  
Vol 668-669 ◽  
pp. 724-728 ◽  
Author(s):  
Yue Yang Zhang ◽  
Jian Jun Zhao ◽  
Wei Liang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Electric Power ◽  
High Voltage ◽  
Bp Neural Network ◽  
Binary Number ◽  
Bp Network ◽  
The Neural Network ◽  
Main Fault ◽  
Power Metering

Momentum BP neural network is used to diagnose the fault of high voltage electric power metering system according to the advantage of strong self-adaptation. First of all, the information of 7 operation parameters of metering system is extracted as input values of the neural network, secondly, we collect samples to train and test BP network and use 4-bit binary number to represent the normal circumstance and 7 main fault types, The results show that the momentum BP network model is effective in the fault diagnosis of high voltage electric power metering system.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

scholarly journals Discretization and machine learning approximation of BSDEs with a constraint on the Gains-process

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Idris Kharroubi ◽  
Thomas Lim ◽  
Xavier Warin
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Neural Networks ◽  
Differential Equations ◽  
Numerical Experiments ◽  
Optimization Problem ◽  
Learning Approach ◽  
The Neural Network ◽  
Machine Learning Approach ◽  
Mesh Grid

AbstractWe study the approximation of backward stochastic differential equations (BSDEs for short) with a constraint on the gains process. We first discretize the constraint by applying a so-called facelift operator at times of a grid. We show that this discretely constrained BSDE converges to the continuously constrained one as the mesh grid converges to zero. We then focus on the approximation of the discretely constrained BSDE. For that we adopt a machine learning approach. We show that the facelift can be approximated by an optimization problem over a class of neural networks under constraints on the neural network and its derivative. We then derive an algorithm converging to the discretely constrained BSDE as the number of neurons goes to infinity. We end by numerical experiments.

Sign in / Sign up

Export Citation Format

Share Document