Simulation of Failure Detection Based on Neural Network for No-Ball Mill

2011 ◽  
Vol 201-203 ◽  
pp. 627-631
Author(s):  
Kun Shan Li ◽  
Xin Hua Wang ◽  
Wen Ming Wang
Keyword(s):  
Neural Network ◽  
Data Processing ◽  
Network Theory ◽  
Ball Mill ◽  
Detection System ◽  
Structural Characteristics ◽  
Failure Detection ◽  
The Neural Network ◽  
Point Set ◽  
Neural Network Theory

According to the structural characteristics of non-ball mill, using the neural network theory to select and measure point, set the failure mode, analyze and determine the cause of malfunction. The newly developed fault detection system was used to simulative detect fault. Through data processing, the results can be directly derived which could be fed back into the design of non-ball mill, thereby improving the design.

The Establishment of Land Appraisal Model in Xiamen Based on BP Neural Network

2012 ◽  
Vol 518-523 ◽  
pp. 6084-6087
Author(s):  
Qing Ye ◽  
Ya Yi Su ◽  
Fei Chen
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Network Theory ◽  
Evaluation Model ◽  
Land Evaluation ◽  
Bp Network ◽  
The Neural Network ◽  
Hidden Layer ◽  
Neural Network Theory ◽  
Error Index

Establish the land evaluation model of Xiamen by means of BP neural network theory, taking 2007-2009 land evaluation cases of Xiamen as examples. Through statistical analysis, we find that the neural network which has 9 net work hidden layer nodes and 19% of maximal error index is more suitable for Xiamen land price assessment than others. Empirical analysis shows that the model has a good generalization ability, which can be used for land evaluation practices. The results indicates that the properties of autonomous learning of BP network can reduce the subjective factors of appraiser in land evaluation , also, the network has the advantage of simple and quick calculation.

Time Series Analysis of Slope Displacement Using Neural Network Method

2013 ◽  
Vol 405-408 ◽  
pp. 129-132
Author(s):  
Zhi Qiang Zhang ◽  
Yan Liang Wen ◽  
Guo Jian Zhang ◽  
Lai Shan Chang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Network Theory ◽  
System Analysis ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Network Method ◽  
Neural Network System ◽  
Neural Network Theory ◽  
Slope Displacement

Based on the artificial neural network theory, a neural network approach is proposed for the analysis of slope displacement time series, the neural network system analysis of slope displacement time series is developed, it is proved that this method is scientific and reasonable.

Modelling Clearance Sales Outshopping Behaviour Using Neural Network Model

2013 ◽  
pp. 230-244
Author(s):  
M. Hemalatha
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Network Theory ◽  
Time Series Prediction ◽  
Strategy Development ◽  
Neural Network Modeling ◽  
System A ◽  
The Neural Network ◽  
Neural Network Theory

The neural network is a very useful tool for approximation of a function, time series prediction, classification, and pattern recognition. If there is found to be a non-linear relationship between input data and output data, it is difficult to analyse the system. A neural network is very effective to solve this problem. This chapter studies the applied neural network model in relation to clearance sales outshopping behaviour. Since neural network theory can be applied effectively to this case, the authors have used neural network theory to recognise the retail area satisfaction and loyalty. To measure the impact among the retail area attributes, retail area satisfaction, and retail area loyalty, the authors have used the neural network model. In this chapter, they have treated twenty seven factors as the input signals into the input layer. Therefore, they find the weights between nodes in the relationship between the value of all twenty seven factors and the retail area satisfaction and loyalty. The development of the model by retail area attributes, and their interpretation, was facilitated by a collection of data across three trading areas. This neural network modeling approach to understand clearance sales outshopping behaviour provides retail managers with information to support retail strategy development.

Study on Neural Networks Usage to Analyse Correlation between Spectrum of Vibration Acceleration Signal from Pin of Ball Mill and its Filling Level

2015 ◽  
Vol 770 ◽  
pp. 540-546 ◽  
Author(s):  
Yuri Eremenko ◽  
Dmitry Poleshchenko ◽  
Anton Glushchenko
Keyword(s):  
Neural Network ◽  
Ball Mill ◽  
Signal Amplitude ◽  
Signal Spectrum ◽  
Laboratory Model ◽  
Training Set ◽  
Vibration Acceleration ◽  
Acceleration Signal ◽  
The Neural Network ◽  
Trained Neural Network

The question about modern intelligent information processing methods usage for a ball mill filling level evaluation is considered. Vibration acceleration signal has been measured on a mill laboratory model for that purpose. It is made with accelerometer attached to a mill pin. The conclusion is made that mill filling level can not be measured with the help of such signal amplitude only. So this signal spectrum processed by a neural network is used. A training set for the neural network is formed with the help of spectral analysis methods. Trained neural network is able to find the correlation between mill pin vibration acceleration signal and mill filling level. Test set is formed from the data which is not included into the training set. This set is used in order to evaluate the network ability to evaluate the mill filling degree. The neural network guarantees no more than 7% error in the evaluation of mill filling level.

scholarly journals An Industrial Micro-Defect Diagnosis System via Intelligent Segmentation Region

Sensors ◽  
2019 ◽  
Vol 19 (11) ◽  
pp. 2636 ◽  
Author(s):  
Xia Fang ◽  
Wang Jie ◽  
Tao Feng
Keyword(s):  
Neural Network ◽  
Defect Detection ◽  
Large Scale ◽  
Detection System ◽  
Scale Production ◽  
System Matrix ◽  
Rotating Platform ◽  
Limited Region ◽  
The Neural Network ◽  
Large Scale Production

In the field of machine vision defect detection for a micro workpiece, it is very important to make the neural network realize the integrity of the mask in analyte segmentation regions. In the process of the recognition of small workpieces, fatal defects are always contained in borderline areas that are difficult to demarcate. The non-maximum suppression (NMS) of intersection over union (IOU) will lose crucial texture information especially in the clutter and occlusion detection areas. In this paper, simple linear iterative clustering (SLIC) is used to augment the mask as well as calibrate the score of the mask. We propose an SLIC head of object instance segmentation in proposal regions (Mask R-CNN) containing a network block to learn the quality of the predict masks. It is found that parallel K-means in the limited region mechanism in the SLIC head improved the confidence of the mask score, in the context of our workpiece. A continuous fine-tune mechanism was utilized to continuously improve the model robustness in a large-scale production line. We established a detection system, which included an optical fiber locator, telecentric lens system, matrix stereoscopic light, a rotating platform, and a neural network with an SLIC head. The accuracy of defect detection is effectively improved for micro workpieces with clutter and borderline areas.

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