The End-Point Ingredient Prediction of Low Carbon Ferrochrome Smelt Based on the Working Condition

2011 ◽  
Vol 128-129 ◽  
pp. 1246-1249
Author(s):  
Niao Na Zhang ◽  
Ke Wei Liu ◽  
Bao Dong Zhang
Keyword(s):  
Working Conditions ◽  
Information Fusion ◽  
Working Condition ◽  
Bayesian Classifier ◽  
Support Vector ◽  
Low Carbon ◽  
Multi Scale ◽  
End Point ◽  
Prediction Result ◽  
Carbon Ferrochrome

Construct the model of the end-point ingredient prediction of low carbon ferrochrome smelt based on the working condition of electrothermal silicon method using the method of multi-scale support vector machiness information fusion, where the best decomposition scale information is according to different smelt working conditions using Levenberg-Marquart algorithm to optimize the design, smelt working condition is judged by Bayesian classifier. Researches have proved that this method can improve the precision of prediction and make the prediction result more accurate, reasonable and practical.

The Online Prediction of the Low Carbon Ferrochrome Terminal Composition in Smelting Process

2013 ◽  
Vol 645 ◽  
pp. 519-522
Author(s):  
Niao Na Zhang ◽  
Ying Ying Wang ◽  
Yong Jun Bai
Keyword(s):  
Power Supply System ◽  
Production Quality ◽  
Smelting Process ◽  
Support Vector ◽  
Low Carbon ◽  
Multi Scale ◽  
Carbon Ferrochrome ◽  
Svm Algorithm ◽  
Wavelet Kernel Function ◽  
Online Prediction

The online prediction of the low carbon ferrochrome terminal composition in electro-silicothemic smelting process plays a key role in guiding the determining the tapping time, the smelting process of the power supply system, the production quality and the energy consumption and so on. By introducing the multi-scale wavelet kernel function in the support vector machine (SVM) algorithm, and according to the Bayesian classifier to certain different smelting conditions, we chose different decomposition scales. In this way, the accuracy of the terminal composition prediction during the smelting process is improved greatly. Experiments show the effectiveness of the proposed method.

scholarly journals Fault Diagnosis of Subway Traction Motor Bearing Based on Information Fusion under Variable Working Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yanwei Xu ◽  
Weiwei Cai ◽  
Tancheng Xie
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Working Conditions ◽  
Information Fusion ◽  
Working Condition ◽  
Rolling Bearing ◽  
Traction Motor ◽  
Bearing Fault ◽  
Diagnosis Method ◽  
Motor Bearing

Under the variable working condition, the fault signal of the rolling bearing contains rich characteristic information. In view of the problem that the traditional fault diagnosis method of the rolling bearing depends on the prior knowledge and expert experience too much and the low recognition rate of some faults with the single signal, one method of rolling bearing fault diagnosis based on information fusion under the variable working condition is proposed. Firstly, one test and multi-information acquisition system of the rolling bearing is built. Secondly, the metro traction motor bearing nu216 is selected as the research object, and to prefabricate the defects, the data of acoustic emission and vibration acceleration signals during the test of the bearing is acquired. Then, the original signal is processed and extracted by the wavelet packet decomposition, and the normalized feature information is fused by the convolution neural network. Finally, the two-dimensional convolution neural network model is established to diagnose the bearing fault of the metro traction motor under different conditions. The test results show that the intelligent fault diagnosis method of the subway traction motor bearing based on information fusion under variable working conditions can accurately identify the fault type of the bearing, while the load and speed change. When the neural network training set and the test set cover the same working conditions, the accuracy can reach 100%.

A novel load prediction method for hybrid electric ship based on working condition classification

2020 ◽  
pp. 014233122092376 ◽  
Author(s):  
Diju Gao ◽  
Yao Jiang ◽  
Nan Zhao
Keyword(s):  
Working Conditions ◽  
Clustering Algorithm ◽  
Working Condition ◽  
Prediction Models ◽  
Markov Chain Model ◽  
Effective Means ◽  
Support Vector ◽  
Chain Model ◽  
Power Sources ◽  
Load Demand

In order to effectively optimize the load distribution between power sources during the navigation of hybrid ships, a method for predicting ship load demand based on real-time classification according to different working conditions is proposed. The k-means clustering algorithm is used to quantify the voyage history data to classify the ship’s navigation conditions into fast-changing conditions and slow-changing conditions. Some characteristic parameters related to working conditions are selected as input. Then, input and the category of working conditions are put into least squares support vector machine to learn and train to get an online working condition classifier. The genetic algorithm is used to optimize the radial-based neural network to predict the load demand under fast-changing conditions, use the Markov chain model to predict the load demand under slow-changing conditions, so as to obtain the most accurate future load demand of the ship. The simulation results show that the proposed prediction models under different conditions have higher precision, which is an effective means of predicting the load demand for hybrid power ships.

scholarly journals A nonlinear prediction model, incorporating mass transfer theory and expert rules, for refining low-carbon ferrochrome

2020 ◽  
Vol 120 (12) ◽  
Author(s):  
C -J. Guan ◽  
W. You
Keyword(s):  
Mass Transfer ◽  
Prediction Model ◽  
Nonlinear Model ◽  
Low Carbon ◽  
Nonlinear Prediction ◽  
Transfer Theory ◽  
End Point ◽  
Carbon Ferrochrome ◽  
Mass Transfer Theory ◽  
Main Components

SYNOPSIS We present an optimal oxygen-blowing system with expert rules to improve the efficiency of refining low-carbon ferrochrome. A nonlinear model based on mass transfer theory, the principles of heat transfer, and the principles of high-temperature chemical reactions for refining low-carbon ferrochrome are established. The model is mainly used to control the oxygen supply rate during argon-oxygen top-bottom double-blown refining, thereby controlling the refining temperature and reducing the carbon content. Twenty production tests using a 5 t argon-oxygen refining furnace demonstrate the effectiveness of the system and reliability of the nonlinear model. A comparison of the model data with the experimental data shows that although the model fails to predict the silicon content in the final refined product, it can predict the contents of the main components at the refining end-point and the refining temperature accurately. Keywords: prediction model, end-point control, mass transfer theory, expert rules.

Feature extraction method of pipeline signal based on parameter optimized vocational mode decomposition and exponential entropy

2021 ◽  
pp. 014233122110294
Author(s):  
Yina Zhou ◽  
Yong Zhang ◽  
Jingyi Lu ◽  
Fan Yang ◽  
Hongli Dong ◽  
...  
Keyword(s):  
Correlation Coefficient ◽  
Working Conditions ◽  
Acoustic Signals ◽  
Normal Operation ◽  
Local Characteristic ◽  
Support Vector ◽  
K Value ◽  
Feature Vectors ◽  
Mode Decomposition ◽  
Exponential Entropy

Pipeline leakage is the main reason that affects normal operation of the pipeline. In this paper, a feature recognition method for pipeline acoustic signals based on vocational mode decomposition (VMD) and exponential entropy (EE) is investigated, which could extract the characteristics of pipeline signals and further accurately identify the pipeline acoustic signals under different working conditions. First, the VMD is used to decompose the collected acoustic signals into a number of mode components, during which process the optimal mode number (i.e., K-value) is determined by combining local characteristic scale decomposition (LCD) and correlation analysis methods. Then, the characteristic content of each mode component is analyzed with the help of the determined correlation coefficient (CC) threshold. If the correlation coefficient of a mode component is greater than the threshold, then the mode component is selected as the feature component. Subsequently, the EE values of the selected feature components are calculated to form the feature vectors corresponding to different kinds of pipeline signals. Finally, the feature vectors are input into support vector machine (SVM) to classify and recognize the different pipeline states. The experimental results demonstrate that the proposed method can identify the pipeline signals under different working conditions, and the recognition accuracy is up to [Formula: see text]. By analyzing and comparing with methods of EE-SVM, original data-SVM, VMD-singular spectrum entropy (SSE) and VMD-information entropy (IE), it is further verified that the proposed method is feasible and superior to the methods.

BP-AdaBoost Algorithm Based on Variational Mode Decomposition Optimized by Envelope Entropy for Diagnosing the Working Conditions of a Slideway Seedling-Picking Mechanism

2021 ◽  
Vol 37 (4) ◽  
pp. 665-675
Author(s):  
Zhitao He ◽  
Haiyang Zhang ◽  
Jun Wang ◽  
Xin Jin ◽  
Song Gao ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Working Conditions ◽  
Working Condition ◽  
Recognition Accuracy ◽  
Variational Mode Decomposition ◽  
Adaboost Algorithm ◽  
Layer Number ◽  
Entropy Minimization ◽  
Mode Decomposition ◽  
Penalty Factor

Highlights A method of monitoring the working conditions of a slideway seedling-picking mechanism based on variational mode decomposition (VMD), envelope entropy, and energy entropy is proposed. Based on the criterion of envelope entropy minimization, the combination of the decomposition layer number and penalty factor in VMD is optimized to yield a satisfactory decomposition effect of the analyzed vibration signal. The BP-AdaBoost algorithm is used to improve the working condition classification performance for the slideway seedling-picking mechanism. The working-condition identification effect with the proposed method are compared with those through EMD-based, LMD-based, and EEMD-based methods. Abstract . The slideway seedling-picking mechanism is a type of rotating machinery. This study proposes a novel method of identifying the working conditions of slideway seedling-picking mechanisms for early fault diagnosis by utilizing a back-propagation adaptive boosting (BP-AdaBoost) algorithm based on variational mode decomposition (VMD) optimized by the envelope entropy. The experimental results demonstrate that the proposed method can effectively verify the four working conditions (normal state, slideway failure, cam failure, and spring failure). The overall recognition accuracy reaches 90.0% under the optimal combination of the decomposition layer number K and penalty factor a in VMD determined through the envelope entropy minimization criterion. Classification comparisons with empirical mode decomposition (EMD), local mean decomposition (LMD) and ensemble empirical mode decomposition (EEMD) integrated into the BP-AdaBoost algorithm indicate that the overall recognition accuracy of the proposed method is 18.1%, 16.9%, and 15.6% higher than the accuracies of the three conventional methods, respectively. Compared with the K-means, support vector machine (SVM) algorithms, BP-AdaBoost algorithm demonstrates a more dependable capability for identifying the working conditions. This study provides a useful reference for monitoring the working conditions of slideway seedling-picking mechanisms. Keywords: BP-AdaBoost algorithm, Energy entropy, Envelope entropy, Slideway seedling-picking mechanism, Variational mode decomposition, Working conditions.

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