Gear Fault Diagnosis Based on Rough Set and Support Vector Machine

By introducing Rough Set Theory and the principle of Support vector machine,a gear fault diagnosis method based on them is proposed.Firstly,diagnostic decision-making is reduced based on rough set theory,and the noise and redundancy in the sample are removed,then,according to the chosen reduction,a support vector machine multi-classifier is designed for gear fault diagnosis.Therefore,SVM’training data can be reduced and running speed can quicken.Test shows its accuracy and effi- ciency of gear fault diagnosis.

Classification of power quality combined disturbances based on phase space reconstruction and support vector machines

Power Quality (PQ) combined disturbances become common along with ubiquity of voltage flickers and harmonics. This paper presents a novel approach to classify the different patterns of PQ combined disturbances. The classification system consists of two parts, namely the feature extraction and the automatic recognition. In the feature extraction stage, Phase Space Reconstruction (PSR), a time series analysis tool, is utilized to construct disturbance signal trajectories. For these trajectories, several indices are proposed to form the feature vectors. Support Vector Machines (SVMs) are then implemented to recognize the different patterns and to evaluate the efficiencies. The types of disturbances discussed include a combination of short-term dis-turbances (voltage sags, swells) and long-term disturbances (flickers, harmonics), as well as their homologous single ones. The feasibilities of the proposed approach are verified by simulation with thousands of PQ events. Comparison studies based on Wavelet Transform (WT) and Artificial Neural Network (ANN) are also reported to show its advantages.

Design and Performance Analysis of Axial Flux Permanent Magnet Machines with Double-Stator Dislocation Using a Combined Wye-Delta Connection

Conventional fractional slot concentrated winding three-phase axial flux permanent magnet machines have an abundance of armature reaction magnetic field harmonics which deteriorate the torque performance of the machine.This paper presents a double-stator dislocated axial flux permanent magnet machine with combined wye-delta winding.A wye-delta(Y-△)winding connection method is designed to eliminate the 6 th ripple torque generated by air gap magnetic field harmonics.Then,the accurate subdomain method is adopted to acquire the no-load and armature magnetic fields of the machine,respectively,and the magnetic field harmonics and torque performance of the designed machine are analyzed.Finally,a 6 k W,4000 r/min,18-slot/16-pole axial flux permanent magnet machine is designed.The finite element simulation results show that the proposed machine can effectively eliminate the 6 th ripple torque and greatly reduce the torque ripple while the average torque is essentially identical to that of the conventional three-phase machines with wye-winding connection.

Support vector machine ensemble using rough sets theory

A support vector machine （SVM） ensemble classifier is proposed. Performance of SVM trained in an input space eonsisting of all the information from many sources is not always good. The strategy that the original input space is partitioned into several input subspaces usually works for improving the performance. Different from conventional partition methods, the partition method used in this paper, rough sets theory based attribute reduction, allows the input subspaces partially overlapped. These input subspaces can offer complementary information about hidden data patterns. In every subspace, an SVM sub-classifier is learned. With the information fusion techniques, those SVM sub-classifiers with better performance are selected and combined to construct an SVM ensemble. The proposed method is applied to decision-making of medical diagnosis. Comparison of performance between our method and several other popular ensemble methods is done. Experimental results demonstrate that our proposed approach can make full use of the information contained in data and improve the decision-making performance.

On-line Condition Monitoring and Diagnostic Network System for Rotating Machine Set

Several industrial computers and a server are combined to set up the on-line monitoring and diagnostic system of turbo-generator sets. The main function of the system is to monitor machine sets' running condition. Through analyzing running data, technicians can detect whether there exist faults and where they occur. To share and transmit the dynamic information of the turbo-generator sets, a distributed network system is introduced. NetWare network operating system is used in the LAN (Local Area Network) system. The LAN is extended to realize the sharing of data and remote transmission of information. Furthermore, functions of monitoring and diagnostic clients are listed.

Estimation of Potato Biomass and Yield Based on Machine Learning from Hyperspectral Remote Sensing Data

The estimation of potato biomass and yield can optimize the planting pattern and tap the production potential.Based on partial least square(PLSR),multiple linear regression(MLR),support vector machine(SVM),random forest(RF),BP neural network and other machine learning algorithms,the biomass estimation model of potato in different growth stages is constructed by using single variables such as original spectrum,first-order differential spectrum,combined spectrum index and vegetation index(VI)and their coupled combination variables.The accuracy of the models is compared and analyzed,and the best modeling method of biomass in different growth stages is selected.Based on the optimized modeling method,the biomass of each growth stage is estimated,and the yield estimation model of different growth stages is constructed based on the estimation results and the linear regression analysis method,and the accuracy of the model is verified.The results showed that in tuber formation stage,starch accumulation stage and maturity stage,the biomass estimation accuracy based on combination variable was the highest,the best modeling method was MLR and SVM,in tuber growth stage,the best modeling method was MLR,the effect of yield estimation is good.It provides a reference for the algorithm selection of crop biomass and yield models based on machine learning.

Fuzzy-support vector machine geotechnical risk analysis method based on Bayesian network

Machine learning method has been widely used in various geotechnical engineering risk analysis in recent years. However, the overfitting problem often occurs due to the small number of samples obtained in history. This paper proposes the FuzzySVM(support vector machine) geotechnical engineering risk analysis method based on the Bayesian network. The proposed method utilizes the fuzzy set theory to build a Bayesian network to reflect prior knowledge, and utilizes the SVM to build a Bayesian network to reflect historical samples. Then a Bayesian network for evaluation is built in Bayesian estimation method by combining prior knowledge with historical samples. Taking seismic damage evaluation of slopes as an example, the steps of the method are stated in detail. The proposed method is used to evaluate the seismic damage of 96 slopes along roads in the area affected by the Wenchuan earthquake. The evaluation results show that the method can solve the overfitting problem, which often occurs if the machine learning methods are used to evaluate risk of geotechnical engineering, and the performance of the method is much better than that of the previous machine learning methods. Moreover,the proposed method can also effectively evaluate various geotechnical engineering risks in the absence of some influencing factors.

Radar Emitter Signal Recognition Using Wavelet Packet Transform and Support Vector Machines

This paper presents a novel method for radar emitter signal recognition. First, wavelet packet transform （WPT） is introduced to extract features from radar emitter signals. Then, rough set theory is used to select the optimal feature subset with good discriminability from original feature set, and support vector machines （SVMs） are employed to design classifiers. A large number of experimental results show that the proposed method achieves very high recognition rates for 9 radar emitter signals in a wide range of signal-to-noise rates, and proves a feasible and valid method.

An Approach Using Fuzzy Sets and Boosting Techniques to Predict Liver Disease

The aim of this research is to develop a mechanism to help medical practitioners predict and diagnose liver disease.Several systems have been proposed to help medical experts by diminishing error and increasing accuracy in diagnosing and predicting diseases.Among many existing methods,a few have considered the class imbalance issues of liver disorder datasets.As all the samples of liver disorder datasets are not useful,they do not contribute to learning about classifiers.A few samples might be redundant,which can increase the computational cost and affect the performance of the classifier.In this paper,a model has been proposed that combines noise filter,fuzzy sets,and boosting techniques(NFFBTs)for liver disease prediction.Firstly,the noise filter(NF)eliminates the outliers from the minority class and removes the outlier and redundant pair from the majority class.Secondly,the fuzzy set concept is applied to handle uncertainty in datasets.Thirdly,the AdaBoost boosting algorithm is trained with several learners viz,random forest(RF),support vector machine(SVM),logistic regression(LR),and naive Bayes(NB).The proposed NFFBT prediction system was applied to two datasets(i.e.,ILPD and MPRLPD)and found that AdaBoost with RF yielded 90.65%and 98.95%accuracy and F1 scores of 92.09%and 99.24%over ILPD and MPRLPD datasets,respectively.

Reduction of Cogging Torque and Electromagnetic Vibration Based on Different Combination of Pole Arc Coefficient for Interior Permanent Magnet Synchronous Machine

Cogging torque and electromagnetic vibration are two important factors for evaluating permanent magnet synchronous machine(PMSM)and are key issues that must be considered and resolved in the design and manufacture of high-performance PMSM for electric vehicles.A fast and accurate magnetic field calculation model for interior permanent magnet synchronous machine(IPMSM)is proposed in this article.Based on the traditional magnetic potential permeance method,the stator cogging effect and complex boundary conditions of the IPMSM can be fully considered in this model,so as to realize the rapid calculation of equivalent magnetomotive force(MMF),air gap permeance,and other key electromagnetic properties.In this article,a 6-pole 36-slot IPMSM is taken as an example to establish its equivalent solution model,thereby the cogging torque is accurately calculated.And the validity of this model is verified by a variety of different magnetic pole structures,pole slot combinations machines,and prototype experiments.In addition,the improvement measure of the machine with different combination of pole arc coefficient is also studied based on this model.Cogging torque and electromagnetic vibration can be effectively weakened.Combined with the finite element model and multi-physics coupling model,the electromagnetic characteristics and vibration performance of this machine are comprehensively compared and analyzed.The analysis results have well verified its effectiveness.It can be extended to other structures or types of PMSM and has very important practical value and research significance.

Fault Diagnosis of a Rotary Machine Based on Information Entropy and Rough Set

There exists some discord or contradiction of information during the process of fault diagnosis for rotary machine. But the traditional methods used in fault diagnosis can not dispose of the information. A model of fault diagnosis for a rotary machine based on information entropy theory and rough set theory is presented in this paper. The model has clear mathematical definition and can dispose both complete unification information and complete inconsistent information of vibration faults. By using the model, decision rules of six typical vibration faults of a steam turbine and electric generating set are deduced from experiment samples. Finally, the decision rules are validated by selected samples and good identification results are acquired.

OPTIMIZATION OF THE METHOD OF SELECTING THE TOOTH NUMBER SERIES IN A SET OF CHANGE GEARS Part Ⅱ The Rules for Permuting the DBL’s of the First Order and Their Intercalated Sets

The problem in practice of determining the proper combination of Zi in a set of changegears may be abstracted to the problem of finding the proper combination and permutation ofthe elements ai,i+1 of the set A1 to give maximum M-d. Some results to find optimal combina-tions of the elements of the set A1 have been reported in part I. In this part, some rules forpermuting these elements are introduced. By means of these rules, three kinds of intercalated setsof A1 have been found, namely: （1） Sets with an even left wing, （2） Sets with coincidence of bothwings, and （3） Sets with circulated elements.

Machine intelligence,rough sets and rough-fuzzy granular computing:uncertainty handling in bio-informatics and Web intelligence

Machine intelligence,is out of the system by the artificial intelligence shown.It is usually achieved by the average computer intelligence.Rough sets and Information Granules in uncertainty management and soft computing and granular computing is widely used in many fields,such as in protein sequence analysis and biobasis determination,TSM and Web service classification Etc.

A combined forecasting method for intermittent demand using the automotive aftermarket data

Intermittent demand forecasting is an important challenge in the process of smart supply chain transformation,and accurate demand forecasting can reduce costs and increase efficiency for enterprises.This study proposes an intermittent demand combination forecasting method based on internal and external data,builds intermittent demand feature engineering from the perspective of machine learning,predicts the occurrence of demand by classification model,and predicts non-zero demand quantity by regression model.Based on the strategy selection on the inventory side and the stocking needs on the replenishment side,this study focuses on the optimization of the classification problem,incorporates the internal and external data of the enterprise,and proposes two combination forecasting optimization methods on the basis of the best classification threshold searching and transfer learning,respectively.Based on the real data of auto after-sales business,these methods are evaluated and validated in multiple dimensions.Compared with other intermittent forecasting methods,the models proposed in this study have been improved significantly in terms of classification accuracy and forecasting precision,which validates the potential of combined forecasting framework for intermittent demand and provides an empirical study of the framework in industry practice.The results show that this research can further provide accurate upstream inputs for smart inventory and guarantee intelligent supply chain decision-making in terms of accuracy and efficiency.

Assessing supply chain performance using genetic algorithm and support vector machine

The rough set-genetic support vector machine(SVM) model is applied to supply chain performance evaluation. First, the rough set theory is used to remove the redundant factors that affect the performance evaluation of supply chain to obtain the core influencing factors. Then the support vector machine is used to extract the core influencing factors to predict the level of supply chain performance. In the process of SVM classification, the genetic algorithm is used to optimize the parameters of the SVM algorithm to obtain the best parameter model, and then the supply chain performance evaluation level is predicted. Finally, an example is used to predict this model, and compared with the result of using only rough set-support vector machine to predict. The results show that the method of rough set-genetic support vector machine can predict the level of supply chain performance more accurately and the prediction result is more realistic, which is a scientific and feasible method.

A Fast Algorithm for Training Large Scale Support Vector Machines

The manuscript presents an augmented Lagrangian—fast projected gradient method (ALFPGM) with an improved scheme of working set selection, pWSS, a decomposition based algorithm for training support vector classification machines (SVM). The manuscript describes the ALFPGM algorithm, provides numerical results for training SVM on large data sets, and compares the training times of ALFPGM and Sequential Minimal Minimization algorithms (SMO) from Scikit-learn library. The numerical results demonstrate that ALFPGM with the improved working selection scheme is capable of training SVM with tens of thousands of training examples in a fraction of the training time of some widely adopted SVM tools.

New Model Die Set for PET Plastic Jet-Moulding Machine

At the China International Food Packing Machinery Exhibition, the new model die set for PET plastic jet-mouldingmachine developed by the Zhejiang Province Taizhou Municipality Huangyan Sanyou Plastics Factory attracted the attention of numerous domestic and foreign clients. They rushed to the stand in great numbers for consultation and talks on ordering. According to the evaluation of the experts concerned, the die set is the most advanced one nationwide for PET plastic jet-moulding machinery.

Five-phase Synchronous Reluctance Machines Equipped with a Novel Type of Fractional Slot Winding

Multi-phase machines are so attractive for electrical machine designers because of their valuable advantages such as high reliability and fault tolerant ability.Meanwhile,fractional slot concentrated windings(FSCW)are well known because of short end winding length,simple structure,field weakening sufficiency,fault tolerant capability and higher slot fill factor.The five-phase machines equipped with FSCW,are very good candidates for the purpose of designing motors for high reliable applications,like electric cars,major transporting buses,high speed trains and massive trucks.But,in comparison to the general distributed windings,the FSCWs contain high magnetomotive force(MMF)space harmonic contents,which cause unwanted effects on the machine ability,such as localized iron saturation and core losses.This manuscript introduces several new five-phase fractional slot winding layouts,by the means of slot shifting concept in order to design the new types of synchronous reluctance motors(SynRels).In order to examine the proposed winding’s performances,three sample machines are designed as case studies,and analytical study and finite element analysis(FEA)is used for validation.

基于组合赋权和SPA-TOPSIS的煤矿安全投入决策模型

为解决煤矿安全投入结构和决策方案不合理的问题,首先基于安全价值的视角,考虑安全功能与安全产出关系,选取8个评价指标,构建煤炭企业安全投入评价体系;其次采用熵权法与层次分析法(AHP),综合确定指标权重;最后采用集对分析(SPA)理论改进逼近理想解排序法(TOPSIS),建立决策模型,用以分析评价某煤矿2012—2022年的决策方案,优选评价方案,并针对实际问题提出改善意见。研究表明:该煤矿企业应更注重安全教育和工业卫生指标的投入;煤矿企业在安全投入决策时应综合考虑安全功能需要与安全产出效益,借鉴最佳投入配置,合理调整未来安全投入决策重点。

Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China

Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R^(2)=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R^(2)of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R^(2)=0.680)and normal year(2017)(R^(2)=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R^(2)=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield.