Fault diagnosis of rotating machinery based on self-learning fuzzy spiking neural network

For fuzziness classific boundry of fault diagnosis of rotating machinery and traditional neural network algorithms difficulted to solve contradiction between application problems example scale and netwok scale,a methord of self-learning fuzzy spiking neural network is put forward. The methord overcomes unavailability of cluster analysis on classific boundry of fault diagnosis of rotating machinery by species encoding of pulse sequence and unsupervised learning. The method shows that it effectively solves boundary fuzziness problem on fault diagnosis of rotating machinery,and greatly improves efficiency of fault diagnosis.     

A New Method of Diesel Engine Fuel System Fault Diagnosis

A method of diesel engine fuel system fault diagnosis based on wavelet transform and fuzzy C-means clustering is presented. Five characteristic parameters of reflecting fault state are distilled with wavelet transform of pressure wave of high-pressure oil pipe of diesel. The theory and generic approach of fuzzy C-means clustering algorithm (FCM) is given, and the validity of evaluating fuzzy clustering making use of partition coefficient, partition entropy and parting coefficient is pointed out. Identification of fault mode can be completed utilizing standard fault character modes established by FCM algorithm, and calculating and comparing the similarity degree between this standard mode and sample. The arithmetic is applied to all kinds of typical faults diagnose in the diesel engine fuel system. Measuring results indicate that the precision of fault diagnosis is increased with the analysis of wavelet and FCM.     

A Multi-agent Fault Diagnosis System of Rotating Machinery Based on CBR

Recently,distributed intelligent systems based on multi-agent have been applied to many fields successfully.the multi-agent technique is introduced into the rotating machinery fault diagnosis system.With the combination of CBR technique,Basalstructure and development approach of the system are analyzed,and better cooperation among agents is realized.As a result,the system overcomes the limitation of single fault diagnosis method,and solves the contradictionbetween versatilityand adaptabilityof diagnosis software.Furthermore,a reference can be used for the research and development of multi-algorithmdiagnosis system of rotating machinery.The application in a factory's Networked Online Monitoring and Fault Diagnosis System for Turbine Fan shows that the system can diagnosefault rapidly and exactly.     

Knowledge Representation for Rotative Machine Fault Diagnosis Based on Semantic Net

Aiming at knowledge representation problem of expert system for rotary machinery fault diagnosis, semantic net knowledge representation is discussed. Semantic net to represent expert knowledge of rotary machinery fault diagnosis is explored. Its realizing method is afforded in computer language, in addition, demostrating model is also developed in Visual C++. The result shows good validity of the knowledge representation.     

Fault diagnosis of rotating machinery based on SVM 2PTMC

A method is proposed to determine the priority class of binary tree SVM. Firstly, the vibration signals of rotating machinery are colleted through a rotating machinery fault experimental platform and data acquisition system. The signals are from 5 different conditions, i.e. rotor normal, rotor unbalance, rotor misalignment, rotor bearing inner ring cracks and rotor bearing outer ring cracks. Then the signals are disposed by zero-mean and the main frequency band of the vibration signals are reconstructed to, extract the dimensionless time domain as characteristic value. Finally, the priority class of SVM 2PTMC can be determined by the correct inspection rate of parallel SVM. Training samples can be completely divided in experiments, which verifies the effectiveness of this method.     

The Application of Fuzzy Neural Network in the Detection for Moving Object

A fuzzy neural network(FNN) of detection for moving object based on BP algorithm is described in this paper.The correctness of the FNN in signal detection for moving object and fault diagnosis for instrument is proved by experiments.     

State and Prospects of Diesel Engine Fault Diagnosis Technique

As a kind of complex power machinery, diesel engine is being paid more and more attention to its condition monitoring and fault diagnosis technology. In the fault diagnosis field of diesel engine, the technique of signal process, character abstraction and identification method have formed a system, but there is a certain distance away from practical. This paper analyzes the common faults and influencing factor of diesel engine. The principle, characteristic and disadvantage of modern fault diagnosis technology, such as various time-frequency methods based on vibration signal, speed fluctuation method, iron content and spectrometry, grey system theoretical diagnosis method, artificial neural network and expert system fault diagnosis method, were reviewed. The difficulties and the development direction of diesel engine fault diagnosis were put forward in the end.     

Order Tracking Method Based on Time-Frequency Analysis

The fault character of moving parts of rotating machinery most time is speed related. The method of order spectrum analysis is more efficient than usual frequency analysis.Two general methods for realizing order tracking are discussed, and a new order tracking method based on time-frequency analysis is proposed. The time frequency order tracking method only depends on software for order tracking, which is a strong supplement to traditional methods, and specially satisfied the require of virtual instruments, so there is a good prospect for its using.     

Research and Application of Information Fusion Technology on Machinery Fault Diagnosis

Aiming at insufficiency exists in the process of machinery fault diagnosis at present, the paper takes consider of the application of information fusion technology in fault diagnosis to make fault diagnosis effectively. The applications of information fusion methods based on neural network, the Bayesian theory and the D-S theory are discussed in detail. Example is also given to explain the validity of information fusion technology in diagnosis analysis.     

Fuzzy c-means Cluster Over Gentic Algorithm

Fuzzy c-means (FCM) algorithm is dynamic cluster algorithm whose result often is local optimal decision. There often exists insignificant clustering in the result of Fuzzy c-means algorithm when traditional union, Intersection and inclusion work in fuzzy set. Our research indicates there are no insignificant clustering in the result of Fuzzy c-means algorithm over genetic algorithm and partial optimal solution can be avoided with this algorithm to a certain extent. The coding, select, corresponding crossover and mutation operators are designed. Finally we compared the performance of GFCM and FCM with testing data. Results show that the performance of GFCM is far better than FCM.     

谱聚类在给水管网分区优化中的应用

利用图划分技术和图论算法实现给水管网分区。根据给水管网分析,确定分区数量,建立权重邻接矩阵并计算图拉普拉斯矩阵及其特征向量,通过多路图划分对隐藏在特征向量中的聚类信息进行数据挖掘,采用遗传算法和K均值方法实现最佳节点聚类。利用PageRank和最短路径算法确定水表和阀门位置,最终实现给水管网优化分区。实际给水管网模型分区实例表明所提方法在给水管网分区的有效性。     

The Application of Fuzzy Neural Networks for Power Transformer Fault Diagnosis

This paper presents the application of fuzzy neural networks (FNN) in power transformer faults diagnosis.A FNN model is builded,in which input is transformer oil color spectrum analysis and output is fault type.The test results of fault examples show its effectiveness and potential applicable worthiness.     

Application of Self-tuning Models to Air Handling Units for Fault Detection

Building management control systems (BMCS) are widely employed in modern buildings. The huge amount of data available on central stations and outstations provide rich information for fault diagnosis of HVAC systems. An online fault diagnosis method for variable air volume air handling units was presented using self-tuning HVAC component models. The model parameters are tuned online by using a genetic algorithm (GA) which minimizes the error between measured and estimated performance data, so high modeling accuracy is assured. If the error between measured and estimated performance data exceeds preset thresholds, it means the occurrence of faults or abnormalities in the air handling unit system. The statistical method of selecting thresholds also is presented. The fault detection method was tested and validated using data collected from real HVAC systems. The results of validation show that the fault detection method can be integrated in BMCS systems to detect faults in air handling unit systems efficiently.     

Drift error compensation algorithm for fiber optic gyro based on T-S fuzzy modeling

A drift error nonlinear compensation algorithm for Fiber Optic Gyro (FOG) is presented based on T-S fuzzy model with the antecedent parameters identified by G-K clustering algorithm and the error model of T-S fuzzy model with the consequent parameters identified by least square algorithm. The computed results show that this model can compensate the original data effectively, while the error principles of FOG do not need to be understood well. Comparing with the original data, compensation with linear fitting and compensation with neural network, the absolute error of the proposed model reduces by 99%, 96% and 10%, respectively. The error variance reduces by 99%, 98% and 20%, respectively. The results indicate that this proposed algorithm can be simply operated with high precision and easy to realize in engineering.     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

Fault Section Diagnosis Algorithm of Distribution Networks Based On Topology Identification

An algorithm of fault section diagnosis based on topology identification for distribution networks is presented. By decomposing the topologic matrix which describing the distribution network into two parts,one part only contain the complex coupling factor ,and the other ignore the complex coupling factor. Using this method, the fault zones in distribution network can be identified and isolated efficiently, and the vertexes of the zones can also be identified automatically. This approach adapts to the changefully network structures.     

Application of Fuzzy-Set Clustering for Regional Typologies

Qualitative and quantitative procedures have been used to aggregate communities and counties for regional economic analysis. However, Once aggregated, communities and counties are perceived as homogeneous entities; this often belies the diversity that may exist. In order to capture the non-uniqueness of counties, fuzzy-set clustering procedures were employed to derive a typology of Nevada counties. Fuzzy-set clustering procedures employing fuzzy-set membership values and possibility theory derive county membership values associated for specific county clusters. Information from fuzzy partitions yields a means for posterior evaluation of county clusters which is independent of the algorithm producing them. From county membership values calculated from results of the fuzzy-set clustering analysis for Nevada, specific economic development programs for aggregate and individual counties are derived.     

A MATHEMATICAL MODEL OF FUZZY EVALUATION METHOD FOR ENVIRONMENTAL POLLUTION

The problem of fuzzy evaluation of environmental pollution is importantboth in environment science and in environmental protection.Usual evaluationmethods have their own characteristics.This paper puts forward a mathematicalmodel of fuzzy evaluation method in grades by means of systematic analysisfor environmental pollution.At the same time,it assigns weighted values byordering based on paired comparison.     

基于数据包络分析的农机维修企业技术效率研究

为综合评价农机维修企业运营情况,用数据包络分析(DEA)法和文本聚类法对调研的116个农机维修企业进行分类统计和技术效率分析.结果表明:农机维修企业技术效率处于较低水平,平均生产技术效率为0.642,纯技术效率和规模效率分别为0.736、0.880.不同类型农机维修企业效率不同,以组建方式分,品牌三包型技术效率最高、社...