屈梁生论文集:机械监测诊断中的理论与方法 本书特色
《机械监测诊断中的理论与方法》适合大学相关专业的教师、研究生，以及从事机械故障诊断研究的科研人员阅读。
屈梁生论文集:机械监测诊断中的理论与方法 目录
屈梁生院士简介序机床自动停刀机构精度的研究热变形对导轨磨削精度的影响计量光栅的几何、光学特性评几项超微米技术的进展自回归谱在机器故障诊断中的应用时间序列分析在机器状态识别中的应用On—line Surveillance of Process Equipment Using Autoregressive Feature Extraction and Walsh TransformationKULLBACK—LEIBLER信息数在识别机器振动信号功率谱中的应用An ExDerimental Research on Optical Fiber Sensor and Its Application to the Grinding Process Supervision机器故障诊断技术及其现状与展望The Evaluation of Low—Frequency Knock of Precise Gearbox Via Spectra Distance Measure Autoregressive Cepstrum and Its Application in Noise Diagnosis of GearboxesOn—line Surveillance of a Grinding Process Via a Kullback—Leibler Information Number Statistical Control of Low Frequency Knock in Machine Tool Gearbox Production光纤传感器及其在磨削过程监测中的应用The Thermal Behavior of Machine Tool GuidwaysThe Application of Maximum Entropy Spectrum in Analysis of the Accuracy 0．f Precision Gear Transmission The Evaluation of Low—frequency Knock in the Gearbox of Modern Machine Tools Friction Failure Diagnosis of Steam Turb0—Generator Sets Via Principal Components andAutoregressive TechniqueGrinding Process Supervision Via Information Distance Measure A New Approach to Computer—aided Vibration Surveillance of Rotating Machinery Discovering the HolospectrumThe H010spectrum：A New Method for Rotor Surveillance and Diagnosis 专家系统中知识一致性和完备性检验的一种方法The Holospectrum：A New FFT Based Rotor Diagnostic MethodA New Me!thod Evaluating Operating Condition for Rotating MachineryTime—frequencv Distributions of Vibration Signals in Rotating Machinery A Rule—based ExPert System for Real—Time Gear—manufacturing Process Control 回转机械振动频谱的模糊分类及应用The Phase Information in the Rotor Vibration Behaviour Analysis Investigation of the Special Trending Method for Rotating Machinery Monitorin9Rotating MachineRy Fault Diagnosis Using wigner Distri bution全息谱分析方法的原理和应用神经网络在大型回转机械故障诊断中的应用全息谱技术用于化工厂机械故障诊断机械监测诊断中的谱距分类与预报大型机组诊断信息的深层次处理问题IntelugentConntro1 oftheGear—Shaving Process转子径向摩擦故障诊断技术的研究HomoSourCe ANC and Its Application to the Machinery DiagnosisStudV and PerformanCe Evaluation of Some Nonlinear Diagnostic：Methods for．Large Rotating Machineryorbit Corrlplexity：A New Criterion for Evaluating the Dynamk：QuantyofRotorsystems齿轮联轴节对中不良振动信息研究Fault Prognosis for Large Rotating Machinery Using Neural Net work基于二维全息谱的回转机械亚同步振动分析Vibrational Diagnosis of Machine Parts Using the wavelet．：Packet Technique转子横向裂纹振动诊断技术的研究谈谈机组故障的可诊断性问题谈谈机组故障的可诊断性问题(续)小波包原理及其在机械故障诊断中的应用Somc Analvtical Problems of High Performance Flexm．e Hinge and Micro—mot：ion Stage Design The Prognosis of Vibration Condition for a 200 MW Turbo—Generator Set Using Anificial NeuralNetworkThe Noise Suppression Chain—A New Approach to Time Series Preprocessmg共轭梯度神经网络的研究机械故障诊断技术与当代前沿科学(一)机械故障诊断技术与当代前沿科学(二)机械故障诊断技术与当代前沿科学(三)小波分析的工程理解及其在机械诊断中的应用小波包的移频算法与振动信号处理柔性转子键相信号初始相位及振动信号相位的确定TeSt SCquencing and Diagnosis in Electronic System withDecision Table大型回转机械故障的组合网络识别方法基于神经网络的喘振早期发现Predicting Grinding Burn Using Artificial Neural Networks非线性动力系统理论在机械故障诊断中的应用基于信息优化的前馈神经网络及其应用人工神经网络与机械工程中的智能化问题全息谱的分解及其在机械诊断中的应用多分辨小波网络的理论及应用小波分析及其在机械诊断中的应用The Fauh Recognition Problem in Engineering Diagnost icsFractal Geometry Used for Chartactcrization of Grinding wheel Profiles全息动平衡技术：原理与实践多平面平衡中平衡面相关问题的处理全息谱力、力偶分解法在全息动平衡中的应用Optimization of the Measuring Path on a Coordinate Measuring Machine Using Genetic Algorithms机械诊断中的故障识别问题平稳熵：一种新的机组运行瞬时稳定性定量化监测指标转子横向裂纹故障的诊断信息提取大机组振动信号复杂性的定量描述截尾奇异值分解技术在动平衡中的应用三维全息谱分解在回转机械诊断中的应用研究回转机械诊断信息的集成：全息谱技术十年基于主分量分析的噪声压缩技术研究全息谱十年：回顾与展望转子动平衡中的相关平衡面问题基于扭振信号的齿轮故障诊断研究Intelligent Method for Online Vibration MonitoringA Soft Computing Based Approach for Multisensor Data FusionFeaturc Extraction Using Continuous WaveletTransform and Its Application for Mechanical Fault Diagnosis基于概率神经网络的机组状态多步预报方法A Difference Resonator for Detecting weak SignalsA Synergetic Approach to Genetic Algorithms for Solving Traveling Salesman ProblemA Genetic Algorithm Based Balancing Framework for Flexible RotorsOne Decade of Holospectral Technique：Review and Prospect小波分析及其在压缩机气阀故障检测中的应用研究改进的决策树生成算法及条件决策表的创建基于概率神经网络
屈梁生论文集:机械监测诊断中的理论与方法 节选
《机械监测诊断中的理论与方法》全文收录了中国工程院院士屈梁生的166篇高水平论文。内容涉及全息谱、全息动平衡、独立分量／主分量、质量保障与优化、小波分析、遗传算法、贝叶斯网络、支持向量机、模糊理论、神经网络、专家系统、监测预报、Wigner分布、噪声抑制、回归分析、信息化生产、粗糙集、决策表／决策树、循环统计、统计模拟、EMD、故障诊断理论与方法等。另附有345篇论文的总篇目、论文主题索引和研究对象索引三个附录。
屈梁生论文集:机械监测诊断中的理论与方法 相关资料
插图：INTRODUCTIONNowadays，the grinding operation is one of the mostimportant metal processing methods.The per.formance and reliability of modem machine productsare seriously influenced by the surface perfection.dimensiona！and geometrical accuracies of their ele.ments.the majority of which are finished bv digfferentkinds of grinding operations.In modem automaticgrinders it is quite necessary to adopt the on.1inesurveillance via dynamic signal recognition in orderto prevent the degradation and deterioration ofworkpieces as the result of wheel wear andlOSS of itscutting ability.In general，the wheel life between twosucceeding turning operations is characterized by thevariation of grinding sound，increase of grindingforces and the violence of grinding chatter。which canbe easily recognized by experienced operators.Butwith regard to the automatic in-process recognitionof these signals the closest attention js now solicitedin order to monitor the performance of wheelandgrinder under different grinding conditions and toprognosticate the remaining wheel lift.In this paper.a new criterion called the Kullback-Leibler informa.tion humber based on time.series analysis and infor.mation theory is suggested to monitor the per.formance of whee！and grinder under difierentworking conditions and to prognosticate the remain.ing wheel life.The computer flow chart for supc-vision of the grinding cycles is also designed andtested.PROCEDURE The main aspccts for on.1ine surveillance of agrinding process by means of the Kullback-Leiblerinformation humber are as follows：（1）measurementof the dynamic signals emitted in difierent stages ofgrinding process between two succeeding trueingoperations；（2）A／D conversion.of each signal to formthe corresponding discrete time series；（3）establish.ement of the reference models.These models can beautoregressive moving average（ARMA）models orsimplified autoregressive（AR）models[1]
