液压系统故障的模糊诊断方法

论述了液压系统的模糊性和模糊诊断意义，提出了模糊诊断的基本原则，通过某一液压系统，介绍了在生产现场中方便易行的模糊诊断方法。     

机械加工误差源模糊智能诊断系统建模研究

提出了基于模糊理论的机械加工误差源诊断分析系统的模型，并对该系统中各模型的建立方法进行了详细研究。论述了在机械加工误差源诊断领域利用模糊理论对模糊信息、模糊知识进行数字化表达的方法；确定了诊断知识与加工误差源之间的模糊关系；实现了以知识为对象的符号智能和置数据为对象的计算智能的有机结合，对机械加工误差源模糊智能诊断分析系统的开发具有指导意义。     

液压故障的模糊诊断原则与方法

阐述了液压设备故障性质及模糊性，提出了模糊诊断综合评判原则、分层分段诊断逐步深入原则、过去和现在状况相结合原则等七种基本原则，在此基础上确立了模糊诊断的基本方法。将液压系统故障的模糊现象与相关因素间的关系用数字方式描述，并用数字化方法进行运算。通过某液压系统故障诊断实例，对该系统中的伺服阀和溢流阀故障信息进行量化，确定关系密切系数，通过运算验证了模糊诊断方法的可靠性和可行性，该方法简单，便于工程技术人员掌握，有利于推广。     

多专家故障诊断系统研究

针对已有诊断系统的知识可重用性差、问题求解能力不足的缺点，研究了多专家协同诊断系统，采用模糊积分方法进行多专家诊断的结论融合。为了寻找最优的模糊密度值，提出了利用遗传算法进行模糊密度值优化，提高基于模糊积分方法的多专家诊断结论融合精度。针对所提出的算法，结合工程实际数据进行了分析，结果表明了该方法的有效性。     

基于模糊逻辑推理的汽车故障诊断的研究

针对汽车故障诊断中故障现象与故障原因之间复杂的不确定关系,提出了汽车故障的模糊逻辑诊断方法.给出了模糊逻辑诊断的模糊诊断模型;根据传统方法建立模糊诊断矩阵的不足,提出了采用人工智能的方法,利用专家知识动态建立模糊诊断矩阵的过程.结合实例在计算机上实现了汽车故障的模糊诊断,验证了该方法诊断的正确性.     

实现内燃机振动故障诊断专家系统的方法

本文结合传统的振动诊断技术，模糊诊断方法和人工智能技术，对专家系统在内燃机振动诊断方法的应用进行了初步的探讨。     

小波图形显示算法在旋转机械模糊自动诊断中的应用

本文建立了济南炼油厂重油催化机组的模糊数学模型，开发该系统基于频域的自动诊断实用程序，对平稳信号的分析可直接得到诊断结果。而对于非平稳信号则采用小波分解法，重点采用了小波图形显示算法保证了信号经小波分解后的频域分辨率，最终将图形显示算法与模糊自动诊断系统相结合用于生产实践中非平稳信号的分析诊断，完善了该系统的功能，并得到了很好的效果。     

模糊-灰色关联-神经网络故障诊断方法研究

提出模糊-灰色关联-神经网络这一故障识别方法，通过将模糊理论和灰色关联度相结合，形成所提取故障特征值的模糊关联度，将模糊关联度作为神经网络的输入量进行变速箱故障种类的识别。实现了针对在不同工作状态下的故障识别，为实现故障智能化诊断提出了一和综合性神经网络故障诊断方法。     

分布式多Agent诊断系统研究

建造了面向制造过程监控的分布式多Ａｇｅｎｔ诊断系统结构。着重对多Ａｇｅｎｔ系统的模糊关联模型进行了研究，建立了Ａｇｅｎｔ工作状态的表达机制，确定了Ａｇｅｎｔ间的工作状态影响关系，并构建了多Ａｇｅｎｔ模糊关联矩阵。最后基于多Ａｇｅｎｔ模糊关联模型，研究了多Ａｇｅｎｔ系统的模糊关系诊断与决策问题。     

设备故障的二级模糊综合诊断方法的研究

在设备故障诊断中,由于故障原因繁多且相互交织和影响,导致设备故障具有一定的模糊性。本文将模糊理论引入设备故障的诊断过程中,针对每种故障原因,建立一个故障原因两两比较模糊矩阵,通过故障原因两两比较模糊矩阵,确定故障症状对故障原因的隶属度,从而建立了模糊诊断矩阵,在模糊综合诊断过程中,由于模糊算子对信息的取舍均带有一定的倾向性,为抑制其偏激,我们首先分别采用5种模糊算子进行综合诊断,然后根据诊断的结果,将可信度较高的3种模糊算子诊断的结果加权平均,作为最终诊断结果,以提高诊断结果的可靠性。最后,通过实例演示了故障的诊断过程。     

基于DataSocket和VB技术融合的异地故障诊断系统开发

应用远程网络,实现异地故障诊断,可以更大范围地集成技术资源,使得异地诊断专家参与到设备故障信号的分析,因此,异地故障诊断系统的开发是最关键的技术支撑.介绍了基于DataSocket技术的网络通讯实现方法及ActiveX控件在VB中的使用.以实例,详细叙述了DataSocket技术和VB技术的结合,给出了通过Web服务器提供网络服务的形式,实现了异地故障诊断数据网络传输的原型系统.     

基于高阶倒谱熵的齿轮故障诊断

在高阶统计量的基础上提出了高阶倒谱的概念,利用信息熵作为其结果的量化指标,应用于齿轮的故障诊断中。由于高阶倒谱同时具备了高阶统计量和倒谱的优点,因此在进行齿轮的故障诊断中具有其独特的优越性,对齿轮的故障反应灵敏,可以用于实现齿轮的早期故障识别;结合其他诊断方法,能够更好地获得齿轮的在线运行状态和故障分析结果。     

Intelligent diagnosis methods for plant machinery

This paper reports several intelligent diagnostic approaches based on artificial neural network and fuzzy algorithm for plant machinery, such as the diagnosis method using the wavelet transform, rough sets, and fuzzy neural network; the diagnosis method based on the sequential inference and fuzzy neural network; the diagnosis approach by the possibility theory and certainty factor model; and the diagnosis method on the basis of the adaptive filtering technique and fuzzy neural network. These intelligent diagnostic methods have been successfully applied to condition diagnosis in different types of practical plant machinery.     

基于模糊专家系统的凝汽器故障诊断

本文以VC 6.0和CLIPS为工具设计了一个用于火电厂故障诊断的模糊专家系统。介绍了模糊专家系统构成及特点.实现了故障诊断的知识录入与修改及故障的自动诊断,搜集了一些火电厂凝汽器系统故障诊断知识并进行了模糊专家系统诊断验证。     

Intelligent diagnosis method for machinery by sequential auto-reorganization of histogram

This paper proposes an intelligent sequential diagnosis method for plant machinery using statistical filter (SF), signal histogram and genetic programming (GP). The SF is used to cancel noise from the measured vibration signal for raising the accuracy of fault diagnosis. Since the vibration signal measured for the condition diagnosis conforms to various probability distributions, histograms are used to reflect the signal features instead of the conventional symptom parameters (SPs). Then, the genetic programming (GP) is used to generate new variables termed “integrated symptom parameters” (GP-ISPs) from the histogram. GP-ISPs obtained by the auto-reorganized histogram can reflect features and raise the sensitivity of the fault diagnosis by the greatest amount possible. Furthermore, a sequential diagnosis algorithm using GP-ISPs is also proposed to realize precise diagnosis for distinguishing fault types. Finally, the effectiveness of the proposed method is verified by applying it to the fault diagnosis of a centrifugal blower. The proposed method has wide applicability and is practical in the field of machinery fault diagnosis.     

Model-based monitoring and fault diagnosis of fossil power plant process units using Group Method of Data Handling

This paper presents an incipient fault diagnosis approach based on the Group Method of Data Handling (GMDH) technique. The GMDH algorithm provides a generic framework for characterizing the interrelationships among a set of process variables of fossil power plant sub-systems and is employed to generate estimates of important variables in a data-driven fashion. In this paper, ridge regression techniques are incorporated into the ordinary least squares (OLS) estimator to solve regression coefficients at each layer of the GMDH network. The fault diagnosis method is applied to feedwater heater leak detection with data from an operating coal-fired plant. The results demonstrate the proposed method is capable of providing an early warning to operators when a process fault or an equipment fault occurs in a fossil power plant.     

A quality control method by ultrasonic vibration energy and diagnosis system at trimming process

In this paper, the characteristics in mechanical properties of ultrasonic cold forging treatment (UCFT) used for the trimming knife and the effects of ultrasonic vibration energy (UVE) into the trimming process on the state of the strip cutting face were studied. And a diagnosis system to quality control for trimming knife and strip cutting face was developed and installed in plant. By the plant application of UCFT, service life of knife was more increased over 100% than that of conventional knife and using the developed diagnosis system, the knife breakage and saw ear have been perfectly detected and quality control of trimming face is effectively obtained.     

Distributed flexible reconfigurable condition monitoring and diagnosis technology

As manufacturing becomes increasingly decentralized, flexible and reconfigurable, more research needs to be done on monitoring and diagnosis technology that accommodate these new trends. The distributed condition monitoring and diagnosis technology based on the “flexible and reconfigurable” concept is studied here. A condition monitoring diagnosis model based on the distributed flexible and reconfigurable idea is proposed in this paper. The component makeup and functions of this model are discussed in detail. The model can fulfill in most instances the manufacturing system requirements for changing the configuration of the monitoring diagnosis system according to different manufacturing system configurations. This model also realizes the flexibility and reconfigurability of the monitoring diagnosis system in some degree. The model has already spawned a successful prototype for monitoring a chemical plant in accomplishing monitoring and control of the production process and equipment. Finally, some future research work is pointed out. __________ Translated from Chin. J. Mech. Eng., 2004, 40 (3) (in Chinese)     

A hybrid fault diagnosis methodology with support vector machine and improved particle swarm optimization for nuclear power plants

The safety and public health during nuclear power plant operation can be enhanced by accurately recognizing and diagnosing potential problems when a malfunction occurs. However, there are still obvious technological gaps in fault diagnosis applications, mainly because adopting a single fault diagnosis method may reduce fault diagnosis accuracy. In addition, some of the proposed solutions rely heavily on fault examples, which cannot fully cover future possible fault modes in nuclear plant operation. This paper presents the results of a research in hybrid fault diagnosis techniques that utilizes support vector machine (SVM) and improved particle swarm optimization (PSO) to perform further diagnosis on the basis of qualitative reasoning by knowledge-based preliminary diagnosis and sample data provided by an on-line simulation model. Further, SVM has relatively good classification ability with small samples compared to other machine learning methodologies. However, there are some challenges in the selection of hyper-parameters in SVM that warrants the adoption of intelligent optimization algorithms. Hence, the major contribution of this paper is to propose a hybrid fault diagnosis method with a comprehensive and reasonable design. Also, improved PSO combined with a variety of search strategies are achieved and compared with other current optimization algorithms. Simulation tests are used to verify the accuracy and interpretability of research findings presented in this paper, which would be beneficial for intelligent execution of nuclear power plant operation.