FAULT DIAGNOSIS OF ROTATING MACHINERY USING KNOWLEDGE-BASED FUZZY NEURAL NETWORK

A novel knowledge-based fuzzy neural network （KBFNN） for fault diagnosis is presented. Crude rules were extracted and the corresponding dependent factors and antecedent coverage factors were calculated firstly from the diagnostic sample based on rough sets theory. Then the number of rules was used to construct partially the structure of a fuzzy neural network and those factors were implemented as initial weights, with fuzzy output parameters being optimized by genetic algorithm. Such fuzzy neural network was called KBFNN. This KBFNN was utilized to identify typical faults of rotating machinery. Diagnostic results show that it has those merits of shorter training time and higher right diagnostic level compared to general fuzzy neural networks.

APPLICATION OF MULTI-SENSOR DATA FUSION BASED ON FUZZY NEURAL NETWORK IN ROTA TING MECHANICAL FAILURE DIAGNOSIS

At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.

Employing adaptive fuzzy computing for RCP intelligent control and fault diagnosis

Loss of coolant accident(LOCA),loss of fluid accident(LOFA),and loss of vacuum accident(LOVA)are the most severe accidents that can occur in nuclear power reactors(NPRs).These accidents occur when the reactor loses its cooling media,leading to uncontrolled chain reactions akin to a nuclear bomb.This article is focused on exploring methods to prevent such accidents and ensure that the reactor cooling system remains fully controlled.The reactor coolant pump(RCP)has a pivotal role in facilitating heat exchange between the primary cycle,which is connected to the reactor core,and the secondary cycle associated with the steam generator.Furthermore,the RCP is integral to preventing catastrophic events such as LOCA,LOFA,and LOVA accidents.In this study,we discuss the most critical aspects related to the RCP,specifically focusing on RCP control and RCP fault diagnosis.The AI-based adaptive fuzzy method is used to regulate the RCP’s speed and torque,whereas the neural fault diagnosis system(NFDS)is implemented for alarm signaling and fault diagnosis in nuclear reactors.To address the limitations of linguistic and statistical intelligence approaches,an integration of the statistical approach with fuzzy logic has been proposed.This integrated system leverages the strengths of both methods.Adaptive fuzzy control was applied to the VVER 1200 NPR-RCP induction motor,and the NFDS was implemented on the Kori-2 NPR-RCP.

Deep convolutional tree-inspired network:a decision-tree-structured neural network for hierarchical fault diagnosis of bearings

The fault diagnosis of bearings is crucial in ensuring the reliability of rotating machinery.Deep neural networks have provided unprecedented opportunities to condition monitoring from a new perspective due to the powerful ability in learning fault-related knowledge.However,the inexplicability and low generalization ability of fault diagnosis models still bar them from the application.To address this issue,this paper explores a decision-tree-structured neural network,that is,the deep convolutional tree-inspired network(DCTN),for the hierarchical fault diagnosis of bearings.The proposed model effectively integrates the advantages of convolutional neural network(CNN)and decision tree methods by rebuilding the output decision layer of CNN according to the hierarchical structural characteristics of the decision tree,which is by no means a simple combination of the two models.The proposed DCTN model has unique advantages in 1)the hierarchical structure that can support more accuracy and comprehensive fault diagnosis,2)the better interpretability of the model output with hierarchical decision making,and 3)more powerful generalization capabilities for the samples across fault severities.The multiclass fault diagnosis case and cross-severity fault diagnosis case are executed on a multicondition aeronautical bearing test rig.Experimental results can fully demonstrate the feasibility and superiority of the proposed method.

A REALIZATION OF FUZZY LOGIC BY A NEURAL NETWORK

This paper proposes a Fuzzy Neural Network (FNN) model, which uses a propagation algorithm. A logical operation is defined by a set of weights which are independent of inputs. The realization of the basic And,Or and Negation fuzzy logical operations is shown by the fuzzy neuron. A example in fault diagnosis is put forward and the result witnesses some effectiveness of the new FNN model.

A fuzzy neural network evolved by particle swarm optimization

A cooperative system of a fuzzy logic model and a fuzzy neural network(CSFLMFNN)is proposed,in which a fuzzy logic model is acquired from domain experts and a fuzzy neural network is generated and prewired according to the model.Then PSO-CSFLMFNN is constructed by introducing particle swarm optimization(PSO)into the cooperative system instead of the commonly used evolutionary algorithms to evolve the prewired fuzzy neural network.The evolutionary fuzzy neural network implements accuracy fuzzy inference without rule matching.PSO-CSFLMFNN is applied to the intelligent fault diagnosis for a petrochemical engineering equipment,in which the cooperative system is proved to be effective.It is shown by the applied results that the performance of the evolutionary fuzzy neural network outperforms remarkably that of the one evolved by genetic algorithm in the convergence rate and the generalization precision.

A NEURAL NETWORK BASED FAULT FUZZY DIAGNOSTIC SYSTEM

A fault fuzzy diagnostic system(FFDS) based on neural network and fuzzy logic hybrid is proposed. FFDS consists of two modes: a fuzzy inference mode and a rule learning mode. The fuzzy inference rules are stored in the memory layer. The excitation levels of the memory neurons reflect the matching degrees between the input vectors and the prototype rules. In the rule learning mode, the rules can be produced automatically through the cluster process. As an application case of this diagnostic system, the fault diagnosis experiment of the rotating axis is simulated.

Gear Transmission Fault Classification using Deep Neural Networks and Classifier Level Sensor Fusion

Gear transmissions are widely used in industrial drive systems.Fault diagnosis of gear transmissions is important for maintaining the system performance,reducing the maintenance cost,and providing a safe working environment.This paper presents a novel fault diagnosis approach for gear transmissions based on convolutional neural networks(CNNs)and decision-level sensor fusion.In the proposed approach,a CNN is first utilized to classify the faults of a gear transmission based on the acquired signals from each of the sensors.Raw sensory data is sent directly into the CNN models without manual feature extraction.Then,classifier level sensor fusion is carried out to achieve improved classification accuracy by fusing the classification results from the CNN models.Experimental study is conducted,which shows the superior performance of the developed method in the classification of different gear transmission conditions in an automated industrial machine.The presented approach also achieves end-to-end learning that ean be applied to the fault elassification of a gear transmission under various operating eonditions and with signals from different types of sensors.

A Novel Real-Time Fault Diagnostic System for Steam Turbine Generator Set by Using Strata Hierarchical Artificial Neural Network

The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.

Synthetic Intelligent Fault Diagnosis Technology for Complex Process

A fault diagnosis method of knowledge based fuzzy neural network is proposed for complex process, which is hard to develop practical mathematical model. Fault detection is performed through a knowledge based system, where fault detection heuristic rules have been generated from deep and shallow knowledge of the process. The fuzzy neural network performs the fault diagnosis task. This method does not need practical mathematical models of objects, so it is a strong implement for complex process.

Intelligent Fault Diagnosis in Lead-zinc Smelting Process

According to the fault characteristic of the imperial smelting process （ISP）, a novel intelligent integrated fault diagnostic system is developed. In the system fuzzy neural networks are utilized to extract fault symptom and expert system is employed for effective fault diagnosis of the process. Furthermore, fuzzy abductive inference is introduced to diagnose multiple faults. Feasibility of the proposed system is demonstrated through a pilot plant case study.

Intelligent Process Fault Diagnosis for Nonlinear Systems with Uncertain Plant Model via Extended State Observer and Soft Computing

There have been many studies on observer-based fault detection and isolation (FDI), such as using unknown input observer and generalized observer. Most of them require a nominal mathematical model of the system. Unlike sensor faults, actuator faults and process faults greatly affect the system dynamics. This paper presents a new process fault diagnosis technique without exact knowledge of the plant model via Extended State Observer (ESO) and soft computing. The ESO’s augmented or extended state is used to compute the system dynamics in real time, thereby provides foundation for real-time process fault detection. Based on the input and output data, the ESO identifies the un-modeled or incorrectly modeled dynamics combined with unknown external disturbances in real time and provides vital information for detecting faults with only partial information of the plant, which cannot be easily accomplished with any existing methods. Another advantage of the ESO is its simplicity in tuning only a single parameter. Without the knowledge of the exact plant model, fuzzy inference was developed to isolate faults. A strongly coupled three-tank nonlinear dynamic system was chosen as a case study. In a typical dynamic system, a process fault such as pipe blockage is likely incipient, which requires degree of fault identification at all time. Neural networks were trained to identify faults and also instantly determine degree of fault. The simulation results indicate that the proposed FDI technique effectively detected and isolated faults and also accurately determine the degree of fault. Soft computing (i.e. fuzzy logic and neural networks) makes fault diagnosis intelligent and fast because it provides intuitive logic to the system and real-time input-output mapping.

Fuzzy Integral Theory and Its Application in Fault Intelligent Integrated Diagnosis

In this paper, by employing the idea of 'dispersion first, then concentration'' adhering to the biological perceptual system, an idea of the multi-symptom-domain based fault consensus diagnosis is developed. From the point of group decision-making, the method based on neural networks to realize this diagnosis idea is studied, and a particular multi-symptom-domain based diagnosis strategy is proposed, which is based on fuzzy integral theory. Finally, a case study is given. The research results show that the proposed diagnosis strategy is available and more efficient than conventional methods.

基于ANN和FUZZY的装载机故障诊断模型

提出了一种基于神经网络和模糊理论的层次诊断模型 .针对装载机的特点建立的模糊系统可自动生成和调整隶属度函数 ,构造了一种平行的神经子网络 ,网络训练的速度和诊断准确率有明显提高 .模型缩小了知识库 ,减少了计算量 .本模型具有比较强的除噪能力 ,能将对故障信息敏感而对噪声不敏感的信息提取出来 .经仿真实验证明 ,识别效果良好 ,有效减少了误判和漏判 .

Fuzzy ART及其在故障诊断中的应用

ＦｕｚｙＡＲＴ是近几年出现的一种新型ＡＲＴ（ａｄａｐｔｉｖｅｒｅｓｏｎａｎｃｅｔｈｅｏｒｙ）技术，文中介绍了该技术的实现方法，并用它对实测的不同工况的机器振动信号进行自组织聚类，收到了令人满意的效果．分析结果表明，采用这种无督学习的神经网络具有有督学习神经网络所无法替代的优势．

基于改进图卷积神经网络的配网线路故障诊断

随着电网的不断扩容,系统结构越来越复杂,多故障频发,而多故障是故障诊断的关键和难点。为解决故障处理数据量大,需要快速、准确地诊断电网故障的问题,本文提出了一种基于模糊优化图卷积神经网络的配网故障诊断模型。首先处理采集的配网故障线路的特征数据;其次,搭建基于图卷积神经网络的故障诊断模型,利用模糊理论建立配电网故障的隶属函数;最后利用训练好的模型进行配网故障诊断。仿真结果表明,模糊优化图卷积神经网络对多故障诊断的准确率高于卷积神经网络以及其他方法,本文方法做出的诊断结果更加精确,综合诊断效果最好。

基于模糊神经网络的机械轴承故障诊断方法研究

针对机械轴承智能化故障诊断的需求,提出了一种融合模糊逻辑和神经网络的故障诊断方法。利用EMD-AR谱提取机械故障振动信号特征,将提取的特征向量作为训练样本库和检验样本库,运用模糊神经网络实现故障诊断。最后设计机械轴承故障诊断专家系统,并通过轴承故障诊断实例,验证了智能诊断技术在机械故障诊断领域可以较好地满足诊断需求。

基于ID3-CNN的旋转机械故障诊断研究

为解决旋转机械故障类型多、等级不均衡的故障诊断难题,构建了一种基于ID3决策树与卷积神经网络(ID3-CNN)的故障诊断模型。首先对原始信号进行人工时域特征提取,使用t-SNE降维可视化提取出特征混叠的故障,而后利用卷积运算对特征混叠的故障进行二次特征提取,提高模型的特征表达能力,最后使用ID3决策树和卷积神经网络对不同等级的故障进行分类。在轴承数据集上对模型进行了验证,结果表明,严重故障的诊断准确率达到100%,轻微故障的诊断准确率达到95%。与传统的支持向量机及二维卷积神经网络比较,提高了模型的诊断准确率及特征提取能力。

车载空调制冷系统故障诊断研究

为实现车载空调制冷系统故障诊断功能,快速判断空调制冷系统可能出现的故障类型,文章建立车载空调制冷系统一维仿真模型,并以压缩机进出口温度、压力等参数为特征参数,冷凝器风量降低、制冷剂泄漏等故障为输出目标结果,构建车载空调制冷系统的反向传播(back-propagation,BP)神经网络故障诊断模型和决策树故障诊断模型。研究结果表明:当冷凝器风量降低时,压缩机排气温度与排气压力上升,空调系统的制冷量和性能系数(coefficient of performance,COP)下降。通过对比2种不同诊断策略的仿真测试结果发现,采用BP神经网络进行车载空调制冷系统故障诊断的准确率可以达到92.5%。

基于线性决策函数的中低压配电网单相接地故障诊断方法

为确保中低压配电网安全稳定供电,研究基于线性决策函数的中低压配电网单相接地故障诊断方法,提升故障诊断效果。通过在线性判别分析法内引入筛选压缩法与近似矩阵法,得到改进的两步线性判别分析法;结合Fisher线性决策函数,提取最佳的中低压配电网单相接地故障特征;在概率神经网络内,输入单相接地故障特征,输出单相接地故障诊断结果。试验结果证明:所提方法可有效提取单相接地故障特征,且各类别故障特征间并无混淆情况,具备较优的故障特征提取效果,故障诊断精度较高。