基于RBF神经网络观测器的非线性系统鲁棒故障检测方法

针对一类仿射非线性动态系统，提出一种基于网络非线性观测器的鲁棒故障检测与隔离的新方法，采用ＲＢＦ神经网络逼近观测器系统中的非线性项，提高了状态估计的精度，证明了状态估计误差稳定且渐近收敛到零；同时提出了一种新的网络权值调整指标方法，提高了神经网络故障分类器的泛化能力，从而保证该方法对监测系统的建模 外部扰动具有良好的鲁棒性。     

一类基于神经网络非线性观测器的鲁棒故障检测

针对一类仿射非线性动态系统,提出了一种基 于神经网络非线性观测器的鲁棒故障检测与隔离的新方法．该方法采用神经网络逼近观测器 系统中的非线性项,提高了状态估计的精度,并从理论上证明了状态估计误差稳定且渐近收 敛到零;另一方面引入神经网络分类器进行故障的模式识别,通过在神经网络输入端加入噪 声项来进行训练,提高神经网络的泛化逼近能力,从而保证对被监测系统的建模误差和外部 扰动具有良好的鲁棒性．最后,利用本文方法针对某型歼击机结构故障进行仿真验证,仿真 结果表明本文方法是有效的．     

基于自组织神经网络的信息融合在故障诊断中的应用

提出了一种用于某机舵面系统故障诊断的方法．应用自组织神经网络的非线性拟合能力扩展相关传感器的测量信息,采用D-S证据论算法将相关传感器的输出信息进行融合．信息融合诊断策略根据这些信息确定出故障,同时对故障信号进行识别．建立了某机舵面系统故障诊断的数学模型,并进行了计算机仿真．仿真实验结果表明,该故障诊断结构形式能够对舵面常见故障进行有效的识别和告警,显著地降低了故障诊断的不确定性,提高了故障模式的识别率．     

一种基于离散马尔科夫模型的神经网络可靠性设计方法

本文给出了一和循环前馈神经网络来表示具有ｎ个状态的离莠马尔科夫模型,及其二次能量函数,并根据梯度下降规则,给出了相应的权值修改规则。利用该神经网络方法可以有效地对容错硬件进行可靠性设计。     

基于粒子滤波状态估计的滚动轴承故障识别方法

提出了一种基于粒子滤波状态估计的滚动轴承故障识别方法,该方法主要包括故障模型建立和故障识别两个步骤。在故障模型建立部分,首先依据滚动轴承不同故障状态下的振动信号,建立对应的自回归模型,作为故障模型;在故障识别部分,将正常状态下对应的模型,转化为状态空间模型,设计粒子滤波器,然后对不同的故障状态进行估计,提取其残差的相关特征,并结合模型参数特征应用BP神经网络识别算法进行故障识别。最后以美国凯斯西储大学的滚动轴承振动数据为例,验证了该方法的有效性。     

基于粗糙集神经网络的旋转机械故障诊断

针对旋转机械故障和故障征兆关系的复杂性及神经网络在故障诊断中存在网络结构复杂和训练时间长等问题,提出了一种基于粗糙集与神经网络结合的故障诊断方法;采用自组织映射方法对属性进行离散化,设计了一种自适应遗传算法对属性进行约简,将获得的最小条件属性集作为神经网络的输入;以轴承的故障诊断为例进行分析,结果表明,该方法在保证诊断正确率的同时,可以有效简化神经网络的结构,降低网络的训练时间;另外,设计的自适应约简算法在保证获得最小约简的基础上,大大加快了收敛速度;该方法可推广应用在其它机械设备的故障中。     

Diagnosis of process valve actuator faults using a multilayer neural network

This paper investigates the ability of a multilayer neural network to diagnose actuator faults in a Fisher-Rosemount 667 process control valve. A software package that comes with the valve is used to obtain experimental figures of merit related to the position response of the valve given a step command. The particular values of the dead time, peak time, percent overshoot, steady state error, 63% and 86% rise times, and gain are shown to depend on the severity of three commonly occurring faults: incorrect supply pressure, actuator vent blockage, and diaphragm leakage. The relationships between these parameters form fault signatures for each operating condition that are subsequently learned by a multilayer feedforward neural network. The results show that the trained network has the capability to detect and identify various magnitudes of the faults of interest. In addition, it is observed that the network has the ability to estimate fault levels not seen by the network during training. The approach presented in this paper allows the existing instrumentation to be utilised without modification. Thus, the proposed methodology is practical to implement.     

A study of fault diagnosis in a scooter using adaptive order tracking technique and neural network

An expert system for scooter fault diagnosis using sound emission signals based on adaptive order tracking and neural networks is presented in this paper. The order tracking technique is one of the important approaches for fault diagnosis in rotating machinery. The different faults present different order figures and they can be used to determine the fault in mechanical systems. However, many breakdowns are hard to classify correctly by human experience in fault diagnosis. In the present study, the order tracking problem is treated as a parametric identification and the artificial neural network technique for classifying faults. First, the adaptive order tracking extract the order features as input for neural network in the proposed system. The neural networks are used to develop the training module and testing module. The artificial neural network techniques using a back-propagation network and a radial basis function network are proposed to develop the artificial neural network for fault diagnosis system. The performance of two techniques are evaluated and compared through experimental investigation. The experimental results indicated that the proposed system is effective for fault diagnosis under various engine conditions.     

基于多重结构神经网络的歼击机结构故障检测

提出了一种基于多重结构神经网络的故障检测方法。针对以歼击机为代表的非线性系统中存在的突发结构故障，构造了一个多重结构神经网络，在输入层对残差信号进行二进离散小波变换，提取其在多尺度下的细节系数作为故障特征向量，并将其输入到神经网络分类器进行相应的模式分类，之后再利用下一级的故障度辨识神经网络对故障的大小进行辨识。仿真结果表明，本文方法为歼击机组合结构故障的检测提供了有效的方法和途径。     

基于退火BP神经网络的模拟电路故障诊断方法

针对BP神经网络在模拟电路故障诊断中存在的固有缺陷,提出模拟退火(simulated annealing,SA)算法与BP算法相结合的神经网络故障诊断方法,发挥SA算法全局寻优的特点来优化BP神经网络的学习过程,避免网络训练收敛速度慢和陷入局部极小;应用MATLAB编写程序对一典型模拟电路的仿真故障样本进行训练,在其它初始条件都相同的情况下,基于提出的算法神经网络比传统BP算法神经网络训练收敛速度快且诊断精度高,是一种新的有效的模拟电路故障诊断方法。     

基于小波神经网络的组合故障模式识别

提出了一种基于小波神经网络的组合故障模式识别方法.针对以歼击机为代表的非线 性系统中存在的多重并发故障,构造了一个多层的小波神经网络,在输入层对残差信号进行二进 离散小波变换,提取其在多尺度下的细节系数作为故障特征向量,并将其输入到神经网络分类器 进行相应的模式分类.仿真结果表明,本文方法为多重并发故障的诊断提供了有效的方法和途径.     

航空发动机排气温度测量通道故障智能检测方法研究

EGT（Exhaust Gas Temperature,排气温度）测量通道故障是一种典型的航空发动机测量系统故障,如果试验过程中不能及时发现,就存在不能及时排除EGT超温、发动机喘振、发动机异常停车等故障的可能,造成重大安全隐患。通过对EGT测量通道故障现象和数据的分析,提出了一种EGT测量通道故障模糊神经网络检测算法。该算法将高压转速偏差、低压转速偏差和排气温度偏差作为模糊逻辑系统的输入参数,通过模糊推理实现故障检测。高压转速、低压转速和排气温度的估计值通过试飞数据训练的神经网络估计模型获得。理论分析和试飞数据验证结果表明,该方法具备实时检测EGT测量通道故障的能力,且具有良好的稳定性,可以满足工程应用要求。     

PSO-optimized modular neural network trained by OWO-HWO algorithm for fault location in analog circuits

Fault diagnosis of analog circuits is a key problem in the theory of circuit networks and has been investigated by many researchers in recent decades. In this paper, an active filter circuit is used as the circuit under test (CUT) and is simulated in both fault-free and faulty conditions. A modular neural network model is proposed in this paper for soft fault diagnosis of the CUT. To optimize the structure of neural network modules in the proposed scheme, particle swarm optimization (PSO) algorithm is used to determine the number of hidden layer nodes of neural network modules. In addition, the output weight optimization–hidden weight optimization (OWO-HWO) training algorithm is employed, instead of conventional output weight optimization–backpropagation (OWO-BP) algorithm, to improve convergence speed in training of the neural network modules in proposed modular model. The performance of the proposed method is compared to that of monolithic multilayer perceptrons (MLPs) trained by OWO-BP and OWO-HWO algorithms, K-nearest neighbor (KNN) classifier and a related system with the same CUT. Experimental results show that the PSO-optimized modular neural network model which is trained by the OWO-HWO algorithm offers higher correct fault location rate in analog circuit fault diagnosis application as compared to the classic and monolithic investigated neural models.     

PWM型VLSI神经网络在故障诊断中的应用

An improved pulse width modulation (PWM) neural network VLSI circuit for fault diagnosis is presented, which differs from the software-based fault diagnosis approach and exploits the merits of neural network VLSI circuit. A simple synapse multiplier is introduced, which has high precision, large linear range and less switching noise effects. A voltage-mode sigmoid circuit with adjustable gain is introduced for realization of different neuron activation functions. A voltage-pulse conversion circuit required for PWM is also introduced, which has high conversion precision and linearity. These 3 circuits are used to design a PWM VLSI neural network circuit to solve noise fault diagnosis for a main bearing. It can classify the fault samples directly. After signal processing, feature extraction and neural network computation for the analog noise signals including fault information, each output capacitor voltage value of VLSI circuit can be obtained, which represents Euclid distance between the corresponding fault signal template and the diagnosing signal, The real-time online recognition of noise fault signal can also be realized.     

Fault Detection and Diagnosis Based on Modeling and Estimation Methods

This paper investigates the problem of fault detection and diagnosis in a class of nonlinear systems with modeling uncertainties. A nonlinear observer is first designed for monitoring fault. Radial basis function (RBF) neural network is used in this observer to approximate the unknown nonlinear dynamics. When a fault occurs, another RBF is triggered to capture the nonlinear characteristics of the fault function. The fault model obtained by the second neural network (NN) can be used for identifying the failure mode by comparing it with any known failure modes. Finally, a simulation example is presented to illustrate the effectiveness of the proposed scheme.     

An engine fault diagnosis system using intake manifold pressure signal and Wigner–Ville distribution technique

This paper proposed an engine fault diagnosis system based on intake manifold pressure signal and artificial neural network with the Wigner–Ville distribution technique. Traditionally, the engine diagnostic method depends on the experience of the technician, but some faults might be inaccurately judged by the technician’s experience when the engine is operating. In the present study, an engine platform diagnosis system using intake manifold pressure was developed. The algorithm of the proposed system consisted of Wigner–Ville distribution (WVD) for feature extraction and the neural network technique for fault classification. In previous work, the Wigner–Ville distribution was often used to analyze the non-stationary signal, because it provides a simple and clear energy spectrum diagram both in the time and frequency domains. This instantaneous energy diagram presented the magnitude of each engine fault under various operating conditions. The Wigner–Ville distribution extracts these features as database input to a neural network and the neural network is used to develop the training and testing modules. To prove the efficiency of the neural network, both the radial basis function neural network and generalized regression neural network are used and compared. The experimental results demonstrated the proposed system is effective and the performance is satisfactory.     

基于Boosting RBF神经网络的滚动轴承故障诊断

提出了一种新颖的基于RBF神经网络滚动轴承故障诊断方法。以滚动轴承动态信号的能量信息作为特征，RBF神经网络作为分类器进行滚动轴承故障自动分类与诊断。为了进一步提高神经网络的泛化能力和故障诊断的准确性，采用Boosting方法，进行网络集成。对七类滚动轴承进行了实验，结果表明该方法具有很好的故障诊断效果。     

基于新小波神经网络的电力电子电路故障诊断

电力电子电路模型具有非线性,所以对它进行故障诊断比较困难.针对这种情况,将小波变换的时频局部化特性和神经网络的非线性映射与学习推理的优点结合起来,建立一种新的小波神经网络.并以双桥12相脉波整流电路为例,首先列出电路的故障类型,片用软件模拟电路的各种故障下的故障信号,然后使用建立的小波神经网络学习和存储双桥12相脉波整流电路的故障信号和故障类型之间的映射关系,实现电路的故障诊断.仿真结果证明了这种故障诊断方法的正确性和准确性.     

基于小波-神经网络的电机振动故障诊断

针对电机振动信号的频谱特点,提出基于小波-神经网络技术的电机故障模式识别与诊断的新方法。利用小波包的多维多分辨率特性,对电机振动信号进行分解与重构,获得振动信号的突变信息,提取与电机故障相关的特征信息,将其作为特征向量输入ART2神经网络,对其进行训练。经过训练后的神经网络可对电机工作状态进行在线监测和实时故障诊断,并在转子实验台上进行了模拟故障仿真试验。通过对仿真结果的分析,证实这种诊断方法的可行性。     

An intelligent fault diagnosis system for newly assembled transmission

Newly assembled automobile transmission has its particular failure characteristic, strict quality testing working procedure on the assembly line is important for quality of automobile transmission. In this paper, we introduce a new automatic fault detection method for automobile transmission. A fault diagnosis expert system for newly assembled transmission is presented, related method of knowledge representation, feature extraction and fault classification is given. Order spectrum analysis method is used to analyze vibratory signal of automobile transmission. After initial feature vectors set are obtained, improved genetic search strategy is used to select fault features, so as to reduce the dimension of feature vector set. Selected feature vector sets are inputted into the BP neural network for fault identification and classification of the newly assembled automobile transmission. A large number of data are collected from industrial site and analyzed, proposed algorithm is verified to be effective and exact.