基于RBF神经网络的非线性系统故障诊断

针对一类模型未知的非线性动态系统,提出了一种基于神经网络在线估计结构的鲁棒故障诊断检测方法.系统中,仅输入输出可测,且包含输出不确定性项.该方法通过构造神经网络在线逼近结构来拟合该非线性系统模型和系统的非线性故障特性,建立系统的状态观测器.神经网络的权重调整规律由李亚普诺夫稳定性方法获得,系统的输出不确定性部分被用于获得诊断算法的阈值.最后,用Matlab/SIMULINK对的算法予以验证,结果表明本算法的合理性.     

Locally Linear Back-propagation Based Contribution for Nonlinear Process Fault Diagnosis

This paper proposes a novel locally linear back-propagation based contribution (LLBBC) for nonlinear process fault diagnosis. As a method based on the deep learning model of auto-encoder (AE), LLBBC can deal with the fault diagnosis problem through extracting nonlinear features. When the on-line fault diagnosis task is in progress, a locally linear model is firstly built at the current fault sample. According to the basic idea of reconstruction based contribution (RBC), the propagation of fault information is described by using back-propagation (BP) algorithm. Then, a contribution index is established to measure the correlation between the variable and the fault, and the final diagnosis result is obtained by searching variables with large contributions. The smearing effect, which is an important factor affecting the performance of fault diagnosis, can be suppressed as well, and the theoretical analysis reveals that the correct diagnosis can be guaranteed by LLBBC. Finally, the feasibility and effectiveness of the proposed method are verified through a nonlinear numerical example and the Tennessee Eastman benchmark process.      

Fault diagnosis of a mixing process using deep qualitative knowledge representation of physical behaviour

This paper describes an on-line fault diagnosis system which diagnoses faults in a pilot scale mixing process using on-line measurements. Fault detection and fault diagnosis is performed based on a qualitative model of the mixing process. The qualitative model provides a set of constraints for the system being diagnosed. Once it is violated, a particular fault is detected. Since most of the information used by the diagnosis system comes from on-line measurements, it is important to determine whether sensors are working normally or not before considering failures of other components. Sensor failure is mainly diagnosed from heuristic considerations, while the failures of other components are diagnosed from a procedure of hypothesis generation, qualitative simulation, and comparison. Based on a hypothesis, the behaviour of the system being diagnosed is simulated from its qualitative model and is compared with the actual measurements. Depending upon whether they conflict or not, the hypothesis is denied or retained. A new approach for reducing the ambiguity in qualitative simulation is described. Ambiguity is reduced by taking account of the information on the order of magnitude relations between different physical variables.     

基于信息融合方法的电力变压器网络在线监测与故障诊断研究

针对电力变压器在线监测与故障诊断问题,提出了基于多传感器信息融合与嵌入式网络相结合的解决方案。介绍了变压器油中气体分析的原理及诊断方法,重点论述了基于Webit的嵌入式网络在线监测方法,基于BP网络的混合气体识别方法及多传感器一致性信息融合方法等通过试验,验证了方法的有效性。     

基于BondGraph模型的定性故障诊断方法

研究一种基于键合图（Bond Graph）模型的定性故障诊断方法。根据Bond Graph模型元件中有关参数和变量的特定因果关系,推导出当某观测参量发生变化时,系统内所有可能产生故障的部位,并在此基础上预测每个故障的将来状态,通过与系统实际观测特征比较,在可能产生故障的集合中准确定位故障源。通过实例仿真验证,该方法是便捷有效的。     

基于双通道传感器的航空发动机在线故障诊断系统研究

对基于双通道传感器的航空发动机在线故障诊断和隔离技术进行了研究;在发动机机载非线性模型的基础上,对发动机的双通道传感器分别设计混合卡尔曼滤波器,利用该滤波器在线估计双通道传感器输出,并结合实际双通道传感器测量值以及发动机机载非线性模型的输出值在线实现传感器故障检测和隔离、部件故障及异常检测确认;利用该技术建立了某型涡扇发动机在线故障诊断系统,通过仿真实例验证了该系统的诊断性能,实验结果表明,本文所建立的在线故障诊断系统能够较好的完成故障诊断与隔离、部件故障及异常检测等功能,为此类系统的工程应用提供了理论依据。     

基于无源振动传感器标签的轴承故障在线诊断技术

提出了一种基于无源振动传感器标签的穿梭车轴承故障在线诊断技术.设计了一种无源射频识别(RFID)振动传感器标签结构,因其工作在无源模式下,减少了在线故障诊断的成本,同时可以实现对轴承故障的长期在线诊断.介绍了振动信号的处理方式,提出了基于奇异熵的奇异值分解信号降噪算法,依据信号的奇异熵自行定阶降噪,避免了人为预设参数所导致的误差,并提出了基于最小二乘支持向量回归(LS-SVR)的故障诊断算法.测试结果表明:设计的标签能够可靠地完成信号采集和传输,采用的算法能够快速而准确地定位故障,较传统故障诊断方法提高了实时性并降低了成本.     

A sparse fault degradation oriented fisher discriminant analysis (FDFDA) algorithm for faulty variable isolation and its industrial application

In a fault process, the variables may be influenced differently. In order to improve the diagnosis performance, it is an important issue to isolate those significant faulty variables that cover informative fault effects. However, those variables are selected one by one and their correlations are not considered in the previous work. As sparse-relevant methods can automatically and efficiently isolate significant correlated variables, it is natural to consider applying the criteria of sparsity to separate the significantly influenced faulty variables and analyze them by specific methods. First, the sparse version of the fault degradation oriented Fisher discriminant analysis (FDFDA) algorithm is proposed to produce informative discriminant directions with sparse loadings. Subsequently, a faulty variable selection strategy is proposed based on the sparse FDFDA algorithm to select significantly influenced faulty variables. By iteratively isolating correlated variables along each sparse fault direction, all the faulty variables can be automatically selected until the left fault data and normal data share the similar characteristics. Therefore, the whole measurement variables can be divided into faulty variable set and normal variable set. Then different fault diagnosis models can be developed according to their different characteristics for each fault class. For online application, a probabilistic fault diagnosis strategy is proposed to determine the fault cause of the new sample by the largest synthetic probability that integrates the diagnosis results of two variable sets. The performance of the proposed fault diagnosis method is illustrated using the data from the cut-made process of cigarette.     

Automated fault diagnosis in nonlinear multivariable systems usinga learning methodology

The paper presents a robust fault diagnosis scheme for detecting and approximating state and output faults occurring in a class of nonlinear multiinput-multioutput dynamical systems. Changes in the system dynamics due to a fault are modeled as nonlinear functions of the control input and measured output variables. Both state and output faults can be modeled as slowly developing (incipient) or abrupt, with each component of the state/output fault vector being represented by a separate time profile. The robust fault diagnosis scheme utilizes on-line approximators and adaptive nonlinear filtering techniques to obtain estimates of the fault functions. Robustness with respect to modeling uncertainties, fault sensitivity and stability properties of the learning scheme are rigorously derived and the theoretical results are illustrated by a simulation example of a fourth-order satellite model.     

基于多级模糊神经网络的故障诊断在化工生产过程中的应用

化工生产过程一般都非常复杂，如柠檬酸蒸发。由于控制回路与测控参数很多，生产过程的故障检测与诊断问题非常困难，难以做到实时检查，得到其故障信息。所以本文提出一种基于神经网络的多级故障诊断系统。采用三级递阶模糊神经网络，降解整个系统故障诊断问题的复杂性，同时采用所有子神经网络全局并行的推理方式，具有快速处理能力，适合系统实时在线故障诊断。     

应用决策层信息融合的模拟电路故障诊断实现

研究了基于多类电量测试信息及其多诊断方法融合的模拟电路故障诊断。获取可及节点电压,运用K故障诊断法进行故障在线检测与初步定位,再离线测量电路在不同的测试频率下输出对输入的增益,运用最小标准差法进行诊断。由所提故障隶属函数得到基于各类测试信息的元件故障隶属度,以此计算D-S证据理论中各传感器的信度函数分配,再运用D—S合成法则实现融合诊断。模拟实验结果表明：所提融合诊断方法大大提高了故障定位的准确率。     

基于深度学习的变压器故障诊断方法研究

油中溶解气体分析方法（DGA）是变压器内部故障诊断的重要方法,广泛应用在变压器在线监测和定期试验检测中,传统的特征气体法和三比值法等诊断方法在实际应用中普遍存在着一定的局限性,导致故障诊断精度偏低。针对这一问题,本文提出了一种基于深度学习技术中的多层感知机的变压器故障综合诊断方法,利用开源的Scikit-learn 机器学习框架及TensorFlow深度学习框架构建了变压器故障诊断模型,并应用实际工程中的故障样本数据,对故障诊断模型进行了训练和测试。试验结果表明,基于多层感知机技术的变压器故障诊断模型能够对变压器故障进行正确诊断,与传统的三比值法及支持向量机技术相比,多层感知机的诊断准确率更高,具有更优的故障诊断性能,能够为变压器的检修提供更为准确的参考信息。     

双离合器自动变速器故障诊断及容错控制

针对双离合器自动变速器的系统组成,应用冗余法、搭建诊断电路法、极值法,制定包括传感器、执行机构、控制器和被控对象在内的故障诊断策略,并开发了车辆行驶状态下的在线综合故障诊断程序.将各故障分为不同等级,提出相应的容错控制方法,使车辆在发生故障时能最大限度地保持继续行驶的能力.最后,对部分典型故障下的双离合器自动变速器起步...     

An application of expert systems techniques to the on-line control and fault diagnosis of a mixing process

An application of expert hierarchical control is described in this paper. The control is implemented in a two-level configuration, where the lower layer performs direct regulation control and the upper layer performs supervisory functions. In the regulation layer, a rule-based controller performs the regulation task, where the controller is constructed upon causal relations between subsystems. The control action is inferred from the measurement of both controlled and noncontrolled variables. In the supervisory layer, the main function is a fault diagnosis system which diagnoses faults on-line. The diagnosis is based upon reasoning from the structure of the system and the functions of its components, and efficient diagnosis is achieved by dividing the system into several subsystems. The overall technique has been successfully implemented on a pilot scale mixing process under on-line computer control.     

逆变电路智能故障诊断系统

针对逆变器由于具有非线性的特征而无法采用精确数学模型进行故障诊断的情况,本文提出一种基于小波分析和神经网络的新型逆变电路故障检测与诊断方法。建立三相SPWM逆变电源的非线性MATLAB仿真模型,以三相输出故障电压作为故障信息,利用小波分析的方法提取低频能量值作为特征向量,通过神经网络实现逆变器故障桥臂定位,最后利利用逆变三相电压同一桥臂故障电压的对称性的特点,用一种简单的判断逻辑实现故障元件的分离。设计了基于DSP的PWM逆变电路在线智能故障诊断系统。测试结果表明,该系统具有良好的故障诊断效果,具有一定的实用价值。     

汽车变速箱嵌入式诊断系统设计与实现

嵌入式技术作为一种在线诊断故障的实现手段,已愈来愈为众多专家、学者和工程技术人员所关注。针对汽车变速箱,设计开发一套基于嵌入式技术与故障诊断技术的汽车变速箱故障诊断系统,并对汽车变速箱故障嵌入式诊断系统的硬件、软件进行简单研究,提出基于多支持向量机的汽车变速箱故障识别方法,并对相关技术开展深入研究。所研究的相关技术及系统的实现方案为嵌入式技术在相关动力装置故障诊断中的有效应用提供一条切实可行的途径。     

基于多元时间序列分析的控制系统执行器故障诊断方法研究

为提高控制系统执行器故障实时诊断的准确率,该文提出一种基于多元时间序列分析的控制系统执行器在线故障诊断方法。首先分析了控制系统执行器故障机理,确定了表征执行器故障的关键信号;其次采用执行器历史数据,建立了时间卷积网络(TCN)在线预测模型,对执行器多通道信号进行在线预测;随后通过长短期记忆网络(LSTM)对多通道残差信号建立了故障分类模型;最后以燃气轮机控制系统执行器半物理试验平台中的电液执行器为例进行了多次重复试验验证。结果表明,基于TCN网络的在线预测模型相比传统循环神经网络(RNN)预测误差较小;基于LSTM网络的故障分类模型准确率较高;通过LSTM网络对多通道残差信号进行故障分类,比对原始故障数据分类故障准确率更高。     

水电数字仿真在线融合故障诊断专家系统

为了解决丰满水电数字仿真系统的在线故障诊断问题，基于信息与方法融合的思想，提出了分布式集成神经网络建模方法、模糊神经网络专家系统(FNNES)在线故障诊断方法。将模糊神经网络(FNN)嵌入专家系统(ES)中，FNN负责知识获取和逻辑推理，ES负责系统信息的输入和输出、符号推理，并对FNN的结论进行解释。系统的运行验证了方法的有效性和实际应用价值。为现场诊断系统的开发提供了有益的方法和经验。     

Hybrid fault characteristics decomposition based probabilistic distributed fault diagnosis for large-scale industrial processes

The performance of fault diagnosis is highly dependent on the representation of fault characteristics. However, for large-scale industrial processes with high-dimension variables, treating the whole process as a single subject will degrade the representation accuracy. It may result from the following reasons: First, fault may disturb a part of variables rather than the whole process where the fault information may be buried by the unaffected variables. Second, fault characteristics may be hybrid, in which linear fault patterns and nonlinear fault patterns coexist. Therefore, an effective process decomposition mechanism is of great demand to well describe the complex fault characteristics of large-scale processes. This paper proposes a fault characteristics decomposition based probabilistic and distributed fault diagnosis method. First, process is decomposed into different subsets by evaluating fault effects from linear and nonlinear aspects. Based on the decomposition result, distributed diagnosis models are developed where different fault modeling strategies are implemented for different subsets to closely describe fault characteristics. For online application, probabilistic fault diagnosis is implemented at two levels. At the lower level, distributed diagnosis models are adopted to reveal the underlying characteristics of new sample in each subset; at the upper level, the final affiliation can be revealed by integrating the results from each subset in a probabilistic way. The effectiveness of the proposed algorithm is tested by both the numerical example and industrial processes.     

基于膜聚类算法的风机振动故障诊断

在工业环境下,风机振动故障常常需要人工诊断,诊断效率低,不易完成实时计算和在线分析判断。针对上述问题,提出了一种膜聚类算法可用于风机振动故障的在线智能诊断。该算法将膜计算的方法引入到聚类中,并采用概率模型更新种群的方法实现最佳聚类中心的寻优。算法首先在多个数据集上进行聚类实验,实验结果显示该算法克服了常规聚类算法聚类结果不稳定,聚类质量差的缺点。然后将其应用于风机振动故障在线诊断系统中进行仿真测试,结果显示所采用的方法能满足风机振动故障在线智能诊断要求,也可应用于其他各类设备的振动故障在线智能诊断。