基于深度学习的航空传感器故障诊断方法

为解决传统神经网络进行传感器故障诊断时存在的过拟合、泛化能力有限等问题,提出一种基于深度置信网络观测器的航空传感器故障诊断方法。利用深度置信网络替代浅层神经网络,在优化网络结构的基础上,给出深度置信网络隐层节点数选取的递推公式,构建深度置信网络状态观测器。离线训练时,利用飞行数据训练深度置信网络观测器。在线诊断时,通过比较观测器输出值与实际输出值判断故障类型,并给出3种故障隔离与信号重构方法。仿真结果表明,与BP神经网络观测器相比,该方法能够快速准确地进行故障诊断与隔离,并且完成信号重构。     

基于RBF神经网络观测器飞控系统故障诊断

为了解决非线性系统采用解析方法进行故障诊断困难的问题,利用神经网络可逼近任意连续有界非线性函数的能力,提出了一种基于RBF神经网络观测器的故障检测与诊断方法,并详细论述了该故障诊断方法的构造原理。以含有非线性项的飞行控制系统的作动器模型为例,仅作动器的输入输出可测量,通过构造RBF神经网络观测器来拟合作动器系统模型,逼近其在正常情况下的输出。最后在飞控系统的闭环控制环境下,对作动器的三种典型故障进行了计算机仿真诊断,结果表明故障诊断方法是有效的。     

基于事件触发的故障诊断与动态调节集成设计

本文发展了一种事件触发采样与更新检测机制、故障诊断及调节的集成设计框架.事件触发技术不仅用于传感器端,同时也用于容错控制器端.所设计的故障诊断观测器能够应用基于事件触发的非均匀采样输出同时估计故障和系统状态,基于所获得的状态和估计信息,构造事件触发更新检测器和动态容错控制器,进而借助于增广系统方法来集成设计两个事件触发检测器、故障诊断观测器和容错控制器,以保证故障系统的性能,同时尽可能的降低传感器、控制器、执行器三者之间的信息传输频率.仿真实例验证了所提方法的可行性和优越性.     

基于广义回归神经网络的传感器故障诊断研究

针对诊断传感器偏置故障与漂移故障的难点问题,提出了一种基于广义回归神经网络(GRNN)的传感器故障诊断方法。该方法充分利用控制系统闭环回路测控信息,建立一组多输入单输出GRNN观测器,通过将观测器输出与传感器实际输出相比较获取残差序列,获得基于残差序列的传感器偏置故障和漂移故障的辨识策略,实现控制系统传感器故障在线诊断。仿真结果表明:该方法可以快速准确地检测和分离传感器故障,辨识传感器故障类型、故障大小以及故障发生的时间。     

基于观测器的故障诊断技术的鲁棒性

基于观测器的动态系统故障诊断技术的可行性和应用性,关键是对系统不确定性因素具有良好的鲁棒性.根据基于观测器的故障诊断系统鲁棒性的特点,强调通过增加观测器自身的鲁棒性,提高线性不确定系统的故障诊断鲁棒性,并对利用未知输入观测器方法增强鲁棒性进行讨论.提出估计复合故障的一种新的复合故障估计器的设计方法,给出其存在条件以及理论证明,并提出相应的算法.     

基于观测器的机械臂执行器故障诊断方法研究

为了提高机器人系统的可靠性，针对机器人的机械臂控制系统，提出一种基于观测器的执行器故障诊断方法。在考虑系统存在外部干扰的情况下，通过设计干扰观测器抵消外部干扰对故障诊断结果的影响。为了进一步诊断系统执行器故障，提出一种基于二阶滑模观测器的故障诊断方法，通过等效输出注入项对系统执行器故障信号进行故障估计，以完成机械臂执行器的故障诊断任务。最后，建立了双关节机械臂控制系统仿真模型并通过数值仿真分析说明了该故障诊断方法的有效性和应用前景。因此，本研究有助于提高工业机器人系统的可靠性和安全性。     

基于滑模变结构观测器的故障诊断研究综述

该文介绍了现实环境中故障及诊断方法的基本分类,故障诊断的工作原理。其中基于观测器的故障诊断算法被公认为较为成熟的故障方法之一,该文对基于滑模变结构观测器的故障诊断算法的研究现状进行了详细的介绍。     

基于自适应观测器的时滞系统执行器故障诊断

该文研究了一类含有未知输人干扰和模型不确定性的线性时滞系统的故障诊断问题。通过设计自适应诊断观测器，得到了一种新型的鲁棒执行器故障诊断方法。首先针对确定性系统分别设计了检测观测器和自适应诊断观测器，前者能够检测出故障的发生，后者能够理想地估计出故障随时间变化的形状。然后考虑系统的外部干扰和模型不确定，改进了自适应诊断观测器的算法，证明了故障诊断系统的稳定性，提高了故障诊断系统的鲁棒性。最后给出了故障检测过程中阈值的选取原则。仿真结果表明算法具有良好的诊断性能。     

基于PI 观测器的奇异摄动系统故障诊断和最优容错控制

针对线性奇异摄动系统, 提出一种基于PI (proportional integral) 观测器的故障诊断和最优容错控制方法. 基于奇异摄动系统相关理论和矩阵变换技术, 给出PI 全维观测器存在的条件, 该观测器可以观测系统的快慢状态和故障系统的状态. 在估测到系统状态的基础上进一步考虑最优性, 应用最优控制理论, 设计状态反馈控制器, 提出基于PI 观测器的故障诊断器和最优容错控制器的设计方法. 最后的数值算例验证了所提出方法的可行性和正确性.

     

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LS-SVM和SVM的气动执行器故障诊断方法

为了解决自确认气动执行器的故障诊断问题,提出了一种基于最小二乘支持向量机(LS-SVM)回归建模和支持向量多分类机(SVM)的执行器故障诊断方法,该方法利用LS-SVM回归建立气动执行器的正常模型,将实际输出与模型输出比较,产生残差作为气动执行器的非线性故障特征向量。利用聚类方法设计了层次支持向量多分类机结构,以残差作为输入建立支持向量多分类机,判断气动执行器故障类型。利用DABLib生成的故障数据对所研究方法进行了验证,并与基于PCA-SVM的故障诊断方法进行了比较,结果表明该方法有效的解决了气动执行器故障诊断的小样本和非线性问题。     

Model-based fault diagnosis using fuzzy matching

A new fuzzy-model-based approach to fault detection and diagnosis is proposed. The scheme uses a set of fuzzy reference models which describe faulty and fault-free operation, and a classifier based on fuzzy matching for fault diagnosis. The reference models are obtained off-line from simulation data. A fuzzy model which describes the actual behavior of the plant is identified online from normal operating data and compared to each of the reference models. A degree of similarity is evaluated every time the online fuzzy model is identified. Dempster's rule of combination is used to combine new evidence with that already collected. The method of diagnosis accounts for any ambiguity (which may result from fault-free and faulty operation, or different faults, having similar symptoms at a given operating point) by comparing the fuzzy reference models with each other. Results are presented which demonstrate the effectiveness of the scheme when it is used to detect and identify faults in the cooling coil subsystem of the air-handling unit of both simulated and experimental air-conditioning plant     

小样本下基于元学习和网络结构搜索的新模态故障诊断

实际多模态化工过程通常由于产品需求等调整而产生新模态,现有基于深度学习的故障诊断方法存在未充分利用现有模态设计经验、小样本下难以训练模型等局限.针对上述问题,提出一种基于元学习(meta learning, ML)和网络结构搜索(neural architecture search, NAS)的新模态故障诊断方法MetaNAS.首先,利用NAS自动获取现有模态性能最优的网络模型;然后,利用ML从现有模态的NAS过程中学习故障诊断模型的设计经验;最后,当新模态产生时,在已学习设计经验基础上进行梯度更新,即在小样本条件下快速得到新模态故障诊断模型.通过数值系统和田纳西伊斯曼(Tennessee Eastman, TE)化工过程的仿真实验充分验证所提出方法的有效性和可行性.     

Fault detection and diagnosis method for batch process based on ELM-based fault feature phase identification

Because of the multiplicity of operation phases in batch process, which have specific control objects, different dominant process variables and distinct process correlation characteristics, the faults may also have phase characteristic. To conduct fault diagnosis for batch process more precisely, this paper proposes a fault detection and diagnosis method based on fault feature phase identification results. Firstly, extreme learning machine is used to identify fault feature phases between the faulty data set and the normal data set. Then, focusing on the different data nature implied in different fault feature phases, several ‘short stages’ are partitioned for the whole batch. After that, different multiway fisher discriminant analysis (MFDA) models are developed for these ‘short stages,’ respectively. The proposed method can deepen the search space analyzed by fault diagnosis into specific fault feature phases, which not only overcome the disadvantage of too many models in MFDA, but also overcome the disadvantage of low diagnosis accuracy and high false recognition rate of traditional MFDA method. Simulation results show the feasibility and validity of the proposed method.     

基于异步IMM融合滤波的网络化系统故障诊断

针对一类带随机丢包的异步多传感器网络化系统,提出了基于网络化异步交互式多模型（Interacting multiple model,IMM）融合滤波的故障诊断方法.考虑不同传感器通道具有不同丢包概率的情况,将未知的故障幅值看作扩维的系统状态,利用提出的网络化异步IMM融合滤波算法对由系统正常模型和各种可能的故障模型构成的模型集进行滤波,根据模型概率进行故障检测和定位,同时得到故障幅值和系统状态的联合估计.提出的方法避免了传统IMM故障诊断方法模型集设计中故障大小难以确定的问题,适用于具有任意采样速率和任意初始采样时刻的异步多传感器网络化系统,并且通过融合多个传感器的信息提高了故障诊断的准确性.仿真实例验证了所提出方法的可行性和有效性.     

Automatic modeling and fault diagnosis of car production lines based on first-principle qualitative mechanics and semantic web technology

Fault diagnosis is critical for intelligent manufacturing by monitoring the status of a production line and preventing financial loss. Model-based fault diagnosis has the advantage of being able to explain the cause and propagation of faults over model-free diagnosis, but would need knowledge about the configuration model and context-specific information of the production line. Ontology modelling can provide context-specific information on top of a configuration model to benefit fault diagnosis. Typically ontologies are manually constructed and then used by a reasoner based on a set of predefined rules. From the perspective of fault diagnosis, this approach works as an expert system where both the ontology models and predefined rules are specific to a given system. Once the system has changed which happens from time to time as repairs and updates in a production line, or in the case of a different system, the ontology models and predefined rules would need to be manually modified or reconstructed. Here a model-based method is proposed to automate generation of configuration models with context-specific information using semantic web technology when a production line is healthy, and to use the generated configuration model and information for diagnosis when the production line has a fault. The method does not rely on predefined rules and reasoners, but rather uses dynamics models that are based on first-principle qualitative mechanics. It uses numerical optimization to minimize the discrepancy between sensor data from the production line and from simulation running the dynamics model to achieve automatic configuration modelling and fault diagnosis. With three use cases commonly found for a production line, i.e. automatic sensor placement modeling or misplacement diagnosis, motor fault diagnosis with single sensor modality, and motor fault diagnosis with sensory substitution, the feasibility of the proposed method is demonstrated. The method’s faster computational speed and comparable accuracy to a quantitative model-based approach suggests it may complement and accelerate the latter with early-stage selection of candidate models for both modelling and fault diagnosis.     

故障诊断算法在MapReduce中的优化实现

针对故障诊断算法特点,给出了MapReduce框架实现故障诊断算法的基本方法。通过对故障诊断算法和MapReduce运算框架的分析,得出诊断算法MapReduce化的基本方法和思路。在算法研究的基础上,针对非迭代诊断算法,采用传统MapReduce框架实现。针对迭代诊断算法,采用添加了传送模块的迭代式MapReduce框架实现。分别以最近邻法和模糊C均值聚类算法为例,给出非迭代和迭代诊断算法MapReduce化的具体实现。实验结果表明,本文所提基本方法可以运用在故障诊断算法中,为诊断算法MapReduce化提供依据,将MapReduce运用到故障诊断算法中可以有效提升故障诊断效率。     

模糊免疫算法及其在模拟电路故障诊断中的应用

对模糊免疫算法应用于模拟电路故障诊断进行了研究;首先简要介绍了免疫系统的工作机理及一些基本概念,然后在此基础上构建出一种模糊免疫算法,并将免疫算法和模糊聚类法结合起来进行故障诊断;人工免疫算法起到学习样本的作用,以寻找到各样本组的聚类中心;而模糊聚类算法则准确地完成对样本的分类任务;仿真实例表明:立足于模拟电路故障诊断字典法,该算法对模拟电路故障诊断非常有效。     

The concept of residuals for fault localization in discrete event systems

In this paper an approach for fault localization in closed-loop Discrete Event Systems is proposed. The presented diagnosis method allows fault localization using a fault-free system model to describe the expected system behavior. Via a systematic comparison of the observed and the expected behavior, a fault can be detected and a set of fault candidates is determined. Inspired by residuals known from diagnosis in continuous systems, different set operations are introduced to generate the fault candidate set. After fault detection and a first fault localization, a procedure is given to render the fault localization more precisely by an analysis of the further observed system behavior. Special emphasis is given to the use of identified models for the fault-free system behavior. The approach is explained using a laboratory manufacturing facility.