Isolability of faults in sensor fault diagnosis

A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.     

Model-based fault diagnosis in continuous dynamic systems

Traditional fault detection and isolation methods are based on quantitative models which are sometimes difficult and costly to obtain. In this paper, qualitative bond graph (QBG) reasoning is adopted as the modeling scheme to generate a set of qualitative equations. The QBG method provides a unified approach for modeling engineering systems, in particular, mechatronic systems. An input-output qualitative equation derived from QBG formalism performs continuous system monitoring. Fault diagnosis is activated when a discrepancy is observed between measured abnormal behavior and predicted system behavior. Genetic algorithms (GA's) are then used to search for possible faulty components among a system of qualitative equations. In order to demonstrate the performance of the proposed algorithm, we have tested it on a laboratory scale servo-tank liquid process rig. Results of the proposed model-based fault detection and diagnosis algorithm for the process rig are presented and discussed.     

Module level fault diagnosis for analog circuits based on system identification and genetic algorithm

This paper proposes a new module level fault diagnosis method for analog circuits. Firstly, the transfer function is constructed according to the relationship between output and input of the circuit under test (CUT). Every system parameter of the transfer function is expressed by several component parameters. These components are divided into several modules. Then, the way of objective function optimization based on genetic algorithm (GA) is adopted to solve nonlinear equations, which are obtained by multi-frequency testing. Finally, the module level faults are detected by comparing the estimated system parameters to their normal values. The results show that the proposed method is effective to identify system parameters and locate module level faults.     

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。     

Multiple manifolds analysis and its application to fault diagnosis

A novel approach to fault diagnosis is proposed using multiple manifolds analysis (MMA) to extract manifold information from the vibration signals collected from a mechanical system. The basic idea of MMA is to reconstruct a manifold by embedding time series into a high-dimensional phase space. The tangent direction of the neighborhood for each point is then used to approximate its local geometry. The variation of the multiple manifolds representing different states of the mechanical system can be revealed by performing multi-way principal component analysis. The vibration signals acquired from roller bearings are employed to validate the proposed algorithms. Test results show that the proposed MMA-based approach can interpret different machine conditions and is effective to the fault diagnosis, and the MMA-based fault clustering and trend analysis algorithms have outperformed the conventional fault diagnosis methods.     

A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art

Preventing induction motors (IMs) from failure and shutdown is important to maintain functionality of many critical loads in industry and commerce. This paper provides a comprehensive review of fault detection and diagnosis (FDD) methods targeting all the four major types of faults in IMs. Popular FDD methods published up to 2010 are briefly introduced, while the focus of the review is laid on the state-of-the-art FDD techniques after 2010, i.e. in 2011–2015 and some in 2016. Different FDD methods are introduced and classified into four categories depending on their application domains, instead of on fault types like in many other reviews, to better reveal hidden connections and similarities of different FDD methods. Detailed comparisons of the reviewed papers after 2010 are given in tables for fast referring. Finally, a dedicated discussion session is provided, which presents recent developments, trends and remaining difficulties regarding to FDD of IMs, to inspire novel research ideas and new research possibilities.     

Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices

This paper deals with the problem of incipient fault diagnosis for a class of Lipschitz nonlinear systems with sensor biases and explores further results of total measurable fault information residual (ToMFIR). Firstly, state and output transformations are introduced to transform the original system into two subsystems. The first subsystem is subject to system disturbances and free from sensor faults, while the second subsystem contains sensor faults but without any system disturbances. Sensor faults in the second subsystem are then formed as actuator faults by using a pseudo-actuator based approach. Since the effects of system disturbances on the residual are completely decoupled, multiple incipient sensor faults can be detected by constructing ToMFIR, and the fault detectability condition is then derived for discriminating the detectable incipient sensor faults. Further, a sliding-mode observers (SMOs) based fault isolation scheme is designed to guarantee accurate isolation of multiple sensor faults. Finally, simulation results conducted on a CRH2 high-speed railway traction device are given to demonstrate the effectiveness of the proposed approach.     

一种基于动态剪枝二叉树SVMs的高炉故障诊断新方法

高炉故障诊断是一个多类分类问题,且各个故障类别间具有一定的关系,在识别其中某一类故障时,并不需要区分全部故障的类别,为此提出了基于剪枝二叉树的支持向量机改进算法,每次识别时都去除相对没有价值的支持向量,根据类间相似度重新构造二叉树,剪掉没有价值的枝节,减少支持向量机个数,加速识别过程。通过对高炉故障模型的仿真实验,比较不同多类分类算法的性能,证明该方法能够在保证识别准确率的情况下提高故障诊断速度。     

Disturbance observer based fault estimation and dynamic output feedback fault tolerant control for fuzzy systems with local nonlinear models

This paper addresses the problems of fault estimation (FE) and fault tolerant control (FTC) for fuzzy systems with local nonlinear models, external disturbances, sensor and actuator faults, simultaneously. Disturbance observer (DO) and FE observer are designed, simultaneously. Compared with the existing results, the proposed observer is with a wider application range. Using the estimation information, a novel fuzzy dynamic output feedback fault tolerant controller (DOFFTC) is designed. The controller can be used for the fuzzy systems with unmeasurable local nonlinear models, mismatched input disturbances, and measurement output affecting by sensor faults and disturbances. At last, the simulation shows the effectiveness of the proposed methods.     

SWE-IPCA方法在传感器故障诊断中的应用

针对目前油田传感器设备故障诊断广泛采用的主元得分向量平方和与平方预测误差方法,在进行在线故障诊断过程中,往往存在对实际生产过程中产生的弱故障诊断不灵敏,甚至无法有效进行故障识别的问题,同时考虑到油田生产过程的特点,提出一种基于平方加权预测误差的迭代主元分析进行故障检测的方法,在不同的残差空间中对故障进行分析,提高了系统对弱故障诊断的准确性.运用迭代算法更新主元分析算法模型,通过残差空间的平方加权预测误差变量重构确定故障,实现对油田生产传感器设备故障的动态在线诊断.最后利用油田生产的实际数据对该方法进行了在线故障诊断实验,实验结果表明,该方法对油田生产传感器设备可以有效地进行故障诊断.     

Neural networks applied for the identification and fault diagnosis of process valves and actuators

This paper is concerned with the instrumentation and technology of fault detection and isolation (FDI) in process valves and actuators. A classification of faults in process valves and actuators is followed by a brief review of EDI techniques. Artificial neural networks (ANNs) are classified and introduced as an effective way of modelling valves and actuators, which are severely nonlinear components. Experimental results obtained from tests conducted on a double acting, twin piston rack-and-pinion actuator, are presented.     

A data-driven multiplicative fault diagnosis approach for automation processes

This paper presents a new data-driven method for diagnosing multiplicative key performance degradation in automation processes. Different from the well-established additive fault diagnosis approaches, the proposed method aims at identifying those low-level components which increase the variability of process variables and cause performance degradation. Based on process data, features of multiplicative fault are extracted. To identify the root cause, the impact of fault on each process variable is evaluated in the sense of contribution to performance degradation. Then, a numerical example is used to illustrate the functionalities of the method and Monte-Carlo simulation is performed to demonstrate the effectiveness from the statistical viewpoint. Finally, to show the practical applicability, a case study on the Tennessee Eastman process is presented.     

高压输电导线的损伤检测与故障诊断

本文利用红外和电磁传感器分别检测铝绞线和钢芯（钢绞线）的断股故障信号,应用db4小波基对故障信号进行了小波分析,由故障信号的时频域和小波分解细节特征,构造了神经网络诊断模型的输入特征矢量.利用3层BP网络实现了导线断股故障的精确诊断,所研制的红外电磁检测仪器可由巡线机器人携载和操作.     

Sensor fault diagnosis based on energy balance evaluation: application to a metal processing

This paper deals with the design of a residual generator for fault detection and isolation in the dynamic closed-loop systems based on the balance of energy which "enters" and "leaves" plants. The main contribution of this paper consists in developing a suitable fault detection and isolation technique to detect faults in single-input single-output closed-loop system based on major signals without the requirement of an accurate static or dynamic model. Indeed, in the absence of conventional input-output models, the proposed method involves the on-line energy balance evaluation to detect a sensor fault. The application to the monitoring of a galvanizing line in steel industry shows the effectiveness of the suggested approach when a sensor fault occurs.     

设备故障诊断方法

对设备故障诊断方法进行分类,并对目前一些常用的设备故障诊断方法进行综述。     

Fault diagnosis for a class of nonlinear systems via ESO

In this paper, a novel fault detection and identification (FDI) scheme for a class of nonlinear systems is presented. First of all, an augment system is constructed by making the unknown system faults as an extended system state. Then based on the ESO theory, a novel fault diagnosis filter is constructed to diagnose the nonlinear system faults. An extension to a class of nonlinear uncertain systems is then made. An outstanding feature of this scheme is that it can simultaneously detect and identify the shape and magnitude of the system faults in real time without training the network compared with the neural network-based FDI schemes. Finally, simulation examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

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，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。文中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。     

一类非线性系统的执行器偏差故障检测与诊断

针对一类具有系统建模误差以及含传感器模型不确定项的非线性控制系统，本文提出了一种用于执行器故障检测与诊断的观测器方法。观测器中引入了自适应补偿项，当出现偏差后将对故障及建模不确定项进行补偿。自适应算法中采用了死区算子，增强了算法的鲁棒性。利用自适应阈值进行故障检测，同时利用自适应律进行偏差估计。仿真结果表明了该方法的有效性。     

故障树分析法在汽车故障诊断中的应用

主要介绍基于故障树分析法的汽车故障诊断系统。基于故障树分析法建立的汽车故障诊断模型能较为全面、系统、层次性地反映故障现象与故障原因之间的复杂关系。并在此基础上用Visual C＋＋6．0编程语言。开发出具有友好的人机交互界面的汽车故障诊断系统。以中央控制门系统的诊断为例。验证了该方法具有良好的应用前景。