Robust fault diagnosis for linear time-delay systems with uncertainty

This paper deals with the problem of fault diagnosis problem for a class of linear systems with delayed state and uncertainty. The systems are transformed into two different subsystems. One is not affected by actuator faults so that a robust observer can be designed under certain conditions. The other whose states can be measured is affected by the faults. The proposed observer is utilized in an analytical-redundancy-based approach for actuator and sensor fault detection and diagnosis in time-delay systems. Finally, the applicability and effectiveness of the proposed method is illustrated through numerical examples.

Power Transformer Fault Diagnosis Using Fuzzy Reasoning Spiking Neural P Systems

This paper presents an intelligent technique to fault diagnosis of power transformers dissolved and free gas analysis (DGA). Fuzzy Reasoning Spiking neural P systems (FRSN P systems) as a membrane computing with distributed parallel computing model is powerful and suitable graphical approach model in fuzzy diagnosis knowledge. In a sense this feature is required for establishing the power transformers faults identifications and capturing knowledge implicitly during the learning stage, using linguistic variables, membership functions with “low”, “medium”, and “high” descriptions for each gas signature, and inference rule base. Membership functions are used to translate judgments into numerical expression by fuzzy numbers. The performance method is analyzed in terms for four gas ratio (IEC 60599) signature as input data of FRSN P systems. Test case results evaluate that the proposals method for power transformer fault diagnosis can significantly improve the diagnosis accuracy power transformer.

Fault Detection under Fuzzy Model Uncertainty

The paper tackles the problem of robust fault detection using Takagi-Sugeno fuzzy models. A model-based strategy is employed to generate residuals in order to make a decision about the state of the process. Unfortunately, such a method is corrupted by model uncertainty due to the fact that in real applications there exists a model-reality mismatch. In order to ensure reliable fault detection the adaptive threshold technique is used to deal with the mentioned problem. The paper focuses also on fuzzy model design procedure. The bounded-error approach is applied to generating the rules for the model using available measurements. The proposed approach is applied to fault detection in the DC laboratory engine.

Fault Diagnosis and Reliability Analysis Using Fuzzy Logic Method

A new fuzzy logic fault diagnosis method is proposed. In this method, fuzzy equations are employed to estimate the component state of a system based on the measured system performance and the relationship between component state and system performance which is called as “performance-parameter” knowledge base and constructed by expert. Compared with the traditional fault diagnosis method, this fuzzy logic method can use human＇s intuitive knowledge and dose not need a precise mapping between system performance and component state. Simulation proves its effectiveness in fault diagnosis. Then, the reliability analysis is performed based on the fuzzy logic method.

Fault Diagnosis for Non-linear System Based On Adaptive Fuzzy System

Although lots of valuable results for fault diagnosis based on model have been achieved in linear system, it is difficult to apply these results to non-linear system due to the difficulty of modeling the non-linear system by analysis. Adaptive Fuzzy system provides a way for solving this problem because it can approximate any non-linear system at any accuracy. The key for adaptive Fuzzy system to solve problem is its learning ability, so the authors present a learning algorithm for Adaptive fuzzy system, which can build the system's model by learning from the measurement data as well as experience knowledge with high accuracy. Furthermore, the experiment using the learning algorithm to model a servo-mechanism and to construct the fault diagnosis system based on the model is carried out, the results is very good.

Research on the Algorithm of Avionic Device Fault Diagnosis Based on Fuzzy Expert System

Based on the fuzzy expert system fault diagnosis theory, the knowledge base architecture and inference engine algorithm are put forward for avionic device fault diagnosis. The knowledge base is constructed by fault query network, of which the basic ele- ment is the test-diagnosis fault unit. Every underlying fault cause＇s membership degree is calculated using fuzzy product inference algorithm, and the fault answer best selection algorithm is developed, to which the deep knowledge is applied. Using some examples the proposed algorithm is analyzed for its capability of synthesis diagnosis and its improvement compared to greater membership degree first principle.

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.

FAULT DIAGNOSIS EXPERT SYSTEM FOR ROTATING MACHINERY BASED ON A FUZZY PROBABILITY LOGIC INFERENCE MODEL

A new theory- the fuzzy probability logic theory is presented , This theory incorpo- rates the genterally-used fuzzy logic and the traditionally-used probability logic theory in attempt to emulate the rational fault diagnosis under uncertainty. According to the theory , an inference model , named as FSL , is thus designed to be devoted to the building of a fault diagnosis expert system for rotating machinery (ROSLES) . The system is put into operation on a vibration simula- tor stand for 300 MW turbine generator set ( 1 : 1 0) and satisfactory results are gained.

A TSK-Type Recurrent Neuro-Fuzzy Systems for Fault Prognosis

As a result from the demanding of process safety, reliability and environmental constraints, a called of fault detection and diagnosis system become more and more important. In this article some basic aspects of TSK (Takigi Sugeno Kang) neuro-fuzzy techniques for the prognosis and diagnosis of manufacturing systems are presented. In particular, a neuro-fuzzy model that can be used for the identification and the simulation of faults prognosis models is described. The presented model is motivated by a cooperative neuro-fuzzy approach based on a vectorized recurrent neural network architecture. The neuro-fuzzy architecture maps the residuals into two classes: a one of fixed direction residuals and another one of faults belonging to rotary kiln.

A Fuzzy Mathematics Based Fault Auto-diagnosis System for Vacuum Resin Shot Dosing Equipment

On the basis of the analysis of faults and their causes of vacuum resin shot dosing equipment, the fuzzy model of fault diagnosis for the equipment is constructed, and the fuzzy relationship matrix, the symptom fuzzy vector, the fuzzy compound arithmetic operator, and the diagnosis principle of the model are determined. Then the fault auto-diagnosis system for the equipment is designed , and the functions for real-time monitoring its operation condition and for fault auto diagosis are realized. Finally, the experiments of fault auto-diagnosis are conducted in practical production and the veracity of the system is verified.

Parity Relation Based Fault Estimation for Nonlinear Systems: An LMI Approach

This paper proposes a parity relation based fault estimation for a class of nonlinear systems which can be modelled by Takagi-Sugeno （TS） fuzzy models. The design of a parity relation based residual generator is formulated in terms of a family of linear matrix inequalities （LMIs）. A numerical example is provided to illustrate the effectiveness of the proposed design techniques.

Fault-tolerant Control of Nonlinear System Using Credit Assign Fuzzy CMAC

The adaptive fault-tolerant control scheme of dynamic nonlinear system based on the credit assigned fuzzy CMAC neural network is presented. The proposed learning approach uses the learned times of addressed hypercubes as the credibility, the amounts of correcting errors are proportional to the inversion of the learned times of addressed hypercubes. With this idea, the learning speed can indeed be improved. Based on the improved CMAC learning approach and using the sliding control technique, the effective control law reconfiguration strategy is presented. The system stability and performance are analyzed under failure scenarios. The numerical simulation demonstrates the effectiveness of the improved CMAC algorithm and the proposed fault-tolerant controller.

基于有源噪声控制系统的电声器件故障诊断技术研究

有源噪声控制系统是一种先进的噪声管理技术,其通过产生与入侵噪声相位相反的声波中和噪声,从而达到降噪效果。深入探讨基于有源噪声控制系统的电声器件故障诊断技术,详细介绍电声器件故障诊断技术的应用,包括整体设计方案、基于阻抗特性和信号均方根误差法的故障诊断系统设计,以及基于STM32的次级声源和误差传声器故障诊断系统的实现,确保系统的高效运行和长期稳定性。

不确定性智能车辆转向系统的容错控制方法研究

对存在执行器死区、故障现象和模型不确定性的智能车辆转向系统的跟踪控制问题进行研究。首先,采用自适应模糊逻辑系统对转向系统中难以建模的非线性项进行在线逼近,并结合动态增益技术为智能车辆转向系统设计了自适应滑模控制器,以补偿系统控制增益未知、执行器死区和故障现象对控制性能的影响。此外,在该滑模控制器的设计中,采用一阶滤波技术消除了滑模控制器对转向系统造成的抖振。最后,结合李雅普诺夫稳定性理论分析可知,所设计的控制器可以实现智能车辆转向系统的渐近稳定,并通过数值仿真和整车实验验证了所设计的控制方法的合理性。

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 .

融合 OCEEMDAN的多模态互量纲一化与宽度学习改进的智能故障诊断

滚动轴承作为旋转机械的重要组成部分,在恶劣环境运行导致振动信号具有非线性和非平稳的特点,使得区分故障信号和正常信号变得困难。针对此,提出一种结合多模态互量纲一化(MMDI)与宽度学习系统(BLS)的智能故障诊断方法。通过优化完全自适应噪声集合经验模态(OCEEMDAN)与小波阈值对轴承观测信号进行分解处理,对有效的本征模态函数(IMF)重构并提取MDI,构建了一批MMDI;采用反向传播算法(BP)与堆叠模块方式优化BLS,改进的BLS算法能够快速识别不同的故障类型;最后通过凯斯西储大学轴承数据中心与某实验室提供的轴承数据集对所提方法进行验证,平均准确率分别为99.8%与100%,验证了方法的有效性。

汽车EPS系统可靠性Fuzzy评价研究

在对汽车EPS系统故障进行分析的基础上,建立以Fuzzy理论为基础评价EPS系统可靠性的模糊综合评价模型,提出一种评价EPS系统可靠性的方法,为汽车EPS系统故障诊断提供依据。

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。

自构建关联噪声下的随机共振及其在故障诊断上的应用

轴承作为旋转机械的重要组件之一,及时对其进行健康监测与更换可有效避免设备停机,减少经济损失。首先基于自构建关联噪声驱动下的随机共振系统(stochastic resonance system driven by self-constructingly correlated noise, DSCSR),推导了在正弦激励下该系统输出的理论信噪比(signal-to-noise ratio, SNR)。研究发现通过调节此非线性系统的参数可观察到随机共振现象。其次,针对将随机共振现象用于故障诊断时需要准确的先验知识这一局限性,进一步提出了基于功率谱的信噪比评价指标,并以此来确定非线性系统随机共振发生时的最优系统参数,对最优参数系统输出信号进行功率谱分析来判断故障类型。最后,通过轴承故障诊断试验以及实际风机轴承内圈故障实例证明了DSCSR方法的有效性,以及其增强微弱故障特征并抑制其他谐波以及噪声的干扰的能力。

煤矿智能压风系统的应用研究

针对传统压风系统多电机之间无法协同配合,压风机房需要定时巡检等问题,设计了一种智能压风系统。该系统通过在控制侧加入可编程控制柜,并引入模糊控制算法,实现压风系统的按压供风、负载均衡、自动轮换的功能。通过布置故障诊断装置,检测压风机的健康状况,在发生故障时可停止压风机的运行,并及时开启另一台压风机,保障管道压力恒定。通过安装视频分析摄像仪实现对压风机房进入人员的监测,分析是否有违规或越界行为,保证人员和设备的安全。智能压风系统的管控平台展示压风机的各项数据,并通过动画生动的展示压风机房内各设备的运行状态,显示压风机健康状况的分析结果。该系统的应用减少了电能的浪费,提供了准确的诊断结果,提高了煤矿智能化水平。