基于定性定量知识的故障诊断

提出一种将检测中的定量信息定性化的方法,并应用QSIM的算法约束的概念。结合区间代数的计算规则,设计了定性与定量结合的诊断系统,缩小诊断空间,增大定性仿真在故障诊断中的应用范围。通过不断扩大先验故障模型,使系统具备一定的学习能力,并以压缩制冷系统为例进行了诊断,验证了系统的正确性。     

基于神经网络模型辨识的串级控制系统传感器容错技术研究

本文根据控制律重构设计的思想，提出了串级控制系统主动容错方案。在工业过程中串级控制系统发生传感器失效故障的情况下，利用人工神经网络在线辨识被控对象模型，在故障诊断系统输出的故障信息驱动下，实现控制律在线重构设计，使工业过程在重构的控制系统控制下稳定工作，保证性能指标。最后，以一个串级控制回路为对象进行了仿真验证，结果表明上述方案达到比较满蔷的容错控制效果。     

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。     

Fault diagnosis of a cstr using fuzzy neural networks

On-line process fault diagnosis using fuzzy neural networks is described in this paper. The fuzzy neural network is obtained by adding a fuzzification layer to a conventional feed forward neural network. The fuzzification layer converts increments in on-line measurements and controller outputs into three fuzzy sets: “increase”, “steady”, and “decrease”. Abnormalities in a process are represented by qualitative increments in on-line measurements and controller outputs. These are classified into various categories by the network. By representing abnormalities in qualitative form, training data can be condensed. The fuzzy approach ensures smooth transitions from one fuzzy sets to another and, hence, robustness to measurement noise is enhanced. The technique has been successfully applied to a CSTR system.     

Neural network based fault diagnosis using unmeasurable inputs

Much of the earlier work presented in the area of on-line fault diagnosis focuses on knowledge based and qualitatively reasoning principles and attempts to present possible root causes and consequences in terms of various measured data. However, there are many unmeasurable operating variables in chemical processes that define the state of the system. Such variables essentially characterise the efficiency and really need to be known in order to diagnose possible malfunction and provide a basis for deciding on appropriate action to be taken by operators. This paper is concerned with developing a soft sensor to assist in on-line fault diagnosis by providing information on the critical variable that is not directly accessible. The features of dynamic trends of the process are extracted using a wavelet transform and a qualitative interpretation, and then are used as inputs in the neural network based fault diagnosis model. The procedure is illustrated by reference to a refinery fluid catalytic cracking reactor.     

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.     

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.     

工程机械的层次模型协同诊断方法

试图以一种集定性、定量模型诊断为一体的分层协同诊断方法运用于运动工程机械的线控电液制动系统,详细介绍了故障传播图的层次化产生方法以及对应的模型诊断观测器,并使用诊断管理单元的FLIP算法和控制管理模块进行在线实时性复杂系统协同诊断,最后用一个简例来表述贯穿本诊断方法全过程.     

HELIX: A helicopter diagnostic system based on qualitative physics

Future helicopter requirements, including expanded missions and single-pilot operation, will greatly increase the demands placed on the pilot. To meet these requirements without overwhelming the pilot, novel approaches to cockpit automation must be devloped. To assess the feasibility of applying Artificial Intelligence technology to helicopter cockpit automation, an expert system for status monitoring and diagnosis designated HELIX (HELicopter Integrated eXpert) has been developed.At the heart of the HELIX program is a Qualitative Reasoning System (QRS). The QRS is a general mechanism to support the creation of hierarchical device models and reasoning about device behaviour using Qualitative Physics. The HELIX qualitative model is represented as a set of constraints that define the normal behaviour of the engines, transmission, flight controls, and rotors of the helicopter. Aircraft health is assessed by determining whether observations (sensor readings and pilot control inputs) are consistent with the constraints of the model. If an inconsistency is detected, a process of systematic constraint suspension is used to test various failure hypotheses.Critical to the efficient operation of the HELIX program is the hierarchical model representation, which enables reasoning at various levels of abstraction. Using a top-down approach, the diagnostic process exploits the hierarchy by beginning fault isolation with the most reduced form of the model. To refine the diagnosis, a branch of the hierarchy may be expanded until a component-level diagnosis is made. The hierarchy also greatly reduces the complexity of multiple failure diagnosis. Rather than considering combinations of failures in all leaf components, the diagnosis can be restricted to combinations of branches in the hierarchy.HELIX has been successfully tested on a variety of simulated failures. By representing only the normal behaviour of the helicopter and testing hypotheses by constraint suspension, HELIX has been able to diagnose single or multiple failures without prior knowledge of failure modes. The approach represents a promising technique for automating the qualitative reasoning required to diagnose novel failures and may form the basis for extensive automation both in airborne and ground-based diagnostic systems.     

Principal components selection for dimensionality reduction using discriminant information applied to fault diagnosis

The Principal Component Analysis is one of most applied dimensionality reduction techniques for process monitoring and fault diagnosis in industrial process. This work proposes a procedure based on the discriminant information contained in the principal components to determine the most significant ones in fault separability. The Tennessee Eastman Process industrial benchmark is used to illustrate the effectiveness of the proposal. The use of statistical hypothesis tests as a separability measure between multiple failures is proposed for the selection of the principal components. The classifier profile concept has been introduced for comparison purposes. Results show an improvement in the classification process when compared with traditional techniques and the StepWise selection. This has resulted in a better classification for a fixed number of components, or a smaller number of required components to obtain a prefixed error rate. In addition, the computational advantage is demonstrated.     

Intelligent state estimation for fault tolerant nonlinear predictive control

There is growing realization that on-line model maintenance is the key to realizing long term benefits of a predictive control scheme. In this work, a novel intelligent nonlinear state estimation strategy is proposed, which keeps diagnosing the root cause(s) of the plant model mismatch by isolating the subset of active faults (abrupt changes in parameters/disturbances, biases in sensors/actuators, actuator/sensor failures) and auto-corrects the model on-line so as to accommodate the isolated faults/failures. To carry out the task of fault diagnosis in multivariate nonlinear time varying systems, we propose a nonlinear version of the generalized likelihood ratio (GLR) based fault diagnosis and identification (FDI) scheme (NL-GLR). An active fault tolerant NMPC (FTNMPC) scheme is developed that makes use of the fault/failure location and magnitude estimates generated by NL-GLR to correct the state estimator and prediction model used in NMPC formulation. This facilitates application of the fault tolerant scheme to nonlinear and time varying processes including batch and semi-batch processes. The advantages of the proposed intelligent state estimation and FTNMPC schemes are demonstrated by conducting simulation studies on a benchmark CSTR system, which exhibits input multiplicity and change in the sign of steady state gain, and a fed batch bioreactor, which exhibits strongly nonlinear dynamics. By simulating a regulatory control problem associated with an unstable nonlinear system given by Chen and Allgower [H. Chen, F. Allgower, A quasi infinite horizon nonlinear model predictive control scheme with guaranteed stability, Automatica 34(10) (1998) 1205–1217], we also demonstrate that the proposed intelligent state estimation strategy can be used to maintain asymptotic closed loop stability in the face of abrupt changes in model parameters. Analysis of the simulation results reveals that the proposed approach provides a comprehensive method for treating both faults (biases/drifts in sensors/actuators/model parameters) and failures (sensor/ actuator failures) under the unified framework of fault tolerant nonlinear predictive control.     

An integrated architecture for fault diagnosis and failure prognosis of complex engineering systems

Complex engineering systems, such as aircraft, industrial processes, and transportation systems, are experiencing a paradigm shift in the way they are operated and maintained. Instead of traditional scheduled or breakdown maintenance practices, they are maintained on the basis of their current state/condition. Condition-Based Maintenance (CBM) is becoming the preferred practice since it improves significantly the reliability, safety and availability of these critical systems. CBM enabling technologies include sensing and monitoring, information processing, fault diagnosis and failure prognosis algorithms that are capable of detecting accurately and in a timely manner incipient failures and predicting the remaining useful life of failing components. If such technologies are to be implemented on-line and in real-time, it is essential that an integrating system architecture be developed that possesses features of modularity, flexibility and interoperability while exhibiting attributes of computational efficiency for both on-line and off-line applications. This paper presents a .NET framework as the integrating software platform linking all constituent modules of the fault diagnosis and failure prognosis architecture. The inherent characteristics of the .NET framework provide the proposed system with a generic architecture for fault diagnosis and failure prognosis for a variety of applications. Functioning as data processing, feature extraction, fault diagnosis and failure prognosis, the corresponding modules in the system are built as .NET components that are developed separately and independently in any of the .NET languages. With the use of Bayesian estimation theory, a generic particle-filtering-based framework is integrated in the system for fault diagnosis and failure prognosis. The system is tested in two different applications—bearing spalling fault diagnosis and failure prognosis and brushless DC motor turn-to-turn winding fault diagnosis. The results suggest that the system is capable of meeting performance requirements specified by both the developer and the user for a variety of engineering systems.     

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。     

BP神经网络在飞控系统传感器故障诊断中的应用

故障检测和诊断技术对提高系统可靠性具有重要意义,针对飞控系统中常见的传感器故障,提出了基于神经网络观测器的故障诊断方法;通过构造神经网络模型代替解析系统建模,利用神经网络的学习能力在线检测传感器故障,最后,应用BP神经网络算法对故障进行仿真;仿真结果表明,神经网络观测器方法对单一传感器故障及多个传感器故障均能够准确识别,并对故障的定位也有不错的效果。     

一种新的计算机辅助故障树生成方法

故障树分析是过程风险分析中的一种有效方法,计算机辅助生成故障树尤其重要.该文中,提出一种基于图论仿真和数据挖掘的新方法.图论模型是一种深层知识,能够表达复杂的因果关系,具有包容大规模潜在信息的能力,尤其适用于安全领域的应用.深入研究故障传播机制及故障树的内在机理,从本质上讲,故障诊断、HAZOP及故障树分析的理论基础都是一样的,即对系统的稳态定性仿真及分析.通过稳态定性仿真即图论仿真,得到故障剧情样本.在此基础上,开发了一个图论建模推理平台.在此平台上进行相应的故障剧情分析,得到故障剧情样本.然后从中提取关联规则,并最终连接形成故障树.该文给出一个离心泵及液位系统的故障树生成过程,证明了此方法的实用性和有效性.     

基于云服务器的旋转机械在线监测诊断系统设计

以LabVIEW为开发平台,介绍了基于云服务器的在线监测及故障诊断系统的设计方法。该系统采用租用云服务器的方式,减免了对监测过程大数据的维护和管理;采用分布式设备振动数据采集器方式采集振动数据,实现设备分布式管理;开发数据采集存储软件,可视化操作;结合客户端软件和Web网页操作开发状态监测和故障诊断系统,通过监测系统发现设备异常情况,发出警报通知现场人员,通过诊断系统对故障数据进行详细分析,定位设备故障,及时采取保护措施,减少经济损失。     

一种新的定性定量故障诊断理论

根据系统的特征进行故障诊断往往因为故障的信息不足而难以确诊。将人工智能的思想引入故障诊断的理论之中以克服信息的不足和有效地利用专家知识,是一种解决故障诊断难题的有效方法。结合灰色定性仿真理论和Reiter R的基于第一原理的故障诊断理论的定性定量故障诊断理论被提出,它根据系统的现实状态与用灰色定性仿真预测的系统的状态的差异判断系统是否发生了故障,若故障出现。通过故障状态与故障模型的匹配确定故障的类型。这种方法能有效地结合的定性信息和定量信息,根据系统有限的定量信息建立其变量间的定性约束并将其应用于故障诊断。     

Formally verified on-line diagnosis

A reconfigurable fault tolerant system achieves the attributes of dependability of operations through fault detection, fault isolation and reconfiguration, typically referred to as the FDIR paradigm. Fault diagnosis is a key component of this approach, requiring an accurate determination of the health and state of the system. An imprecise state assessment can lead to catastrophic failure due to an optimistic diagnosis, or conversely, result in underutilization of resources because of a pessimistic diagnosis. Differing from classical testing and other off-line diagnostic approaches, we develop procedures for maximal utilization of the system state information to provide for continual, on-line diagnosis and reconfiguration capabilities as an integral part of the system operations. Our diagnosis approach, unlike existing techniques, does not require administered testing to gather syndrome information but is based on monitoring the system message traffic among redundant system functions. We present comprehensive on-line diagnosis algorithms capable of handling a continuum of faults of varying severity at the node and link level. Not only are the proposed algorithms on-line in nature, but are themselves tolerant to faults in the diagnostic process. Formal analysis is presented for all proposed algorithms. These proofs offer both insight into the algorithm operations and facilitate a rigorous formal verification of the developed algorithms     

青霉素生产过程的在线统计监测与产品质量控制

将多向偏最小二乘(MPLS)方法应用于青霉素间歇生产过程的建模与故障诊断中。从青霉素反应过程的特点来看,数据具有多维性,应用传统的偏最小二乘方法会使过程的统计建模与故障诊断难以实现。MPLS可对间歇过程的多维数据沿变量方向进行分割,使得多批量的数据可以在过程的各操作阶段建立相应的PLS模型,从而完成对该反应过程的实时监视与故障诊断。运用T2统计、Q统计方法,结合贡献图对过程进行了仿真分析,从理论分析和仿真实验结果的一致性,证明了该方法在青霉素生产过程的故障检测与诊断方面是可行的。