Modeling a web-based remote monitoring and fault diagnosis system with UML and component technology

As the Machinery Condition Monitoring and Fault Diagnosis Systems (MCMFDSs) are more and more complex, the design and development of these systems are becoming a challenge. The best way to manage the complexity and risk is to abstract and model them. This paper presents a new method of modeling Web-based Remote Monitoring and Fault Diagnosis Systems (WRMFDSs) with Unified Modeling Language (UML). A component framework model is put forward. A highly maintainable WRMFDS with three reusable component packages was developed using component-based programming. This paper, which studies a reusable WRMFDS model, aims at making such advanced information technologies be used widely in the condition monitoring and fault diagnosis domain, it can give developers a paradigm to accomplish the similar systems.     

非线性系统的故障诊断技术

文中对非线性系统的故障诊断方面问题给予了归纳总结,指出了基于数学模型方法,基于信号处理方法和基于知识的方法在实现非线性系统故障诊断的基本思想,并进一步指出了各各非线性系统故障诊断方法及可能的发展方向。     

Adaptive sliding mode observer for sensor fault diagnosis of an industrial gas turbine

Sensors are one of the crucial components in gas turbines and the failure in sensor measurements can lead to serious problems in maintaining their safety and performance requirements. Our aim in this paper is to develop an adaptive sliding mode observer for sensor fault diagnosis in an industrial gas turbine. The proposed observer has a robustness against gas turbine parameter uncertainties caused by degradations without any priori knowledge about the bounds of faults and parameter uncertainties. The efficiency of the proposed fault diagnosis approach is validated with Matlab/Simulink simulations and the realistic gas turbine data extracted from the PROOSIS software.     

Input design for guaranteed fault diagnosis using zonotopes

An input design method is presented for guaranteeing the diagnosability of faults from the outputs of a system. Faults are modeled by discrete switches between linear models with bounded disturbances and measurement errors. Zonotopes are used to efficiently characterize the set of inputs that are guaranteed to lead to outputs that are consistent with at most one fault scenario. Provided that this set is nonempty, an element is then chosen that is minimally harmful with respect to other control objectives. This approach leads to a nonconvex optimization problem, but is shown to be equivalent to a mixed-integer quadratic program that can be solved efficiently. Methods are given for reducing the complexity of this program, including an observer-based method that drastically reduces the number of binary variables when many sampling times are required for diagnosis.     

一起500千伏变压器多维状态监测与故障诊断

为了提升变压器故障诊断水平并有效实现状态检修,本文针对一起500千伏主变压器内部潜伏性故障案例介绍了利用多种在线监测、离线检测等手段与故障诊断方法快速准确查找出变压器故障原因及位置。它通过采用油色谱检测、变压器振动及声音检测、容性设备在线监测、变压器局部放电检测、带电测试等多维状态监测技术开展变压器状态评价及故障诊断,并帮助实现对主变压器内部故障的准确定位和制定状态检修策略。该主变在通过状态监测及故障诊断后实现隐患故障快速准确定位,并通过紧急处理重新恢复正常运行状态,从而避免一起重大设备事故发生。结果表明,应用变压器油溶解气体色谱分析检测技术,可通过排除法准确地判断变压器故障性质和严重程度,它是早期发现变压器潜伏性故障特别有效的方法。同时,采用基于交叉小波的变压器振动信号特征量提取方法分析评价结果表明,变压器铁芯发生接地故障后其振动信号存在大量50赫兹谐波分量。借助振动及声音检测技术,能够有助于变压器故障诊断及准确定位,提高设备状态检修效率。     

Sensor selection for fault diagnosis in uncertain systems

ABSTRACT

Finding the cheapest, or smallest, set of sensors such that a specified level of diagnosis performance is maintained is important to decrease cost while controlling performance. Algorithms have been developed to find sets of sensors that make faults detectable and isolable under ideal circumstances. However, due to model uncertainties and measurement noise, different sets of sensors result in different achievable diagnosability performance in practice. In this paper, the sensor selection problem is formulated to ensure that the set of sensors fulfils required performance specifications when model uncertainties and measurement noise are taken into consideration. However, the algorithms for finding the guaranteed global optimal solution are intractable without exhaustive search. To overcome this problem, a greedy stochastic search algorithm is proposed to solve the sensor selection problem. A case study demonstrates the effectiveness of the greedy stochastic search in finding sets close to the global optimum in short computational time.     

基于故障跟踪估计器的非线性时滞系统故障诊断

提出一种可有效检测和估计一类非线性时滞系统故障的故障跟踪估计器.根据预测控制和迭代学习控制的思想,在所选取的优化时域长度内,通过迭代算法调节故障跟踪估计器中的可调参数,使之逼近系统中实际发生的故障.与以往基于观测器的故障诊断方法不同的是,故障跟踪估计器可同时检测和估计系统中发生的故障,而且针对不同类型的故障亦有很好的适应性.仿真结果表明了所提出算法的可行性和有效性.     

Error detection and diagnosis for fault tolerance in distributed systems

The early error detection and the understanding of the nature and conditions of an error occurrence can be useful to make an effective and efficient recovery in distributed systems. Various distributed system extensions were introduced for the implementation of fault tolerance in distributed software systems. These extensions rely mainly on the exchange of contextual information appended to every transmitted application specific message. Ideally, this information should be used for checkpointing, error detection, diagnosis and recovery should a transient failure occur later during the distributed program execution. In this paper, we present a generalized extension suitable for fault-tolerant distributed systems such as communication software systems and its detection capabilities are shown. Our extension is based on the execution of message validity test prior to the transmission of messages and the piggybacking of contextual information to facilitate the detection and diagnosis of transient faults in the distributed system.     

计算机远程故障诊断系统的设计实现

目前存在某种结合B／S模式进行计算机远程故障疏导的系统结构样式,本文在具体研究该类系统故障分布形态以及框架机理调试过程中,主动开辟正反向推理途径实现机制延展,同时结合整体布局标准与规划结构进行细致应用方案研讨,确保为后期相关工程管制提供合理疏导经验,避免隔离故障隐患的集聚效应。     

Actuator fault diagnosis for a class of nonlinear systems and its application to a laboratory 3D crane

In this paper, an actuator fault diagnosis scheme is proposed for a class of affine nonlinear systems with both known and unknown inputs. The scheme is based on a novel input/output relation derived from the considered nonlinear systems and the use of the recently developed high-order sliding-mode robust differentiators. The main advantages of the proposed approach are that it does not require a design of nonlinear observer and applies to systems not necessarily detectable. Conditions are provided to characterize the feasibility of fault detection and isolation using the proposed scheme and the maximum number of isolatable actuator faults. The efficacy of the proposed actuator fault diagnosis approach is tested through experiments on a laboratory 3D Crane, and the experimental results show that the proposed actuator fault diagnosis approach is promising and can achieve fault detection and isolation satisfactorily.     

Design of a fault diagnosis scheme for a class of singular nonlinear systems

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.     

Multidisciplinary fault diagnosis of complex engineering systems: A case study of nuclear power plants

Fault diagnosis is a key process in ensuring complex engineering system safety. It often requires collaborative and multidisciplinary efforts. This study seeks to understand the process of multidisciplinary fault diagnosis in complex engineering systems and the key human factors issues that impair this process. Data were collected from multidisciplinary diagnostic activities conducted in the commissioning phase of nuclear power plants (NPPs). In the first phase, we proposed a process model based on a combination of literature review, specialist interviews, and field observations. In phase two, the influencing issues identified in the first phase were assessed through a survey with 117 NPP commissioning specialists. Five factors influencing multidisciplinary fault diagnosis were identified: cognitive artifacts, diagnosis biases, preparation for multidisciplinary diagnosis, information sharing and collaborative reasoning, and collaborative decision-making. The significances of each factor were compared. The results provide guidance for the development of improvement measures to enhance the performance of multidisciplinary fault diagnosis.Practitioner summaryThe processes and influencing issues of multidisciplinary fault diagnosis during the commissioning phase of nuclear power plants were studied with field observations, interviews of 28 specialists, and a survey of 117 specialists. Five major influencing factors were identified, and their influences were compared.     

Design of a fault diagnosis scheme for a class of singular nonlinear systems

A new fault detection and diagnosis approach is developed in this paper for a class of singular nonlinear systems via the use of adaptive updating rules. Both detection and diagnostic observers are established, where Lyapunov stability theory is used to obtain the required adaptive tuning rules for the estimation of the process faults. This has led to stable observation error systems for both fault detection and diagnosis. A simulated numerical example is included to demonstrate the use of the proposed approach and encouraging results have been obtained.     

A comprehensive fault diagnosis technique for reversible logic circuits

Fault diagnosis is a complex and challenging problem in reversible logic circuits. The paper proposes a novel fault diagnosis technique for missing control faults in reversible logic circuits. The main focus of this technique is to extract the unique fault signature for each missing control fault in the circuit. The fault signatures are the sequences of test vectors to identify the location of the faults. Based on these fault signatures a unique fault diagnosis tree is built. Our proposed fault diagnosis algorithm is used to traverse the fault diagnosis tree to find the presence and location of the fault. The traversal process is simple and fast. The algorithm executes in linear time and experimental results for benchmark circuits show the reduction of test patterns compared to earlier works.     

Robust fault diagnosis for a class of nonlinear systems

Robust fault diagnosis based on adaptive observer is studied for a class of nonlinear systems up to output injection. Adaptive fault updating laws are designed to guarantee the stability of the diagnosis system. The upper bounds of the state estimation error and fault estimation error of the adaptive observer are given respectively and the effects of parameter in the adaptive updating laws on fault estimation accuracy are also discussed. Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.     

Robust fault diagnosis for a class of nonlinear systems

Robust fault diagnosis based on adaptive observer is studied for a class of nonlinear systems up to output injection. Adaptive fault updating laws are designed to guarantee the stability of the diagnosis system. The upper bounds of the state estimation error and fault estimation error of the adaptive observer are given respectively and the effects of parameter in the adaptive updating laws on fault estimation accuracy are also discussed. Simulation example demonstrates the effectiveness of the proposed methods and the analysis results.     

Data-driven sensor fault diagnosis systems for linear feedback control loops

This paper develops a sensor fault diagnosis (SFD) scheme for a multi-input and multi-output linear dynamic system under feedback control to identify different types of sensor faults (bias, drift and precision degradation), particularly for the incipient sensor faults. Feedback control, leading to fault propagation and disguised fault rectification, imposes the challenge on the data-driven SFD. With only available output data in closed loop, the proposed scheme comprises two stages of residual generation and residual evaluation. In the residual generation, a data-driven identification of the residual generator for the feedback control system is proposed. One class of parameters in the residual generator are estimated using process delays while another class of parameters describing the output dynamic are derived by the Bayes’ formula. The means and variances control charts of online calculated residuals are made to judge the root cause. Two case studies are performed to illustrate the effectiveness of the proposed method.     

Model-based diagnosis and fault tolerant control for ensuring torque functional safety of pedal-by-wire systems

This paper presents a model based approach for defining automotive functional safety requirements and provides a solution to ensure functional safety through model-based diagnosis and fault tolerant control. This model-based approach is consistent with ISO 26262 – functional safety standard. In particular, this paper presents the necessary steps for defining and implementing functional safety requirements, including item and function definition, Hazard Analysis and Risk Assessment, as well as the design of a model-based diagnostic and fault tolerant control (FTC) system that can lead to a systematic solution to automotive functional safety problems. The methodology proposed in this paper is applied to the problem of torque functional safety of pedal-by-wire systems.     

Formal approach to fault diagnosis in distributed discrete event systems with OBDD

In this paper, we study the fault diagnosis problem for distributed discrete event systems. The model assumes that the system is composed of distributed components which are modeled in labeled Petri nets and interact with each other via sets of common resources (places). Further, a component’s own access to a common resource is an observable event. Based on the diagnoser approach proposed by Sampath et al., a distributed fault diagnosis algorithm with communication is presented. The distributed algorithm assumes that the local diagnosis process can exchange messages upon the occurrence of observable events. We prove the distributed diagnosis algorithm is correct in the sense that it recovers the same diagnostic information as the centralized diagnosis algorithm. Furthermore, we introduce the ordered binary decision diagrams (OBDD) in order to manage the state explosion problem in state estimation of the system.