无人机PCA故障检测与诊断技术研究

无人机（UAV）飞控系统传感器故障检测和诊断常采用基于解析模型的方法,但飞行控制系统（FCS）的精确数学模型往往获取困难。针对此问题,提出了一种UAV-PCA算法。该算法在传统主成分分析（PCA）方法的基础上结合方差敏感自适应阈值的故障检测方法和基于特征方向的故障诊断方法,实现UAV飞控系统传感器的故障检测和诊断。算法不需要系统的数学模型,解决了应用传统PCA方法进行FCS故障检测与诊断时易出现暂态过程虚警和误诊断的问题。仿真结果证明该算法可以快速准确地检测传感器故障,而且可以有效地降低暂态过程虚警和提高诊断结果准确度。     

一种新的故障检测与诊断方法及其应用研究

给出了在故障检测与诊断中采用经验模式分解与希尔伯特变换相结合的方法。经验模式分解不同于小波变换、KL变换、奇异值分解(SVD)等信号分解方法,它把数据序列分解为能够表示数据中嵌入的不同振荡的本征模式函数。首先介绍方法的原理与特点,然后将该方法用于齿轮故障的探测与诊断,结果显示,这种方法能准确识别出裂缝故障的特征频率。     

Comparison of fault detection techniques

Haar Wave-Net (HWN) and Projection Pursuit Regression (PPR) are two useful modeling tools for pattern classification. In this study, the two methodologies are compared with respect to the problem of misclassification close to class boundaries with sparse training data. A variety of examples were specifically tailored to elucidate their respective properties. It is observed that PPR locates the class boundaries at the midline of two classes of training data, which is a logical choice for the class boundary location, in the absence of sufficient information. For HWN, both the initial positioning of receptive fields and the density of training data near the class boundary may have great impact on the definition of the class boundary. Additionally, PPR and HWN are also compared to the Backpropagation Network (BPN), a standard technique for fault detection, with respect to their sensitivity to noise. The orthonormal and localized properties of the Haar basis functions enable a HWN to limit the noise effect within its local receptive fields. BPN propagates the noise effect throughout the input space. PPR provides a good tradeoff between reasonable generalization and noise localization. The fault diagnosis problem is investigated in a CSTR process, at both steady state and dynamic conditions. It is found that, for the dynamic case, the misclassification close to the class boundary is often due to lack of system observability.     

Cognitive processes in software fault detection: A review and synthesis

The authors discuss empirical research about the cognitive structures and strategies used by programmers during fault location. Empirical evidence indicates the cognitive processes involved in fault detection consist of a comprehension process and a fault location process. The two processes are distinct and separate. The comprehension process is extremely important and was found to be superior in experts due to the semantic encoding they utilize. The semantic representations used by experts consist of abstract hierarchies based on functional meaning. Fault location is less important and usually takes the form of hand simulation or causal reasoning. The fault locating strategies used by experts and novices were similar. The better debugging performance by experts is due to their superior abilities at comprehension. Research indicates that the semantic organizations used by experts can be successfully taught to novices and used by them to improve performance.

The authors also examine two methods, slicing and team reviews, which seek to improve the debugging process. Each was found to affect comprehension and fault location differently. A review of slicing research revealed that it is performed during the fault location process, and does not apply to the comprehension process as some believe. Automating slicing was found to be a technique with potential benefits for debugging. The survey of the team dynamics during inspections and other reviews found them to be effective by enhancing the comprehension process, by improving fault location, and by providing more than one chance to catch each error.     

A fault detection system based on unsupervised techniques for industrial control loops

This research describes a novel approach for fault detection in industrial processes, by means of unsupervised and projectionist techniques. The proposed method includes a visual tool for the detection of faults, its final aim is to optimize system performance and consequently obtaining increased economic savings, in terms of energy, material, and maintenance. To validate the new proposal, two datasets with different levels of complexity (in terms of quantity and quality of information) have been used to evaluate five well‐known unsupervised intelligent techniques. The obtained results show the effectiveness of the proposed method, especially when the complexity of the dataset is high.     

基于故障检测次序的供料单元故障诊断

针对气动PLC自动生产线中供料单元,在一次供料过程时,上电后却无法运作,通过观察其故障现象,分析其故障原因,提出设定故障检查次序,综合利用假设验证法、替换法、经验法和测量法等故障诊断方法,排除设备的故障,继而通过实践证明合理设定故障检查次序对设备故障排除的重要性.     

A quest for appropriate software fault models: Case studies on fault detection effectiveness of model-based test generation techniques

Model-based test generation (MBTG) is becoming an area of active research. These techniques differ in terms of (1) modeling notations used, and (2) the adequacy criteria used for test generation. This paper (1) reviews different classes of MBTG techniques at a conceptual level, and (2) reports results of three case studies comparing various techniques in terms of their fault detection effectiveness. Our results indicate that MBTG technique which employs mutation and explicitly generates state verification sequences has better fault detection effectiveness than those based on boundary values, and predicate coverage criteria for transitions. Instead of a default adequacy criteria, certain techniques allow the user to specify test objectives in addition to the model. Our experience indicates that the task of defining appropriate test objectives is not intuitive. Furthermore, notations provided to describe such test objectives may have inadequate expressive power. We posit the need for a suitable fault modeling notation which also treats domain invariants as first class entities.     

导航系统故障检测与诊断技术综述

导航系统的故障检测与诊断技术受到理论界的广泛重视,总结了国内外应用于导航系统的故障检测与诊断方法:基于硬件冗余方法、基于χ2检验方法、基于奇偶空间方法、基于小波变换方法、基于神经网络方法、基于联邦滤波器方法和一些其他方法.讨论了导航系统的故障检测与诊断发展趋势.     

基于定性和半定性方法的故障检测与诊断技术

首先介绍了基于定性和半定性方法的故障检测与诊断技术的产生背景和发展状况, 然后分基于定性方法和基于半定性方法. ———两大类介绍了主要的故障检测和诊断技术, 讨论了各种方法的优缺点, 介绍了一些典型的应用实例, 并对发展的趋向进行了探讨.     

A distributed approach for fault detection and diagnosis based on Time Petri Nets

This paper proposes an algorithm for the model based design of a distributed protocol for fault detection and diagnosis for very large systems. The overall process is modeled as different Time Petri Net (TPN) models (each one modeling a local process) that interact with each other via guarded transitions that becomes enabled only when certain conditions (expressed as predicates over the marking of some places) are satisfied (the guard is true). In order to use this broad class of time DES models for fault detection and diagnosis we derive in this paper the timing analysis of the TPN models with guarded transitions. In this paper we also extend the modeling capability of the faults calling some transitions faulty when operations they represent take more or less time than a prescribed time interval corresponding to their normal execution. We consider here that different local agents receive local observation as well as messages from neighboring agents. Each agent estimates the state of the part of the overall process for which it has model and from which it observes events by reconciling observations with model based predictions. We design algorithms that use limited information exchange between agents and that can quickly decide “questions” about “whether and where a fault occurred?” and “whether or not some components of the local processes have operated correctly?”. The algorithms we derive allow each local agent to generate a preliminary diagnosis prior to any communication and we show that after communicating the agents we design recover the global diagnosis that a centralized agent would have derived. The algorithms are component oriented leading to efficiency in computation.     

A framework for real-time fault detection and diagnosis using temporal data

Successful real-time sensor-based fault detection and diagnosis in large and complex systems is seldom achieved by operators. The lack of an effective method for handling temporal data is one of several key problems in this area. A methodology is introduced which advantageously uses temporal data in performing fault diagnosis in a subsystem of a Navy ship propulsion system. The methodology is embedded in a computer program designed to be used as a decision aid to assist the operator. It utilizes machine learning, is able to cope with uncertainty at several levels, and works in real-time. Program performance data is presented and analysed. The approach illustrates how relatively simple existing techniques can be assembled into more powerful real-time diagnostic tools.     

A systematic review of machine learning techniques for software fault prediction

BackgroundSoftware fault prediction is the process of developing models that can be used by the software practitioners in the early phases of software development life cycle for detecting faulty constructs such as modules or classes. There are various machine learning techniques used in the past for predicting faults.MethodIn this study we perform a systematic review of studies from January 1991 to October 2013 in the literature that use the machine learning techniques for software fault prediction. We assess the performance capability of the machine learning techniques in existing research for software fault prediction. We also compare the performance of the machine learning techniques with the statistical techniques and other machine learning techniques. Further the strengths and weaknesses of machine learning techniques are summarized.ResultsIn this paper we have identified 64 primary studies and seven categories of the machine learning techniques. The results prove the prediction capability of the machine learning techniques for classifying module/class as fault prone or not fault prone. The models using the machine learning techniques for estimating software fault proneness outperform the traditional statistical models.ConclusionBased on the results obtained from the systematic review, we conclude that the machine learning techniques have the ability for predicting software fault proneness and can be used by software practitioners and researchers. However, the application of the machine learning techniques in software fault prediction is still limited and more number of studies should be carried out in order to obtain well formed and generalizable results. We provide future guidelines to practitioners and researchers based on the results obtained in this work.     

基于马尔科夫预测的多传感器故障检测与诊断机制

随着无线传感器网络应用规模的不断扩大,各类应用中传感器故障检测与诊断成为系统正常作业、安全可靠性保障的关键技术。针对多传感器系统与节点工作过程定义3种状态,基于故障检测信息建立状态转移矩阵,通过马尔科夫模型预测传感器故障信息,为故障检测与诊断提供决策依据。另外,拓展数据包信息字段包括故障类型、节点定位等,故障处理后节点转移至正常状态后将故障处理和诊断特征等信息存储到网关或者汇聚节点,为改善故障检测精度和诊断效率以及系统资源利用率提供依据。实验结果表明：所提故障检测与诊断算法与传统算法相比,具有更高的故障检测精度,更短的故障诊断时延、能够准确判断故障类型等性能。     

故障诊断知识标准化描述方法综述

诊断知识是智能诊断系统的核心。对传统的知识表示、面向对象的知识表示、AI-ESTATE诊断知识标准化表示、基于XML语言和ATML标准的诊断知识表示进行比较,总结了故障诊断知识表示方法的发展趋势,分析了ATML描述AI-ESTATE标准定义的各种测试诊断知识的优势与实现困难。通过对典型的AI-ESTATE诊断知识类型转换方法和描述进行举例,为面向ATML标准的AI-ESTATE诊断信息标准化描述方法指明了方向。针对语言定义的不同使得目前的描述还不高效的现状,对未来标准化描述发展方向做出了展望。     

A discrete event systems approach to network fault management: detection and diagnosis of faults

An important aspect of network management is fault management, which involves, detecting, locating, isolating, correcting and adapting to faults in the network. We study modeling of communication network protocol and fault detection, identification and localization in the discrete event system diagnosis framework. As an illustration of the approach, normal and faulty behavior of the X.25 network protocol is modeled as a finite state machine. This modeling formalism allows the utilization of discrete event system analysis for the detection and diagnosis of faults. Our approach provides a systematic way of performing fault diagnosis for network fault management. Copyright © 2011 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

一类基于神经网络非线性观测器的鲁棒故障检测和诊断

利用神经网络的非线性建模能力，对一类具有建模不确定项的非线性系统提出一种基于观测器的故障检测和诊断的方法。设计的观测器不仅能实现故障检测，而旦应用神经网络设计的故障估计器能在线估计系统中的故障向量。通过分析验证了该方法对系统中的建模误差和外部扰动具有良好的鲁棒性。仿真结果表明所提出的方法是有效的。     

A neuro-fuzzy online fault detection and diagnosis algorithm for nonlinear and dynamic systems

This paper presents a new fault detection and diagnosis approach for nonlinear dynamic plant systems with a neuro-fuzzy based approach to prevent developing of fault as soon as possible. By comparison of plants and neuro-fuzzy estimator outputs in the presence of noise, residual signal is generated and compared with a predefined threshold, the fault can be detected. To diagnose the type, size, time and fault conditions, are used analytical approach and neural network for tracking fault developing online. The neuro-fuzzy nets are compared with some other identification methods in application of power plant gas turbine. Faults are considered in two forms, step, and ramp shape. This work was implemented with real data from gas turbine of Kazeroun (Iran) power plant (Mitsubishi unit) and result is presented to demonstrate the benefits of the proposed method.     

A novel data-based quality-related fault diagnosis scheme for fault detection and root cause diagnosis with application to hot strip mill process

In this paper, a new technology or solution of quality-related fault diagnosis is provided for hot strip mill process (HSMP). Different from traditional data-based fault diagnosis methods, the alternative approach is focused more on root cause diagnosis. The new scheme addresses the quality-related fault detection with the developed modified canonical variable analysis (MCVA) model, then the advantage of original generalized reconstruction based contribution (GRBC) is followed to identify the faulty variables. Meanwhile, a new transfer entropy (TE)-based causality analysis method is proposed for root cause diagnosis of quality-related faults. Finally, the whole proposed framework is practiced with real HSMP data, and the results demonstrate the usage and effectiveness of these approaches.     

Induction motors bearing fault detection using pattern recognition techniques

This paper proposes a systematic procedure based on a pattern recognition technique for fault diagnosis of induction motors bearings through the artificial neural networks (ANNs). In this method, the use of time domain features as a proper alternative to frequency features is proposed to improve diagnosis ability. The features are obtained from direct processing of the signal segments using very simple calculation. Three different cases including, healthy, inner race defect and outer race defect are investigated using the proposed algorithm. The ANNs are trained with a subset of the experimental data for known machine conditions. Once the network is trained, efficiency of the proposed method is evaluated using the remaining set of data. The obtained results indicate that using time domain features can be effective in accurate diagnosis of various motor bearing faults with high precision and low computational burden.     

嵌入式设备故障检测和诊断系统设计

本文分析嵌入式设备的特点,并在此基础上提出充分利用嵌入式设备提供的接口资源,实现故障检测和诊断的方法,将设备故障定位到电路板级,为板级电路的故障检测和诊断奠定基础。此种方法在复杂工程环境下,无需拆卸嵌入式设备即可判断整机性能,达到故障检测和诊断的基本要求。在这一方法基础上,本文以某车载GPS导航系统为被测对象,详细介绍了故障检测和诊断系统设计。