Interface of the program Bayesian Diagnosis

This is a brief description of the interface of the program Bayesian Diagnosis

Optimal discrimination between transient and permanent faults

An important practical problem in fault diagnosis is discriminating between permanent faults and transient faults. In many computer systems, the majority of errors are due to transient faults. Many heuristic methods have been used for discriminating between transient and permanent faults; however, we have found no previous work stating this decision problem in clear probabilistic terms. We present an optimal procedure for discriminating between transient and permanent faults, based on applying Bayesian inference to the observed events (correct and erroneous results). We describe how the assessed probability that a module is permanently faulty must vary with observed symptoms. We describe and demonstrate our proposed method on a simple application problem, building the appropriate equations and showing numerical examples. The method can be implemented as a run-time diagnosis algorithm at little computational cost; it can also be used to evaluate any heuristic diagnostic procedure by compariso

Research on Improvement and Applications for Bayesian Fault Diagnosis

控制回路故障检测与诊断有助于保证生产过程的安全和高效、降低维护费用和减少停机时间。贝叶斯诊断是控制回路监测的概率化诊断框架，它能够综合多个监测器技术，以构建诊断系统进而作出最优决策。然而，工业过程控制回路诊断中存在许多不同的实际情况，严重制约了贝叶斯诊断的性能。本文重点从数据降维、似然估计等方面研究改进贝叶斯诊断性能的方法，提出了基于优化直方图估计的证据离散化方法、基于线性判别分析的特征提取与降维以及平均移动似然估计方法。通过仿真系统、工业基准数据和工业规模系统的仿真实验，验证了所提方法的有效性。论文主要包含以下几个方面的工作： (1) 综述了现有的贝叶斯诊断方法及其研究现状，系统介绍了控制...The purpose of control loop detection and diagnosis is to ensure the safety and efficacy of the production process, reduce maintenance costs and downtime. Bayesian diagnosis is a probabilistic diagnosis framework of control loop monitoring, which can combine multiple monitor technology to build a diagnosis system and make an optimal decision. However, there are many different situations in the con...学位：工程硕士院系专业：航空航天学院_工程硕士(控制工程)学号：2322013115337

A Bayesian Approach to Control Loop Performance Diagnosis Incorporating Background Knowledge of Response Information

To isolate the problem source degrading the control loop performance, this work focuses on how to incorporate background knowledge into Bayesian inference. In an effort to reduce dependence on the amount of historical data available, we consider a general kind of background knowledge which appears in many applications. The knowledge, known as response information, is about what faults can possibly affect each of the monitors. We show how this knowledge can be translated to constraints on the underlying probability distributions and introduced in the Bayesian diagnosis. In this way, the dimensionality of the observation space is reduced and thus the diagnosis can be more reliable. Furthermore, for the judgments to be consistent, the set of posterior probabilities of each possible abnormality that are computed from different observation subspaces is synthesized to obtain the partially ordered posteriors. The eigenvalue formulation is used on the pairwise comparison matrix. The proposed approach is applied to a diagnosis problem on an oil sand solids handling system, where it is shown how the combination of background knowledge and data enhances the control performance diagnosis even when the abnormality data are sparse in the historical database

Near Real-Time Probabilistic Damage Diagnosis Using Surrogate Modeling and High Performance Computing

This work investigates novel approaches to probabilistic damage diagnosis that utilize surrogate modeling and high performance computing (HPC) to achieve substantial computational speedup. Motivated by Digital Twin, a structural health management (SHM) paradigm that integrates vehicle-specific characteristics with continual in-situ damage diagnosis and prognosis, the methods studied herein yield near real-time damage assessments that could enable monitoring of a vehicle's health while it is operating (i.e. online SHM). High-fidelity modeling and uncertainty quantification (UQ), both critical to Digital Twin, are incorporated using finite element method simulations and Bayesian inference, respectively. The crux of the proposed Bayesian diagnosis methods, however, is the reformulation of the numerical sampling algorithms (e.g. Markov chain Monte Carlo) used to generate the resulting probabilistic damage estimates. To this end, three distinct methods are demonstrated for rapid sampling that utilize surrogate modeling and exploit various degrees of parallelism for leveraging HPC. The accuracy and computational efficiency of the methods are compared on the problem of strain-based crack identification in thin plates. While each approach has inherent problem-specific strengths and weaknesses, all approaches are shown to provide accurate probabilistic damage diagnoses and several orders of magnitude computational speedup relative to a baseline Bayesian diagnosis implementation

Emergency Management Information Frameworks for Spatial-alert Iterative Integration

Information frameworks are assuming progressively more imperative part in current emergency administration handle. A coordinated framework with abilities like foreknowledge, forecast and choice bolster capacities can give considerable included an incentive to leaders both strategic and arrangement making levels. It is however a testing errand to consistently incorporate different frameworks with devoted functionalities on useful and specialized angles, particularly when these frameworks are produced autonomously from each other with considerably extraordinary plan method of reasoning and programming innovation. In this paper, an iterative framework coordination approach is proposed by fitting administration situated, show driven and nimble framework improvement. A few outline standards and best practices from the product building group are received to encourage the incorporation undertaking. Furthermore, additional consideration is paid to give upgraded support to incorporating spatial information into the emergency administration work process. This approach means to give a commonsense framework reconciliation philosophy to incorporate emergency management information frameworks in a more compelling and proficient form

Bayesian network approach to fault diagnosis of a hydroelectric generation system

This study focuses on the fault diagnosis of a hydroelectric generation system with hydraulic-mechanical-electric structures. To achieve this analysis, a methodology combining Bayesian network approach and fault diagnosis expert system is presented, which enables the time-based maintenance to transform to the condition-based maintenance. First, fault types and the associated fault characteristics of the generation system are extensively analyzed to establish a precise Bayesian network. Then, the Noisy-Or modeling approach is used to implement the fault diagnosis expert system, which not only reduces node computations without severe information loss but also eliminates the data dependency. Some typical applications are proposed to fully show the methodology capability of the fault diagnosis of the hydroelectric generation system

Institute of Science and Technology Progress report, 15 Apr. 1969 - 15 Apr. 1970

Applications of decision making, probability theory, and multi-level inference systems to aerospace information processin