Development of an ontology for aerospace engine components degradation in service

This paper presents the development of an ontology for component service degradation. In this paper, degradation mechanisms in gas turbine metallic components are used for a case study to explain how a taxonomy within an ontology can be validated. The validation method used in this paper uses an iterative process and sanity checks. Data extracted from on-demand textual information are filtered and grouped into classes of degradation mechanisms. Various concepts are systematically and hierarchically arranged for use in the service maintenance ontology. The allocation of the mechanisms to the AS-IS ontology presents a robust data collection hub. Data integrity is guaranteed when the TO-BE ontology is introduced to analyse processes relative to various failure events. The initial evaluation reveals improvement in the performance of the TO-BE domain ontology based on iterations and updates with recognised mechanisms. The information extracted and collected is required to improve service k nowledge and performance feedback which are important for service engineers. Existing research areas such as natural language processing, knowledge management, and information extraction were also examined

Extracting causal relationships from Chinese written text

Expert systems form one of the most important research areas in Artificial Intelligence. The main parts in expert systems are knowledge bases and inference engines. In the knowledge bases the main knowledge is knowledge in the form of ``IF-THEN" statements. In knowledge graphs, a new form of knowledge representation, the ``IF-THEN" statements are tied up with causal operators (CAU-relations). In this paper, we picked out some Chinese operators with ``CAU" meaning, and investigated these operators. We also show by an example how to extract causal relations from a given Chinese writing text

Data mining and knowledge reuse for the initial systems design and manufacturing: Aero-engine service risk drivers

Service providers of civil aero engines are typically confronted with a high cost of maintenance, replacement and refurbishment of the service damaged components. In such context, service experience becomes a key issue for determining the service risk drivers for operational disruptions and maintenance burden. This paper presents an industrial case study to produce new knowledge on the relationships between degradation and component design to manufacture. The study applied semantic data mining as a methodology for an efficient and the consistent data capture, representation, and analysis. The paper aims at identifying the service risk drivers based on service experience and event data. The analysis shows that the 3 top mechanisms accounting for 32% of the mechanism references have a strong Pareto effect. The paper concludes with missing information links and future research directions

Root cause analysis in large and complex networks

Tese de mestrado em Segurança Informática, apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2008Uma grande parte do sucesso de uma empresa depende do desempenho da função de Tecnologias de Informação. Em redes de grandes dimensões, devido à evolução do número de clientes e às constantes mudanças nas necessidades das empresas, as dependências entre sistemas e elementos de rede têm vindo a tornar-se cada vez mais complexas. Consequentemente, a localização das causas originais de problemas de desempenho de sistemas é uma tarefa complexa. A rede tem de ser analizada como um todo porque, mesmo durante a ocorrência de uma falha, todos os sistemas podem parecer estar correctos quando analizados separada e instantâneamente. O objectivo deste projecto é o estudo de uma solução automática de análise de causas originais de falhas em redes complexas e de grandes dimensões. Neste trabalho, é apresentado o Etymon, uma ferramenta que identifica os componentes e métricas mais relevantes para explicar os problemas que afectam o trabalho diário dos utilizadores finais. O presente trabalho propõe uma arquitectura modular para executar as acções necessárias para encontrar uma explicação para um problema de desempenho. A análise começa por processar registos de falhas (trouble-tickets) de forma a identificar os principais períodos de desempenho degradado. O tráfego de rede é analizado continuamente para identificar as dependências entre componentes e mantê-las actualizadas. Usando a informação sobre dependências, é criado um modelo da rede que representa o ambiente para uma aplicação específica. De seguida, é avaliado o estado de cada componente do modelo durante o período do problema com base em desvios do seu comportamento habitual. Finalmente, é feita a pesquisa no modelo por caminhos causais em que o primeiro componente corresponde à causa original do problema. Para testar a aplicação desenvolvida foi utilizada a rede empresarial de um operador de telecomunicações Europeu. Assim, foram enfrentados todos os desafios inerA huge share of a company's success relies on the performance of its IT infrastructure. In large networks, due to the evolution of the number of clients and changes in the company requirements, the dependencies among systems and network elements tend to become increasingly complex. Consequently, the localization of root-causes of performance problems is a very challenging task. The network must be analyzed as a whole because, despite the failure, all systems may seem to work fine when analyzed separately. The purpose of this project is to study an automatic root-cause analysis of failures in large and complex networks. We present Etymon, a tool that identifies the most relevant network components and metrics to explain performance problems affecting the daily work of end-users. We propose a modular architecture to perform the tasks necessary to find explanation root-cause of a problem. The analysis starts by processing trouble tickets in order to identify the major performance issues. Traffic monitoring and analysis are continuously performed on the network to identify the dependencies among components. Using the dependency information, we create a network model that represents the environment for a specific application. We then evaluate the state of each component of the model during the time when the trouble ticket is issued, based on deviations from observed normal behavior. Finally, we search the model for causal paths that start on a root-cause component and provide an explanation for the failure. The testbed for our application is the enterprise IT network of a large European Telecom operator. Therefore, we face challenges of applying such tools to a production network. For example, the challenges are possible lack of information about applications, complex interactions, and high number of workflows. Etymon introduces concepts such as environment-specific network model, context-conditioned dependency information, temporal correlation of the anomalies an

Structural Agnostic Modeling: Adversarial Learning of Causal Graphs

A new causal discovery method, Structural Agnostic Modeling (SAM), is presented in this paper. Leveraging both conditional independencies and distributional asymmetries in the data, SAM aims at recovering full causal models from continuous observational data along a multivariate non-parametric setting. The approach is based on a game between $d$ players estimating each variable distribution conditionally to the others as a neural net, and an adversary aimed at discriminating the overall joint conditional distribution, and that of the original data. An original learning criterion combining distribution estimation, sparsity and acyclicity constraints is used to enforce the end-to-end optimization of the graph structure and parameters through stochastic gradient descent. Besides the theoretical analysis of the approach in the large sample limit, SAM is extensively experimentally validated on synthetic and real data

Neural Connectivity with Hidden Gaussian Graphical State-Model

The noninvasive procedures for neural connectivity are under questioning. Theoretical models sustain that the electromagnetic field registered at external sensors is elicited by currents at neural space. Nevertheless, what we observe at the sensor space is a superposition of projected fields, from the whole gray-matter. This is the reason for a major pitfall of noninvasive Electrophysiology methods: distorted reconstruction of neural activity and its connectivity or leakage. It has been proven that current methods produce incorrect connectomes. Somewhat related to the incorrect connectivity modelling, they disregard either Systems Theory and Bayesian Information Theory. We introduce a new formalism that attains for it, Hidden Gaussian Graphical State-Model (HIGGS). A neural Gaussian Graphical Model (GGM) hidden by the observation equation of Magneto-encephalographic (MEEG) signals. HIGGS is equivalent to a frequency domain Linear State Space Model (LSSM) but with sparse connectivity prior. The mathematical contribution here is the theory for high-dimensional and frequency-domain HIGGS solvers. We demonstrate that HIGGS can attenuate the leakage effect in the most critical case: the distortion EEG signal due to head volume conduction heterogeneities. Its application in EEG is illustrated with retrieved connectivity patterns from human Steady State Visual Evoked Potentials (SSVEP). We provide for the first time confirmatory evidence for noninvasive procedures of neural connectivity: concurrent EEG and Electrocorticography (ECoG) recordings on monkey. Open source packages are freely available online, to reproduce the results presented in this paper and to analyze external MEEG databases