Health monitoring,of a helicopter rotor in forward flight using fuzzy logic

A fuzzy logic system is developed for helicopter rotor system fault isolation. Inputs to the fuzzy logic system are measurement deviations of blade bending and torsion response and vibration from a "good" undamaged helicopter rotor. The rotor system measurements used are flap and lag bending tip deflections, elastic twist deflection at the tip, and three forces and three moments at the rotor hub. The fuzzy logic system uses rules developed from an aeroelastic model of the helicopter rotor with implanted faults to isolate the fault while accounting for uncertainty in the measurements. The faults modeled include moisture absorption, loss of trim mass, damaged lag damper, damaged pitch control system, misadjusted pitch link, and damaged flap. Tests with simulated data show that the fuzzy system isolates rotor system faults with an accuracy of about 90-100%. Furthermore, the fuzzy system is robust and gives excellent results, even when some measurements are not available. A rule-based expert system based on similar rules from the aeroelastic model performs much more poorly than the fuzzy system in the presence of high levels of uncertainty.

Noise and outlier removal from jet engine health signals using weighted FIR median hybrid filters

The removal of noise and outliers from measurement signals is a major problem in jet engine health monitoring. Topical measurement signals found in most jet engines include low rotor speed, high rotor speed. fuel flow and exhaust gas temperature. Deviations in these measurements from a baseline 'good' engine are often called measurement deltas and the health signals used for fault detection, isolation, trending and data mining. Linear filters such as the FIR moving average filter and IIR exponential average filter are used in the industry to remove noise and outliers from the jet engine measurement deltas. However, the use of linear filters can lead to loss of critical features in the signal that can contain information about maintenance and repair events that could be used by fault isolation algorithms to determine engine condition or by data mining algorithms to learn valuable patterns in the data, Non-linear filters such as the median and weighted median hybrid filters offer the opportunity to remove noise and gross outliers from signals while preserving features. In this study. a comparison of traditional linear filters popular in the jet engine industry is made with the median filter and the subfilter weighted FIR median hybrid (SWFMH) filter. Results using simulated data with implanted faults shows that the SWFMH filter results in a noise reduction of over 60 per cent compared to only 20 per cent for FIR filters and 30 per cent for IIR filters. Preprocessing jet engine health signals using the SWFMH filter would greatly improve the accuracy of diagnostic systems. (C) 2002 Published by Elsevier Science Ltd.

Cuckoo: a Language for Implementing Memory- and Thread-safe System Services

This paper is centered around the design of a thread- and memory-safe language, primarily for the compilation of application-specific services for extensible operating systems. We describe various issues that have influenced the design of our language, called Cuckoo, that guarantees safety of programs with potentially asynchronous flows of control. Comparisons are drawn between Cuckoo and related software safety techniques, including Cyclone and software-based fault isolation (SFI), and performance results suggest our prototype compiler is capable of generating safe code that executes with low runtime overheads, even without potential code optimizations. Compared to Cyclone, Cuckoo is able to safely guard accesses to memory when programs are multithreaded. Similarly, Cuckoo is capable of enforcing memory safety in situations that are potentially troublesome for techniques such as SFI.

Improved Process Monitoring Using Nonlinear Principal Component Models

This paper presents two new approaches for use in complete process monitoring. The firstconcerns the identification of nonlinear principal component models. This involves the application of linear
principal component analysis (PCA), prior to the identification of a modified autoassociative neural network (AAN) as the required nonlinear PCA (NLPCA) model. The benefits are that (i) the number of the reduced set of linear principal components (PCs) is smaller than the number of recorded process variables, and (ii) the set of PCs is better conditioned as redundant information is removed. The result is a new set of input data for a modified neural representation, referred to as a T2T network. The T2T NLPCA model is then used for complete process monitoring, involving fault detection, identification and isolation. The second approach introduces a new variable reconstruction algorithm, developed from the T2T NLPCA model. Variable reconstruction can enhance the findings of the contribution charts still widely used in industry by reconstructing the outputs from faulty sensors to produce more accurate fault isolation. These ideas are illustrated using recorded industrial data relating to developing cracks in an industrial glass melter process. A comparison of linear and nonlinear models, together with the combined use of contribution charts and variable reconstruction, is presented.

Improved principal component monitoring using the local approach

This paper shows that current multivariate statistical monitoring technology may not detect incipient changes in the variable covariance structure nor changes in the geometry of the underlying variable decomposition. To overcome these deficiencies, the local approach is incorporated into the multivariate statistical monitoring framework to define two new univariate statistics for fault detection. Fault isolation is achieved by constructing a fault diagnosis chart which reveals changes in the covariance structure resulting from the presence of a fault. A theoretical analysis is presented and the proposed monitoring approach is exemplified using application studies involving recorded data from two complex industrial processes. © 2007 Elsevier Ltd. All rights reserved.

Aplicação de redes neuronais artificiais à deteção e isolamento de falhas em processos industriais

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

A proposal for the diagnosis of uncertain dynamic systems based on interval models

The performance of a model-based diagnosis system could be affected by several uncertainty sources, such as,model errors,uncertainty in measurements, and disturbances. This uncertainty can be handled by mean of interval models.The aim of this thesis is to propose a methodology for fault detection, isolation and identification based on interval models. The methodology includes some algorithms to obtain in an automatic way the symbolic expression of the residual generators enhancing the structural isolability of the faults, in order to design the fault detection tests. These algorithms are based on the structural model of the system. The stages of fault detection, isolation, and identification are stated as constraint satisfaction problems in continuous domains and solved by means of interval based consistency techniques. The qualitative fault isolation is enhanced by a reasoning in which the signs of the symptoms are derived from analytical redundancy relations or bond graph models of the system. An initial and empirical analysis regarding the differences between interval-based and statistical-based techniques is presented in this thesis. The performance and efficiency of the contributions are illustrated through several application examples, covering different levels of complexity.

Network reconfiguration and loss allocation in a deregulated environment of distribution systems

Low flexibility and reliability in the operation of radial distribution networks make those systems be constructed with extra equipment as sectionalising switches in order to reconfigure the network, so the operation quality of the network can be improved. Thus, sectionalising switches are used for fault isolation and for configuration management (reconfiguration). Moreover, distribution systems are being impacted by the increasing insertion of distributed generators. Hence, distributed generation became one of the relevant parameters in the evaluation of systems reconfiguration. Distributed generation may affect distribution networks operation in various ways, causing noticeable impacts depending on its location. Thus, the loss allocation problem becomes more important considering the possibility of open access to the distribution networks. In this work, a graphic simulator for distribution networks with reconfiguration and loss allocation functions, is presented. Reconfiguration problem is solved through a heuristic methodology, using a robust power flow algorithm based on the current summation backward-forward technique, considering distributed generation. Four different loss allocation methods (Zbus, Direct Loss Coefficient, Substitution and Marginal Loss Coefficient) are implemented and compared. Results for a 32-bus medium voltage distribution network, are presented and discussed.

Reconfiguração de sistemas de distribuição operando em vários níveis de demanda através de uma meta-heurística de busca em vizinhança variável

Pós-graduação em Engenharia Elétrica - FEIS

Mixed-criticality design of a satellite software system

The continuous increment of processors computational power and the requirements on additional functionality and services are motivating a change in the way embedded systems are built. Components with different criticality level are allocated in the same processor, which give rise to mixed-criticality systems. The use of partitioned systems is a way of preventing undesirable interferences between components with different criticality level. An hypervisor provides these partitions or virtual machines, ensuring spatial, temporal and fault isolation between them. The purpose of this paper is to illustrate the development of a mixed-critical system. The attitude control subsystem is used for showing the different steps, which are supported by a toolset developed in the context of the MultiPARTES research project.

Implementación de un entorno de mantenimiento centralizado para el diagnóstico y aislamiento de fallos en plataformas de sistemas modulares

En la actualidad gran parte de las industrias utilizan o desarrollan plataformas, las cuales integran un número cada vez más elevado de sistemas complejos. El mantenimiento centralizado permite optimizar el mantenimiento de estas plataformas, por medio de la integración de un sistema encargado de gestionar el mantenimiento de todos los sistemas de la plataforma. Este Trabajo Fin de Máster (TFM) desarrolla el concepto de mantenimiento centralizado para sistemas complejos, aplicable a plataformas formadas por sistemas modulares. Está basado en la creciente demanda de las diferentes industrias en las que se utilizan este tipo de plataformas, como por ejemplo la industria aeronáutica, del ferrocarril y del automóvil. Para ello este TFM analiza el Estado del Arte de los sistemas de mantenimiento centralizados en diferentes industrias, además desarrolla los diferentes tipos de arquitecturas de sistemas, las técnicas de mantenimiento aplicables, así como los sistemas y técnicas de mantenimiento basados en funciones de monitorización y auto diagnóstico denominadas Built-In-Test Equipment (BITE). Adicionalmente, este TFM incluye el desarrollo e implementación de un modelo de un Entorno de Mantenimiento Centralizado en LabVIEW. Este entorno está formado por el modelo de un Sistema Patrón, así como el modelo del Sistema de Mantenimiento Centralizado y la interfaces entre ellos. El modelo del Sistema de Mantenimiento Centralizado integra diferentes funciones para el diagnóstico y aislamiento de los fallos. Así mismo, incluye una función para el análisis estadístico de los datos de fallos almacenados por el propio sistema, con el objetivo de proporcionar capacidades de mantenimiento predictivo a los sistemas del entorno. Para la implementación del modelo del Entorno de Mantenimiento Centralizado se han utilizado recursos de comunicaciones vía TCP/IP, modelización y almacenamiento de datos en ficheros XML y generación automática de informes en HTML. ABSTRACT. Currently several industries are developing or are making use of multi system platforms. These platforms are composed by many complex systems. The centralized maintenance allows the maintenance optimization, integrating a maintenance management system. This system is in charge of managing the maintenance dialog with the different and multiple platforms. This Master Final Project (TFM) develops the centralized maintenance concept for platforms integrated by modular and complex systems. This TFM is based on the demand of the industry that uses or develops multi system platforms, as aeronautic, railway, and automotive industries. In this way, this TFM covers and analyzes several aspects of the centralized maintenance systems like the State of the Art, for several industries. Besides this work develops different systems architecture types, maintenance techniques, and techniques and systems based on Built-in-test Equipment functions. Additionally, this TFM includes a LabVIEW Centralized System Environment model. This model is composed by a Standard System, the Centralized Maintenance System and the corresponding interfaces. Several diagnostic and fault isolation functions are integrated on the Centralized Maintenance Systems, as well a statistic analysis function, that provides with predictive maintenance capacity, based on the failure data stored by the system. Among others, the following resources have been used for the Centralized System Environment model development: TCP/IP communications, XML file data modelization and storing, and also automatic HTML reports generation.

Custom Surrogate Host for ActiveX In-Process Servers

This paper describes the design, implementation and evaluation of AX Host, a custom surrogate host for ActiveX in-process servers. AX Host aims to give ActiveX client applications improved stability by using software fault isolation.

A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended Kalman filter

Interior permanent-magnet synchronous motors (IPMSMs) become attractive candidates in modern hybrid electric vehicles and industrial applications. Usually, to obtain good control performance, the electric drives of this kind of motor require one position, one dc link, and at least two current sensors. Failure of any of these sensors might lead to degraded system performance or even instability. As such, sensor fault resilient control becomes a very important issue in modern drive systems. This paper proposes a novel sensor fault detection and isolation algorithm based on an extended Kalman filter. It is robust to system random noise and efficient in real-time implementation. Moreover, the proposed algorithm is compact and can detect and isolate all the sensor faults for IPMSM drives. Thorough theoretical analysis is provided, and the effectiveness of the proposed approach is proven by extensive experimental results.

Actuator fault detection and isolation in linear systems

A scheme for the detection and isolation of actuator faults in linear systems is proposed. A bank of unknown input observers is constructed to generate residual signals which will deviate in characteristic ways in the presence of actuator faults. Residual signals are unaffected by the unknown inputs acting on the system and this decreases the false alarm and miss probabilities. The results are illustrated through a simulation study of actuator fault detection and isolation in a pilot plant doubleeffect evaporator.

Hybrid fault detection and isolation in an heater bank of an HVAC system

In this work a hybrid technique that includes probabilistic and optimization based methods is presented. The method is applied, both in simulation and by means of real-time experiments, to the heating unit of a Heating, Ventilation Air Conditioning (HVAC) system. It is shown that the addition of the probabilistic approach improves the fault diagnosis accuracy.