Detección y diagnóstico de fallos múltiples en sistemas dinámicos usando análisis de componentes principales no lineal y residuos estructurados

In recent years the detection and diagnosis of faults in devices and processes has been a field of research and development that has been addressed from multiple perspectives: control engineering, artificial intelligence and statistics among others. Within each with a number of techniques: expert systems , neural networks, case-based reasoning , signal analysis, observers equations analytical redundancy based on consistency, but only considering the presence of multiple failures and no diagnosis. In this thesis the detection and diagnosis of multiple faults in dynamic systems using nonlinear analysis for the detection and diagnosis for structured waste components will be studied. The study includes the development of a mathematical model of a gas turbine mini SR -30 and implementation of algorithms for detection and diagnosis of multiple faults in the system. The detection and diagnosis of multiple faults is more complex than the detection and diagnosis of single faults. This is not only because it increases the number of failures, but also because of the emergence of new phenomena that should be considered, such as a combination or interaction of faults, compensation, and the combinatorial explosion of possible failure scenarios. The problem of multiple failures is important, since the single fault assumption can lead to incorrect diagnoses when multiple faults occur.En los últimos años la detección y el diagnóstico de fallos en dispositivos y en procesos ha sido un campo de investigación y desarrollo que se ha podido abordar desde múltiples perspectivas: la ingeniería de control, la inteligencia artificial y la estadística entre otros. Dentro de cada una de ellas con multitud de técnicas: sistemas expertos, redes neuronales, razonamiento basado en casos, análisis de señales, observadores, ecuaciones de redundancia analítica, diagnóstico basado en consistencia, pero considerando la presencia de fallos únicos y no múltiples. En esta tesis se estudiará la detección y el diagnóstico de fallos múltiples en sistemas dinámicos utilizando análisis de componentes no lineal para la detección y los residuos estructurados para el diagnóstico. El estudio incluye el desarrollo de un modelo matemático de una mini turbina de gas SR-30 y en la implementación de algoritmos de detección y diagnóstico de fallos múltiples en dicho sistema. La detección y el diagnóstico de fallos múltiples ha resultado ser más complejo que la detección y el diagnóstico de fallos únicos. Esto no es sólo porque aumenta el número de fallos, sino también debido a la aparición de nuevos fenómenos que deben ser considerados, tales como una combinación o la influencia mutua de los fallos, la compensación, y la explosión combinatoria de posibles escenarios de fallos. El problema de fallos múltiples es importante, ya que el supuesto de un solo fallo puede llevar a diagnósticos incorrectos cuando fallos múltiples ocurren

A Neural Network Approach to Aircraft Performance Model Forecasting

Performance models used in the aircraft development process are dependent on the assumptions and approximations associated with the engineering equations used to produce them. The design and implementation of these highly complex engineering models are typically associated with a longer development process. This study proposes a non-deterministic approach where machine learning techniques using Artificial Neural Networks are used to predict specific aircraft parameters using available data. The approach yields results that are independent of the equations used in conventional aircraft performance modeling methods and rely on stochastic data and its distribution to extract useful patterns. To test the viability of the approach, a case study is performed comparing a conventional performance model describing the takeoff ground roll distance with the values generated from a neural network using readily-available flight data. The neural network receives as input, and is trained using, aircraft performance parameters including atmospheric conditions (air temperature, air pressure, air density), performance characteristics (flap configuration, thrust setting, MTOW, etc.) and runway conditions (wet, dry, slope angle, etc.). The proposed predictive modeling approach can be tailored for use with a wider range of flight mission profiles such as climb, cruise, descent and landing

Degradation Prognostics in Gas Turbine Engines Using Neural Networks

In complex systems such as aircraft engines, system reliability and adequate monitoring is of high priority. The performance of all physical systems degrades over time due to aging, the working and environmental conditions. Considering both time and safety, it is important to predict the system health condition in future in order to be able to assign a suitable maintenance policy. Towards this end, two artificial intelligence based methodologies are proposed and investigated in this thesis. The main objective is to predict degradation trends by studying their effects on the engine measurable parameters such as the temperature and pressure at critical points of a gas turbine engine. The first proposed prognostic scheme for the gas turbine engine is based on a recurrent neural networks (RNN) architecture. This closed-loop architecture enables the network to learn the increasing degradation dynamics using the collected data set. Training the neural networks and determining the suitable number of network parameters are challenging tasks. The other challenge associated with the prognostic problem is the uncertainty management. This is inherent in such schemes due to measurement noise and the fact that one is trying to project forward in time. To overcome this problem, upper and lower prediction bounds are defined and obtained in this thesis. The two bounds constitute a prediction band which helps one not to merely depend on the single point prediction. The prediction bands along with the prediction error statistical measures, allow one to decide on the goodness of the prediction results. The second prognostic scheme is based on a nonlinear autoregressive with exogenous input (NARX) neural networks architecture. This recurrent dynamical structure takes advantage of both features which makes it easy to manage the main objective. The network is trained with fewer examples and the prediction errors are lower as compared to the first architecture. The statistical error measures and the prediction bands are obtained for this architecture as well. In order to evaluate and compare the prediction results from the two proposed neural networks a metric known as the normalized Akaike information criterion (NAIC) is applied in this thesis. This metric takes into account the prediction error, the number of parameters used in the neural networks architecture and the number of samples in the test data set. A smaller NAIC value shows a better, more accurate and more effective prediction result. The NAIC values are found for each case and the networks are compared at the end of the thesis. Neural networks performance is based on the suitability of the data they are provided with. Two main causes of engine degradation are modelled in this thesis and a SIMULINK model is developed. Various scenarios and case studies are presented to illustrate and demonstrate the effectiveness of our proposed neural networks based prognostic approaches. The prognostic results can be employed for the engine health management purposes. This is a growing and an active area of research for the aircraft engines where only a few references exist in the literature

Anomaly detection in aircraft gas turbine engines

PERFORMANCE, reliability, and operational flexibility are critical requirements of complex mechanical systems, such as aircraft propulsion, and these requirements may vary with different mission perspectives during the service life. In spite of meticulous engineering design, complex systems do eventually degrade and often fail to yield the anticipated performance during the later phases of their operational life. Recently, much attention has been devoted t

Symbolic identification for anomaly detection in aircraft gas turbine engines

Abstract—This paper presents a robust and computationally inexpensive technique of fault detection in aircraft gas-turbine engines, based on a recently developed statistical pattern recog-nition tool. The method involves abstraction of a qualitative description from a general dynamical system structure, using state space embedding of the output data-stream and dis-cretization of the resultant pseudo state and input spaces. The system identification is achieved through grammatical inference techniques, and the deviation of the plant output from the nominal estimated language gives a metric for fault detection. The algorithm is validated on a numerical simulation test-bed that is built upon the NASA C-MAPSS model of a generic commercial aircraft engine