A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Diagnosis methodology based on deep feature learning for fault identification in metallic, hybrid and ceramic bearings

Scientific and technological advances in the field of rotatory electrical machinery are leading to an increased efficiency in those processes and systems in which they are involved. In addition, the consideration of advanced materials, such as hybrid or ceramic bearings, are of high interest towards high-performance rotary electromechanical actuators. Therefore, most of the diagnosis approaches for bearing fault detection are highly dependent of the bearing technology, commonly focused on the metallic bearings. Although the mechanical principles remain as the basis to analyze the characteristic patterns and effects related to the fault appearance, the quantitative response of the vibration pattern considering different bearing technology varies. In this regard, in this work a novel data-driven diagnosis methodology is proposed based on deep feature learning applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology consists of three main stages: first, a deep learning-based model, supported by stacked autoencoder structures, is designed with the ability of self-adapting to the extraction of characteristic fault-related features from different signals that are processed in different domains. Second, in a feature fusion stage, information from different domains is integrated to increase the posterior discrimination capabilities during the condition assessment. Third, the bearing assessment is achieved by a simple softmax layer to compute the final classification results. The achieved results show that the proposed diagnosis methodology based on deep feature learning can be effectively applied to the diagnosis and identification of bearing faults for different bearing technologies, such as metallic, hybrid and ceramic bearings, in electromechanical systems. The proposed methodology is validated in front of two different electromechanical systems and the obtained results validate the adaptability and performance of the proposed approach to be considered as a part of the condition-monitoring strategies where different bearing technologies are involved.Peer ReviewedPostprint (published version

Neural nonlinear autoregressive model with exogenous input (Narx) for turboshaft aeroengine fuel control unit model†

none3noOne of the most important parts of a turboshaft engine, which has a direct impact on the performance of the engine and, as a result, on the performance of the propulsion system, is the engine fuel control system. The traditional engine control system is a sensor-based control method, which uses measurable parameters to control engine performance. In this context, engine component degradation leads to a change in the relationship between the measurable parameters and the engine performance parameters, and thus an increase of control errors. In this work, a nonlinear model predictive control method for turboshaft direct fuel control is implemented to improve engine response ability also in presence of degraded conditions. The control objective of the proposed model is the prediction of the specific fuel consumption directly instead of the measurable parameters. In this way is possible decentralize controller functions and realize an intelligent engine with the development of a distributed control system. Artificial Neural Networks (ANN) are widely used as data-driven models for modelling of complex systems such as aeroengine performance. In this paper, two Nonlinear Autoregressive Neural Networks have been trained to predict the specific fuel consumption for several transient flight maneuvers. The data used for the ANN predictions have been estimated through the Gas Turbine Simulation Program. In particular the first ANN predicts the state variables based on flight conditions and the second one predicts the performance parameter based on the previous predicted variables. The results show a good approximation of the studied variables also in degraded conditions.openDe Giorgi M.G.; Strafella L.; Ficarella A.De Giorgi, M. G.; Strafella, L.; Ficarella, A

Engine health monitoring with fuzzy data: lessons learned from aircraft industry

Conferencia de la Asociación Española para la Inteligencia Artificial (18th, 2018, Granada

Exploring Prognostic and Diagnostic Techniques for Jet Engine Health Monitoring: A Review of Degradation Mechanisms and Advanced Prediction Strategies

Maintenance is crucial for aircraft engines because of the demanding conditions to which they are exposed during operation. A proper maintenance plan is essential for ensuring safe flights and prolonging the life of the engines. It also plays a major role in managing costs for aeronautical companies. Various forms of degradation can affect different engine components. To optimize cost management, modern maintenance plans utilize diagnostic and prognostic techniques, such as Engine Health Monitoring (EHM), which assesses the health of the engine based on monitored parameters. In recent years, various EHM systems have been developed utilizing computational techniques. These algorithms are often enhanced by utilizing data reduction and noise filtering tools, which help to minimize computational time and efforts, and to improve performance by reducing noise from sensor data. This paper discusses the various mechanisms that lead to the degradation of aircraft engine components and the impact on engine performance. Additionally, it provides an overview of the most commonly used data reduction and diagnostic and prognostic techniques

Удосконалення опор роторів двоконтурних турбореактивних двигунів, що використовуються в силових установках дальньомагістральних літаків

Робота публікується згідно наказу Ректора НАУ від 27.05.2021 р. №311/од «Про розміщення кваліфікаційних робіт здобувачів вищої освіти в репозиторії університету» . Керівник проекту: доцент, к. т. н. Хімко Андрій Миколайович.Projected engine is based on the engine GEnx prototype，The General Electric GEnx ("General Electric Next-generation") is an advanced dual rotor, axial flow, highbypass turbofan jet engine in production by GE Aviation for the Boeing 787 and 747- 8.The double -circuit turbojet engine (TRDD) has a design that allows to move additional masses of air that passes through the outer circuit of the engine. This design provides the higher efficiency than the usual TRD. The first concept of TRDD in aircraft engineering was proposed by the Ukrainian designer of the aircraft engines Архип Люлька . On the basis of the experiments conducted since 1937, Архип Люлька applied for the invention of two -circuit TRD . But critical assessment of the situation reveals that The main advantage of this engine is their high efficiency. Disadvantages -Large weight and size. Especially the large diameter of the fan, which leads to a significant air resistance in the flight. The scope of such engines is a mid -length commercial airliner and military transportation aviation. So engine necessitates changes in order to improve previously mentioned qualities such as: thrust, efficiency, fuel consumption, durability, weight, and size.Проектований двигун базується на прототипі двигуна GEnx, General Electric GEnx ("Дженерал Електрик наступного покоління") - це передовий подвійний ротор, осьовий потік, високий байпас турбовентиляторний реактивний двигун у виробництві GE Aviation для Boeing 787 і 747- 8. Двоконтурний турбореактивний двигун (ТРДД) має конструкцію, яка дозволяє йому рухатися додаткові маси повітря, які проходять через зовнішній контур двигуна. Цей дизайн забезпечує більш високу ефективність, ніж звичайний ТРД. Перша концепція TRDD в авіаційну техніку запропонував український конструктор авіаційних двигунів Архип Люлька. На основі проведених з 1937 р. дослідів архіп Люлька претендувала на винахід двоконтурного ТРД. Але критична оцінка ситуації виявляє, що головна перевага цього двигуна полягає в їх високому ККД. Недоліки - велика вага і розміри. Особливо великий діаметр вентилятора, що призводить до значного опору повітря в польоті. The Сфера застосування таких двигунів - комерційні авіалайнери середньої довжини і військово-транспортні авіації. Отже, двигун потребує змін, щоб покращити вищезгадане такі якості, як: тяга, ефективність, споживання палива, довговічність, вага та розмір

Case Studies In Turbomachinery Operation And Maintenance Using Condition Monitoring.

LecturePg. 101-112With exceedingly high downtime costs and the need for efficient operation of turbo machinery, integrated condition monitoring, wherein a number of health parameters are analyzed, is becoming increasingly popular in process plants and in utilities. Most operational problems can be diagnosed by developing a correlation among several key operating parameters. A wide range of condition monitoring approaches are available and this paper shows how several approaches can be used in conjunction with one another to solve operational problems. Several case studies pertaining to gas and steam turbines and compressors are presented. A matrix of condition monitoring techniques is provided and case studies are presented. Finally, future trends in the area of condition monitoring are presented

Gas Turbines

This book is intended to provide valuable information for the analysis and design of various gas turbine engines for different applications. The target audience for this book is design, maintenance, materials, aerospace and mechanical engineers. The design and maintenance engineers in the gas turbine and aircraft industry will benefit immensely from the integration and system discussions in the book. The chapters are of high relevance and interest to manufacturers, researchers and academicians as well