1,494 research outputs found

    Neural Networks for Gas Turbine Diagnosis

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    The present chapter addresses the problems of gas turbine gas path diagnostics solved using artificial neural networks. As a very complex and expensive mechanical system, a gas turbine should be effectively monitored and diagnosed. Being universal and powerful approximation and classification techniques, neural networks have become widespread in gas turbine health monitoring over the past few years. Applications of such networks as a multilayer perceptron, radial basis network, probabilistic neural network, and support vector network were reported. However, there is a lack of manuals that summarize neural network applications for gas turbine diagnosis

    Engine Condition Monitoring and Diagnostics

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    Gas Turbine Diagnostics

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    Vehicle level health assessment through integrated operational scalable prognostic reasoners

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    Today’s aircraft are very complex in design and need constant monitoring of the systems to establish the overall health status. Integrated Vehicle Health Management (IVHM) is a major component in a new future asset management paradigm where a conscious effort is made to shift asset maintenance from a scheduled based approach to a more proactive and predictive approach. Its goal is to maximize asset operational availability while minimising downtime and the logistics footprint through monitoring deterioration of component conditions. IVHM involves data processing which comprehensively consists of capturing data related to assets, monitoring parameters, assessing current or future health conditions through prognostics and diagnostics engine and providing recommended maintenance actions. The data driven prognostics methods usually use a large amount of data to learn the degradation pattern (nominal model) and predict the future health. Usually the data which is run-to-failure used are accelerated data produced in lab environments, which is hardly the case in real life. Therefore, the nominal model is far from the present condition of the vehicle, hence the predictions will not be very accurate. The prediction model will try to follow the nominal models which mean more errors in the prediction, this is a major drawback of the data driven techniques. This research primarily presents the two novel techniques of adaptive data driven prognostics to capture the vehicle operational scalability degradation. Secondary the degradation information has been used as a Health index and in the Vehicle Level Reasoning System (VLRS). Novel VLRS are also presented in this research study. The research described here proposes a condition adaptive prognostics reasoning along with VLRS

    Application of the discrete wavelet transform and probabilistic neural networks in IC engine fault diagnostics

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    Around the world are continued attempts to use the vibroacoustic phenomena for purposes of diagnosis of machine condition. Particularly important becomes non-invasive methods including methods based on vibration and acoustic signals. Vibroacoustic phenomena, which relates to the working of technical objects, includes all necessary information connected with the technical condition. The biggest difficulty is the transformation of registered vibroacoustic signals and creation on their basic measures, which will be non-sensitive to any type of interference occurring during the operation of objects and recording signals. To the group of technical objects, for which are already conducted numerous studies all over the world, connected with use of vibroacoustic phenomena for diagnostic purposes which relates to the automotive drive systems, including combustion engines. Combustion engines during its working generate a whole range of vibroacoustic phenomena bringing information on the proper operation of the engine, as well as on condition of each of its elements. In a combustion engine, there are many sources of vibroacoustic phenomena, which contributes to the disruption of diagnostic information. The development of appropriate methods for vibroacoustic signal processing and complete diagnostic systems may allow future extension of the on-board diagnostics OBD system – currently in used cars. The most interesting would be the development of complex system for diagnosing the condition of the individual elements of the car engine operating by basing on information from vibroacoustic signals. In this article are shown results of research, which aim is to diagnose damages of mechanical elements of car combustion engine using vibration signals and artificial neural networks

    Review of Machine Learning Approaches In Fault Diagnosis applied to IoT System

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    International audienceWith increasing complex systems, low production costs, and changing technologies, for this reason, the automatic fault diagnosis using artificial intelligence (AI) techniques is more in more applied. In addition, with the emergence of the use of reconfigurable systems, AI can assist in self-maintenance of complex systems. The purpose of this article is to summarize the diagnosis research of systems using AI approaches and examine their application particularly in the field of diagnosis of complex systems. It covers articles published from 2002 to 2018 using Machine Learning tools for fault diagnosis in industrial systems

    Methods of Technical Prognostics Applicable to Embedded Systems

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    Hlavní cílem dizertace je poskytnutí uceleného pohledu na problematiku technické prognostiky, která nachází uplatnění v tzv. prediktivní údržbě založené na trvalém monitorování zařízení a odhadu úrovně degradace systému či jeho zbývající životnosti a to zejména v oblasti komplexních zařízení a strojů. V současnosti je technická diagnostika poměrně dobře zmapovaná a reálně nasazená na rozdíl od technické prognostiky, která je stále rozvíjejícím se oborem, který ovšem postrádá větší množství reálných aplikaci a navíc ne všechny metody jsou dostatečně přesné a aplikovatelné pro embedded systémy. Dizertační práce přináší přehled základních metod použitelných pro účely predikce zbývající užitné životnosti, jsou zde popsány metriky pomocí, kterých je možné jednotlivé přístupy porovnávat ať už z pohledu přesnosti, ale také i z pohledu výpočetní náročnosti. Jedno z dizertačních jader tvoří doporučení a postup pro výběr vhodné prognostické metody s ohledem na prognostická kritéria. Dalším dizertačním jádrem je představení tzv. částicového filtrovaní (particle filtering) vhodné pro model-based prognostiku s ověřením jejich implementace a porovnáním. Hlavní dizertační jádro reprezentuje případovou studii pro velmi aktuální téma prognostiky Li-Ion baterii s ohledem na trvalé monitorování. Případová studie demonstruje proces prognostiky založené na modelu a srovnává možné přístupy jednak pro odhad doby před vybitím baterie, ale také sleduje možné vlivy na degradaci baterie. Součástí práce je základní ověření modelu Li-Ion baterie a návrh prognostického procesu.The main aim of the thesis is to provide a comprehensive overview of technical prognosis, which is applied in the condition based maintenance, based on continuous device monitoring and remaining useful life estimation, especially in the field of complex equipment and machinery. Nowadays technical prognosis is still evolving discipline with limited number of real applications and is not so well developed as technical diagnostics, which is fairly well mapped and deployed in real systems. Thesis provides an overview of basic methods applicable for prediction of remaining useful life, metrics, which can help to compare the different approaches both in terms of accuracy and in terms of computational/deployment cost. One of the research cores consists of recommendations and guide for selecting the appropriate forecasting method with regard to the prognostic criteria. Second thesis research core provides description and applicability of particle filtering framework suitable for model-based forecasting. Verification of their implementation and comparison is provided. The main research topic of the thesis provides a case study for a very actual Li-Ion battery health monitoring and prognostics with respect to continuous monitoring. The case study demonstrates the prognostic process based on the model and compares the possible approaches for estimating both the runtime and capacity fade. Proposed methodology is verified on real measured data.

    Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

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    A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine

    Diagnostics of power setting sensor fault of gas turbine engines using genetic algorithm

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    Gas path diagnostics is one of the most effective condition monitoring techniques in supporting condition-based maintenance of gas turbines and improving availability and reducing maintenance costs of the engines. The techniques can be applied to the health monitoring of different gas path components and also gas path measurement sensors. One of the most important measurement sensors is that for the engine control, also called the power setting sensor, which is used by the engine control system to control the operation of gas turbine engines. In most of the published research so far, it is rarely mentioned that faults in such sensors have been tackled in either engine control or condition monitoring. The reality is that if such a sensor degrades and has a noticeable bias, it will result in a shift in engine operating condition and misleading diagnostic results. In this paper, the phenomenon of a power-setting sensor fault has been discussed and a gas path diagnostic method based on a Genetic Algorithm (GA) has been proposed for the detection of power-setting sensor fault with and without the existence of engine component degradation and other gas path sensor faults. The developed method has been applied to the diagnostic analysis of a model aero turbofan engine in several case studies. The results show that the GA-based diagnostic method is able to detect and quantify the power-setting sensor fault effectively with the existence of single engine component degradation and single gas path sensor fault. An exceptional situation is that the power-setting sensor fault may not be distinguished from a component fault if both faults have the same fault signature. In addition, the measurement noise has small impact on prediction accuracy. As the GA-based method is computationally slow, it is only recommended for off-line applications. The introduced GA-based diagnostic method is generic so it can be applied to different gas turbine engines. This paper will be presented at the ISABE 2017 Conference, 5-8 September 2017, Manchester, UK

    A review of model based and data driven methods targeting hardware systems diagnostics

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    System health diagnosis serves as an underpinning enabler for enhanced safety and optimized maintenance tasks in complex assets. In the past four decades, a wide-range of diagnostic methods have been proposed, focusing either on system or component level. Currently, one of the most quickly emerging concepts within the diagnostic community is system level diagnostics. This approach targets in accurately detecting faults and suggesting to the maintainers a component to be replaced in order to restore the system to a healthy state. System level diagnostics is of great value to complex systems whose downtime due to faults is expensive. This paper aims to provide a comprehensive review of the most recent diagnostics approaches applied to hardware systems. The main objective of this paper is to introduce the concept of system level diagnostics and review and evaluate the collated approaches. In order to achieve this, a comprehensive review of the most recent diagnostic methods implemented for hardware systems or components is conducted, highlighting merits and shortfalls
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