Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review

With the privatization and intense competition that characterize the volatile energy sector, the gas turbine industry currently faces new challenges of increasing operational flexibility, reducing operating costs, improving reliability and availability while mitigating the environmental impact. In this complex, changing sector, the gas turbine community could address a set of these challenges by further development of high fidelity, more accurate and computationally efficient engine health assessment, diagnostic and prognostic systems. Recent studies have shown that engine gas-path performance monitoring still remains the cornerstone for making informed decisions in operation and maintenance of gas turbines. This paper offers a systematic review of recently developed engine performance monitoring, diagnostic and prognostic techniques. The inception of performance monitoring and its evolution over time, techniques used to establish a high-quality dataset using engine model performance adaptation, and effects of computationally intelligent techniques on promoting the implementation of engine fault diagnosis are reviewed. Moreover, recent developments in prognostics techniques designed to enhance the maintenance decision-making scheme and main causes of gas turbine performance deterioration are discussed to facilitate the fault identification module. The article aims to organize, evaluate and identify patterns and trends in the literature as well as recognize research gaps and recommend new research areas in the field of gas turbine performance-based monitoring. The presented insightful concepts provide experts, students or novice researchers and decision-makers working in the area of gas turbine engines with the state of the art for performance-based condition monitoring

Derivative-driven window-based regression method for gas turbine performance prognostics

The domination of gas turbines in the energy arena is facing many challenges from environmental regulations and the plethora of renewable energy sources. The gas turbine has to operate under demand-driven modes and its components consume their useful life faster than the engines of the base-load operation era. As a result the diagnostics and prognostics tools should be further developed to cope with the above operation modes and improve the condition based maintenance (CBM). In this study, we present a derivative-driven diagnostic pattern analysis method for estimating the performance of gas turbines under dynamic conditions. A real time model-based tuner is implemented through a dynamic engine model built in Matlab/Simulink for diagnostics. The nonlinear diagnostic pattern is then partitioned into data-windows. These are the outcome of a data analysis based on the second order derivative which corresponds to the acceleration of degradation. Linear regression is implemented to locally fit the detected deviations and predict the engine behavior. The accuracy of the proposed method is assessed through comparison between the predicted and actual degradation by the remaining useful life (RUL) metric. The results demonstrate and illustrate an improved accuracy of our proposed methodology for prognostics of gas turbines under dynamic modes. © 2017 Elsevier Lt

Vehicle level health assessment through integrated operational scalable prognostic reasoners

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

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

Methods of Technical Prognostics Applicable to Embedded Systems

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.

A review of physics-based models in prognostics: application to gears and bearings of rotating machinery

Health condition monitoring for rotating machinery has been developed for many years due to its potential to reduce the cost of the maintenance operations and increase availability. Covering aspects include sensors, signal processing, health assessment and decision-making. This article focuses on prognostics based on physics-based models. While the majority of the research in health condition monitoring focuses on data-driven techniques, physics-based techniques are particularly important if accuracy is a critical factor and testing is restricted. Moreover, the benefits of both approaches can be combined when data-driven and physics-based techniques are integrated. This article reviews the concept of physics-based models for prognostics. An overview of common failure modes of rotating machinery is provided along with the most relevant degradation mechanisms. The models available to represent these degradation mechanisms and their application for prognostics are discussed. Models that have not been applied to health condition monitoring, for example, wear due to metal–metal contact in hydrodynamic bearings, are also included due to its potential for health condition monitoring. The main contribution of this article is the identification of potential physics-based models for prognostics in rotating machinery

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Aeronautical Engineering: A special bibliography with indexes, supplement 64, December 1975

This bibliography lists 288 reports, articles, and other documents introduced into the NASA scientific and technical information system in November 1975

Prognostic Algorithms for Condition Monitoring and Remaining Useful Life Estimation

To enable the benets of a truly condition-based maintenance philosophy to be realised, robust, accurate and reliable algorithms, which provide maintenance personnel with the necessary information to make informed maintenance decisions, will be key. This thesis focuses on the development of such algorithms, with a focus on semiconductor manufacturing and wind turbines. An introduction to condition-based maintenance is presented which reviews dierent types of maintenance philosophies and describes the potential benets which a condition- based maintenance philosophy will deliver to operators of critical plant and machinery. The issues and challenges involved in developing condition-based maintenance solutions are discussed and a review of previous approaches and techniques in fault diagnostics and prognostics is presented. The development of a condition monitoring system for dry vacuum pumps used in semi- conductor manufacturing is presented. A notable feature is that upstream process mea- surements from the wafer processing chamber were incorporated in the development of a solution. In general, semiconductor manufacturers do not make such information avail- able and this study identies the benets of information sharing in the development of condition monitoring solutions, within the semiconductor manufacturing domain. The developed solution provides maintenance personnel with the ability to identify, quantify, track and predict the remaining useful life of pumps suering from degradation caused by pumping large volumes of corrosive uorine gas. A comprehensive condition monitoring solution for thermal abatement systems is also presented. As part of this work, a multiple model particle ltering algorithm for prog- nostics is developed and tested. The capabilities of the proposed prognostic solution for addressing the uncertainty challenges in predicting the remaining useful life of abatement systems, subject to uncertain future operating loads and conditions, is demonstrated. Finally, a condition monitoring algorithm for the main bearing on large utility scale wind turbines is developed. The developed solution exploits data collected by onboard supervisory control and data acquisition (SCADA) systems in wind turbines. As a result, the developed solution can be integrated into existing monitoring systems, at no additional cost. The potential for the application of multiple model particle ltering algorithm to wind turbine prognostics is also demonstrated