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

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

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

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

A Review on Expert System Applications in Power Plants

The control and monitoring of power generation plants is being complicated day by day, with the increase size and capacity of equipments involved in power generation process. This calls for the presence of experienced and well trained operators for decision making and management of various plant related activities. Scarcity of well trained and experienced plant operators is one of the major problems faced by modern power industry. Application of artificial intelligence techniques, especially expert systems whose main characteristics is to simulate expert plant operator’s actions is one of the actively researched areas in the field of plant automation. This paper presents an overview of various expert system applications in power generation plants. It points out technological advancement of expert system technology and its integration with various types of modern techniques such as fuzzy, neural network, machine vision and data acquisition systems. Expert system can significantly reduce the work load on plant operators and experts, and act as an expert for plant fault diagnosis and maintenance. Various other applications include data processing, alarm reduction, schedule optimisation, operator training and evaluation. The review point out that integration of modern techniques such as neural network, fuzzy, machine vision, data base, simulators etc. with conventional rule based methodologies have added greater dimensions to problem solving capabilities of an expert system.DOI:http://dx.doi.org/10.11591/ijece.v4i1.502

The use of a fuzzy logic approach for integration of vibration-based diagnostic features of rolling element bearings

Modern condition monitoring systems (CMS) collect and process enormous amount of data in order to provide the earliest and most dependable information of fault development within any of the machine components and their operation combined. According to numerous studies one of the most fault susceptible mechanical elements in rotating machinery are rolling element bearings. Although reliable techniques for their diagnostics are already proposed, the new investigation is needed. According to authors experience in many industrial applications the operators are obligated to simultaneously track hundreds of diagnostic estimates, such as signals energy, its peakedness or narrowband characteristics for localized faults. As mentioned, for a vibration-based CMS of single wind turbine there are nearly 150 of them. Therefore, the authors employ a fuzzy logic approach for integration of bearing diagnostic features. A new estimate that carry most relevant information about bearing condition is discussed. The reasoning is presented on simulated data that mimics real rotating machine

Machine-learning-based condition assessment of gas turbine: a review

Condition monitoring, diagnostics, and prognostics are key factors in today’s competitive industrial sector. Equipment digitalisation has increased the amount of available data throughout the industrial process, and the development of new and more advanced techniques has significantly improved the performance of industrial machines. This publication focuses on surveying the last decade of evolution of condition monitoring, diagnostic, and prognostic techniques using machinelearning (ML)-based models for the improvement of the operational performance of gas turbines. A comprehensive review of the literature led to a performance assessment of ML models and their applications to gas turbines, as well as a discussion of the major challenges and opportunities for the research on these kind of engines. This paper further concludes that the combination of the available information captured through the collectors and the ML techniques shows promising results in increasing the accuracy, robustness, precision, and generalisation of industrial gas turbine equipment.This research was funded by Siemens Energy.Peer ReviewedPostprint (published version

A Framework for Prognostics Reasoning

The use of system data to make predictions about the future system state commonly known as prognostics is a rapidly developing field. Prognostics seeks to build on current diagnostic equipment capabilities for its predictive capability. Many military systems including the Joint Strike Fighter (JSF) are planning to include on-board prognostics systems to enhance system supportability and affordability. Current research efforts supporting these developments tend to focus on developing a prognostic tool for one specific system component. This dissertation research presents a comprehensive literature review of these developing research efforts. It also develops presents a mathematical model for the optimum allocation of prognostics sensors and their associated classifiers on a given system and all of its components. The model assumptions about system criticality are consistent with current industrial philosophies. This research also develops methodologies for combine sensor classifiers to allow for the selection of the best sensor ensemble

Development of Diagnostic Program for Gas Compressor using Knowledge Based Management Concept

Compressor maintenance is vital in oil and gas industry because it is an important equipment that runs continuously. Among all of the deterioration mechanisms, fouling is found to be the most common in oil and gas industry and it is relatively easier to be analyzed. Currently, plant engineers face difficulties in predicting the appropriate time for maintenance and usually they will follow the original equipment manufacturer (OEM) recommendations. Most plant engineers do not have a predictive tool to advise them on compressor maintenance and the necessary steps to be taken. Usually, the engineers will only attend to the equipment when problems or abnormalities arise from it, apart from the planned maintenance. Late decision made on compressor maintenance will sometimes cause problems to operation either due to late arrival of spare parts or staff availability. The objective of this project is to develop a software that will be able to assist engineers in determining the performance deterioration of gas compressor and deciding the optimum time to do maintenance. The maintenance history data is collected and analysed by the software regularly. The correlations between isentropic efficiency, isentropic head, and gas power and the compressor deterioration are studied based on two centrifugal gas compressors from January 2009 to December 2010. Later, a software that is able to produce maintenance advice based on the input parameters given by the user is created. The software is developed using Microsoft Excel 2010 and Microsoft Visual Basic. From the analysis conducted, it is found that due to fouling, isentropic efficiency and isentropic head decrease with time for low pressure compressors. In contrast, the gas power increases with time. Based on these findings, Performance Indicators Monitoring Program (PIMP) is developed

Knowledge Based Management For Rotating Equipment Diagnostics

Knowledge based management system for rotating equipment diagnostics is a expert system to help the maintenance engineers in the power plants or gas plants using gas turbines for power generation. Industrial gas turbine (Rolls - Royce Allison 510 - KB7) is used as the emphasis of the project to develop the application. Case based Reasoning and Spiral Life Cycle model are used as the methodology in this project for the methods can support and fulfil the objectives of the project. Microsoft Access and Java runtime are used for the database set up and system development respectively. The final system offers eight different scenarios for gas turbine diagnostics. Reference tables and Scenario note function The system is effective and less time consuming, platform( operating system) independent, easy to use and should be helpful for the maintenance engineers. Diagnostics for the auxiliary system of the gas turbine should be incorporated in the system to have a more complete system. MySQL database system should be used in the future development if the database is to expand