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

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

HYBRID EARLY WARNING SYSTEMS

ABSTRACT New tools are needed to reach high goals for uptime and availability in industrial processes. Early warning of developing faults is one part of the strategy to reach these goals. A single method rarely meets all requirements, but combining methods and techniques in a hybrid system offers advantages and can overcome limitations in the individual approaches. Methods considered are physical models, artificial neural networks, and case-based reasoning. The paper discusses the pros and cons, strengths and weaknesses of the three methods and three combinations of hybrid solutions in order to assist in select a suitable combination for a specific early warning challenge ahead

Digitalisaation hyödyt höyryturbiinien käyttöomaisuuden hallinnassa

Steam turbines are considered long-lived and require little attention during normal operation. Cost optimizations due to infrequent demand for turbine expertise, together with retiring workforce, have resulted in increasing shortage of know-how. Digitalization could substitute unavailable turbine resources, but the projects and investment have been challenging to initiate and incentivize. The objective of this thesis was to map the benefits of digitalized steam turbine asset management, what kind of challenges digitalization could mitigate and how the implementation could be facilitated. The research confirmed that turbine operating companies lack the domain know-how and resources required for some current systems and demands. Prolonging of overhauls and deficiencies in asset management, such as insufficient documenting and data utilization, were observed to be other main challenges. Increased downtime and unoptimized practices and systems reduce efficiency, usability, reliability and availability. Advanced diagnostics in condition monitoring systems could increase availability and reliability by enabling optimized condition-based maintenance and facilitate shorter overhauls by reducing unforeseen findings. Solutions and service that allow faster fact-finding in anomalies would increase availability as well. Asset management systems with more connectivity, centralization, user-friendliness and AI would reduce downtime by enhancing planning, documenting and spare part management. Such systems could also increase usability and the overall efficiency of operations and maintenance. Main hindrances for digitalization are the imbalance between costs and perceived added value, and insufficient focus on the usability of asset management systems. Development of advanced solutions in current business models is disincentivized. Long-term contracts could enable the implementation of best practices, reduce risks and incentivize higher quality of services. Partnership business models facilitate mutual benefits better than short-term and stand-alone services.Höyryturbiinit ovat yleensä pitkäikäisiä ja vaativat vain vähän huomiota normaalin käynnin aikana. Huollon ja turbiiniosaamisen harvan tarpeen takia höyryturbiinien käyttö- ja hallintakustannuksista on jatkuvasti säästetty. Tämä, yhdessä eläköityvän työvoiman kanssa, on johtanut krooniseen tietotaidon puutteeseen höyryturbiinilaitoksilla. Digitalisaatiolla voisi korvata puuttuvia resursseja, mutta projektien ja investointien kanssa on ollut ongelmia. Tämän diplomityön tarkoituksena oli kartoittaa höyryturbiinien digitaalisen käyttöomaisuuden hallinnan hyötyjä, millaisiin haasteisiin se voisi vastata, ja mitä digitalisaation hyötyjen menestyksekäs implementointi vaatisi. Tutkimus varmisti, että loppukäyttäjillä on puutetta turbiiniosaamisesta ja -resursseista, joita tämänhetkiset systeemit ja tarpeet vaatisivat. Muita suuria haasteita olivat huoltojen pitkittymiset ja puutteet käyttöomaisuuden hallinnassa, kuten riittämätön dokumentointi ja mitatun datan hyödyntäminen. Pitkittyvät huollot ja optimoimattomat toiminnot kasvattavat epäkäytettävyysaikaa ja pienentävät tehokkuutta, käytön helppoutta ja luotettavuutta. Kehittyneen turbiinidiagnostiikan hyödyntäminen kunnonvalvonnassa voisi kasvattaa käytettävyyttä ja luotettavuutta mahdollistamalla turbiinin todelliseen huoltotarpeeseen perustuvan huollon. Ennakoivalla analytiikalla voitaisiin vähentää odottamattomia löydöksiä, jotka ovat yksi yleisin syy huoltojen pitkittymiseen. Käytettävyyttä lisäisivät myös ratkaisut ja palvelut, joilla nopeutettaisiin ongelmanratkaisua häiriötilanteissa. Käyttö- ja kunnossapitojärjestelmien parempi liitettävyys, keskitettävyys ja käyttäjäystävällisyys sekä tekoälyn hyödyntäminen tehostaisivat käyttöomaisuuden, kuten varaosien, hallintaa ja helpottaisivat suunnittelua ja dokumentointia. Merkittävimpiä esteitä turbiinien käyttöomaisuuden hallinnan digitalisaatiolle ovat kustannusten ja hahmotetun lisäarvon epätasapaino sekä riittämätön huomio käyttöomaisuuden hallinnan optimointiin. Edistyksellisten digitaalisten ratkaisujen kehittämiselle ja loppuasiakkaalle tarjoamiseen ei ole riittävästi kannustimia. Pitkäaikaiset ylläpitosopimukset voisivat mahdollistaa parhaiden käytäntöjen implementoinnin, vähentää liiketoimien riskiä ja tehdä korkeimmankin laadun palveluista ja ratkaisuista kannattavampia. Pitkäaikaiseen kumppanuuteen perustuvat liiketoimintamallit fasilitoivat osapuolten yhteisiä etuja paremmin, kuin lyhytaikaiset erillissopimukset yksittäisille ratkaisuille ja palveluille

Condition Monitoring of Gas Turbines using Acoustic Emissions

Acoustic emission (AE) technology has recently found its way in condition monitoring of rotary equipment due to its advantage of earlier detection of defects and anoma- lies in comparison to vibration analysis. However, there has been very little industrial application of AE signals for condition monitoring of safety-critical equipment if any, partly due to the diffculty in processing, interpreting and classifying the acquired data in a highly reliable fashion. The motivation in this thesis was to develop a methodol- ogy for inferring health related information in a gas turbine without intruding the engine. Our work has targeted a broad class of rotary equipment known as cyclostationary processes, therefore, instead of analyzing particular AE samples of gas turbines we have tried to build a mathematical framework that would suit any arbitrary machine comply- ing certain conditions. The result of our work mainly encompasses a feature extraction technique that eliminates the random e↵ects associated with a gas turbine AE signal, and a hypothetical testing method for classification of AE signals with any desirable level of certainty, subject to a set of assumptions and conditions. We have validated our methodologies and derivations using actual real-life gas turbine AE signals, and compared our solutions with some of the techniques published in the literature

Knowledge Extraction and Summarization for Textual Case-Based Reasoning: A Probabilistic Task Content Modeling Approach

Case-Based Reasoning (CBR) is an Artificial Intelligence (AI) technique that has been successfully used for building knowledge systems for tasks/domains where different knowledge sources are easily available, particularly in the form of problem solving situations, known as cases. Cases generally display a clear distinction between different components of problem solving, for instance, components of the problem description and of the problem solution. Thus, an existing and explicit structure of cases is presumed. However, when problem solving experiences are stored in the form of textual narratives (in natural language), there is no explicit case structure, so that CBR cannot be applied directly. This thesis presents a novel approach for authoring cases from episodic textual narratives and organizing these cases in a case base structure that permits a better support for user goals. The approach is based on the following fundamental ideas: - CBR as a problem solving technique is goal-oriented and goals are realized by means of task strategies. - Tasks have an internal structure that can be represented in terms of participating events and event components. - Episodic textual narratives are not random containers of domain concept terms. Rather, the text can be considered as generated by the underlying task structure whose content they describe. The presented case base authoring process combines task knowledge with Natural Language Processing (NLP) techniques to perform the needed knowledge extraction and summarization