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

Application of Adaptive Neuro-fuzzy inference System in Performance-based Condition Monitoring of Industrial Gas Turbines

This thesis provides a detailed investigation on performance-based data-driven automatic condition monitoring of industrial gas turbine engines for fault detection, isolation and prognosis to improve the maintenance strategy of this critical equipment

Design optimization of industrial gas turbines using simulated annealing algorithms

Currently, gas turbine is one of the most widely-used power generating technologies. The race to achieve higher efficiency from gas turbines is gathering momentum with most of the major manufacturers. Cogeneration with advanced engines has the prospect of attaining thermal efficiencies around 60% in the future. In this condition, further development of gas turbine design optimization in order to obtain higher thermal efficiency seems to be beneficial. In the current work, the design of a single shaft gas turbine in a cogeneration plant is optimized based on the model established using thermodynamic theory. The overall thermal efficiency of the engine is tried to be optimized by adjusting the compressor efficiency, turbine efficiency, compression pressure ratio, and turbine inlet temperatures. A feasible solution should satisfy two physical constraints, namely a desired gas turbine power and a suitable limit of engine exhaust temperature. An evolutionary model using Simulated Annealing algorithm is developed to find the sets of optimal solutions in the space defined by user experience and literature. A number of case studies have been performed and an optimal solution and their corresponding performance are discussed

A Framework for Intelligent Condition-based Maintenance of Rotating Equipment using Mechanical Condition Monitoring

The ideal end result of maintenance strategy is to increase profitability, improve product quality and ensure safety conditions. In condition-based maintenance (CBM), asset health is monitored regularly to maximize reliability and availability by determining necessary maintenance at the right time. Review of recent studies shows most of developed approaches propose a standalone system for each stage of maintenance system. In order to standardize a generic architecture for machinery CBM, this paper attempts to introduce an intelligent framework consisting of several functional modules, starting from data acquisition and ending to advisory generation, with the emphasis on approaches of condition monitoring and maintenance decision-making

A Framework for Intelligent Condition-based Maintenance of Rotating Equipment using Mechanical Condition Monitoring

The ideal end result of maintenance strategy is to increase profitability, improve product quality and ensure safety conditions. In condition-based maintenance (CBM), asset health is monitored regularly to maximize reliability and availability by determining necessary maintenance at the right time. Review of recent studies shows most of developed approaches propose a standalone system for each stage of maintenance system. In order to standardize a generic architecture for machinery CBM, this paper attempts to introduce an intelligent framework consisting of several functional modules, starting from data acquisition and ending to advisory generation, with the emphasis on approaches of condition monitoring and maintenance decision-making