Gas Turbine Diagnostics

Loboda, Igor

doi:10.5772/38185

Cited by 8 publications

(4 citation statements)

References 27 publications

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“…These two latter conditions must be distinguished by dedicated diagnostic units. This section describes the possible alternative solutions to effectively and efficiently implement diagnostic and condition monitoring techniques in safety instrumented systems, with particular reference to the oil and gas industry [21].…”

Section: Diagnostic Solutions For Safety Instrumented Systemsmentioning

confidence: 99%

Logic Solver Diagnostics in Safety Instrumented Systems for Oil and Gas Applications

Catelani

Ciani

Patrizi

2022

Safety

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A safety instrumented system (SIS) is a complex unit composed of a set of hardware and software controls which are expressly used in critical process systems. A SIS should be specifically designed to obtain the failsafe state of the monitored plant or maintain safety of the procedure or a process when unacceptable or dangerous conditions occur. This paper focuses on condition monitoring and different diagnostic solutions used in safety instrumented systems, such as limit alarm trips, on-board diagnostics, and logic solver diagnostics. A case study consisting of the design of a safety loop using standard IEC 61508 for a complex safety instrumented system in the oil and gas field is presented in the paper using a diagnostics-oriented approach. The presented methodology aims at reaching the optimal tradeoff between IEC 61508 and the market requirements focusing on the best technological solutions to optimize diagnostics and safety and minimize the system’s response time in case of failure. The results of the application emphasize the importance of an accurate diagnostic strategy on safety instrumented systems for oil and gas plants.

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Section: Diagnostic Solutions For Safety Instrumented Systemsmentioning

confidence: 99%

Logic Solver Diagnostics in Safety Instrumented Systems for Oil and Gas Applications

Catelani

Ciani

Patrizi

2022

Safety

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“…A large body of literature is devoted to the importance of performance monitoring and early problem detection. The components of monitoring and detection include data extraction, fault detection, fault location and prognosis [1][2][3][4]. Much of the current literature focuses on fault location algorithms which can be broken down into pattern recognition methods such as fuzzy logic [5,6], genetic algorithms [7], Bayesian belief networks [8][9][10], and neural networks [11][12][13][14] and model identification methods such as Kalman filtering [15] and weighted least squares [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Enhanced turbine monitoring using emissions measurements and data reconciliation

Syed

Dooley

Madron³

et al. 2016

Applied Energy

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Standard monitoring within a gas-turbine based cogeneration system includes key flow rates, temperatures, pressures and turbine vibration. These standard measurements can be enhanced with continuous emissions monitoring to help pinpoint system problems. A combination of these measurements, a fast prediction model and data reconciliation constitute an improved monitoring and diagnostic tool that can quantitatively predict the existence of turbine problems (for example, damaged combustor nozzles) even when standard turbine monitoring indicates no problems exist.

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“…Generic signal processing, neural network and fusion models are utilized for sensor validation to ensure the highest possible sensor fault detection with minimal false alarms [12]. Graphical tools are also useful and effective to find and remove data errors [13]. However, it should be noted that there is no general algorithm to clean the data.…”

Section: Data Processingmentioning

confidence: 99%

“…Intensity of the faults therefore should be assessed indirectly through their symptoms in deviating the turbine performance, namely, loss of isentropic efficiency and rise of the mass flow. The fault parameters (the symptoms) are the parameters of the thermodynamic model of the GTE, and they should be estimated using the available measurements of the system[13,105]. The GTE receives timevarying input, i.e., the control variables and the ambient conditions, and it generates the output.…”

mentioning

confidence: 99%

Gas Turbine Engine Performance Estimation and Prediction

Hanachi

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Modern health management approaches for gas turbine engines (GTE) aim to acquire precise information about the health state of the GTE components to optimize the maintenance decisions with respect to both the economy and safety. The task becomes more challenging for the GTE parts inaccessible to direct measurements with the available sensors of the GTE control system. This article-based thesis integrates a set of five coherent research work to address this problem.A detailed nonlinear thermodynamic model for single shaft GTEs is developed to predict the expected cycle parameters for the GTE in the healthy condition. In reality, the measured cycle parameters gradually deviate from the prediction due to performance deterioration. Physics-based performance indicators are defined based on the deviations in the measured performance parameters, compared to the respective model predictions.The indicators can effectively monitor the GTE performance deterioration in both shortterm and long-term regimes. In the next step, effect of the air humidity is taken into account to enhance the GTE model, and it is shown that the enhanced model can improve the performance monitoring by reducing the uncertainties.In order to separate the effects of different fault modes, an inference-based model is developed to predict the short-term recoverable performance deterioration due to the compressor fouling under different ambient and operating conditions. For the long-term non-recoverable performance deterioration due to the degradation mechanisms in the turbine hot section, two steps are undertaken; 1) a state estimation framework is developed for nonlinear/non-Gaussian systems with non-uniform time steps to track a degradation symptom of the turbine, i.e., loss of isentropic efficiency, using the observable performance indicators, and 2) the state estimation framework is extended to multidimensional dynamical systems with stochastic inputs for simultaneous tracking of two degradation symptoms, i.e., loss of isentropic efficiency and increase of the mass flow, using the observable parameters, provided by the GTE operating system.The developed techniques and frameworks are verified and validated, using a set of three-year operating data from an industrial GTE in a power plant. iii Preface This thesis is an integrated article-based thesis, consisted of the peer-reviewed journal articles and a conference paper, either published or currently under review. In all of the work presented henceforth, Houman Hanachi was the principal contributor in all areas of conceptualization, methodology development, implementation and validation, computer code writing, data preprocessing and analysis, as well as manuscript composition, including tables and figures. Professor Jie Liu provided the guidance and supervision of the overall project as well as timely and constructive feedbacks to all the research work. Dr. Avisekh Banerjee provided his ideas and guidance regarding the work as well as editorial suggestions. Dr. Ying Chen provided supportive explanatio...

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Gas Turbine Diagnostics

Cited by 8 publications

References 27 publications

Logic Solver Diagnostics in Safety Instrumented Systems for Oil and Gas Applications

Logic Solver Diagnostics in Safety Instrumented Systems for Oil and Gas Applications

Enhanced turbine monitoring using emissions measurements and data reconciliation

Gas Turbine Engine Performance Estimation and Prediction

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