901 research outputs found
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
Fault diagnostics for advanced cycle marine gas turbine using genetic algorithm
The
major challenges faced by the gas turbine industry, for both the users and the
manufacturers, is the reduction in life cycle costs , as well as the safe and efficient
running of
gas turbines. In view of the above, it would be advantageous to have a
diagnostics system capable of reliably detecting component faults (even though limited
to
gas path components) in a quantitative marmer. V
This thesis
presents the development an integrated fault diagnostics model for
identifying shifts in component performance and sensor faults using advanced concepts
in
genetic algorithm. The diagnostics model operates in three distinct stages. The rst
stage uses response surfaces for computing objective functions to increase the
exploration potential of the search space while easing the computational burden. The
second
stage uses the heuristics modification of genetics algorithm parameters through a
master-slave
type configuration. The third stage uses the elitist model concept in genetic
algorithm to preserve the accuracy of the solution in the face of randomness.
The above fault
diagnostics model has been integrated with a nested neural network to
form a
hybrid diagnostics model. The nested neural network is employed as a pre-
processor or lter to reduce the number of fault classes to be explored by the genetic
algorithm based diagnostics model. The hybrid model improves the accuracy, reliability
and
consistency of the results obtained. In addition signicant improvements in the total
run time have also been observed. The advanced
cycle Intercooled Recuperated WR2l
engine has been used as the test engine for implementing the diagnostics model.SOE Prize winne
Comparing different schemes in a combined technique of Kalman filter, artificial neural network and fuzzy logic for gas turbines online diagnostics
The paper presents research on the online performance-based diagnostics by implementing a novel methodology, which is based on the combination of Kalman Filter, Artificial Neural Network, Neuro-Fuzzy Logic and Fuzzy Logic. These methods are proposed to improve the success rate, increase the flexibility, and allow the detection of single and multiple failures. The methodology is applied to a 2-shaft industrial gas turbine engine for the automated early detection of single and multiple failures with the presence of measurement noise.
The methodology offers performance prediction and the possibility of utilizing multiple schemes for the online diagnostics. The architecture leads to three possible schemes. The first scheme includes the base methodology and enables Kalman Filter for data filtering, Artificial Neural Network for the component efficiency prediction, the Neuro-Fuzzy logic for the failure quantification and the Fuzzy Logic for the failure classification. For this scheme, a performance simulation tool (Turbomatch) is used to calculate the thermodynamic baseline. The second scheme replaces Turbomatch with the Artificial Neural Network, that is used to calculate the deteriorated efficiencies and the reference efficiencies. The third scheme is identical to the first one but excludes the shaft power measurements, which are not available in aero engines or might not be usable for some power plant configurations.
The paper compares the performance of the three methodologies, with the presence of measurement noise (0.4% reference noise and 2.0% reference noise), and 24 types of random single and multiple failures, with variable magnitude. The first methodology has been already presented by Togni et al. [10], whereas the other two methodologies and results are part of the PhD thesis presented by Togni [18] and they extend the applicability of the method. The success rate, targeting the correct detection of the of the failure magnitude ranges between 92% and 100% without measurement noise and ranges between 66% and 83% with measurement noise. Instead, the success rate of the classification, targeting the correct detection of the type of failure ranges between 93% and 100% without measurement noise and between 85% and 100% with measurement noise
Computational intelligence techniques for maximum energy efficiency of cogeneration processes based on internal combustion engines
153 p.El objeto de la tesis consiste en desarrollar estrategias de modelado y optimización del rendimiento energético de plantas de cogeneración basadas en motores de combustión interna (MCI), mediante el uso de las últimas tecnologías de inteligencia computacional. Con esta finalidad se cuenta con datos reales de una planta de cogeneración de energía, propiedad de la compañía EnergyWorks, situada en la localidad de Monzón (provincia de Huesca). La tesis se realiza en el marco de trabajo conjunto del Grupo de Diseño en Electrónica Digital (GDED) de la Universidad del País Vasco UPV/EHU y la empresa Optimitive S.L., empresa dedicada al software avanzado para la mejora en tiempo real de procesos industriale
Transient modelling of a diesel engine and air-path control
Due to the inherent nonlinearity of the diesel engine, real-time control of the variable geometry turbocharger (VGT) and exhaust gas recirculation (EGR) valve still remains a challenging task. A controller has to be capable of coping with the transient operating condition of the engine, the interactions between the VGT and EGR, and also the trade-off effect in this control problem. In this work, novel real-time fuzzy logic controllers (RFLC) were developed and tested. Firstly, the proposed controllers were calibrated and validated in a transient diesel engine model which was developed and validated against the Caterpillar 3126B engine test bed located at the University of Sussex. The controllers were then further tested on the engine test bed. Compared to conventional controllers, the proposed controllers can effectively reduce engine emissions as well as fuel consumption. Experimental results show that compared to the baseline engine running on the Nonroad Transient Cycle (NRTC), mean values of the exhaust gas opacity and the nitrogen oxides (NOx) emission production were reduced by 36.8% and 33%, respectively. Instant specific fuel consumption of the RFLC engine was also reduced by up to 50% compared to the baseline engine during the test. Moreover, the proposed fuzzy logic controllers can also reduce development time and cost by avoiding extensive engine mapping of inlet air pressure and flow. When on-line emission measurements were not available, on-board emission predictors were developed and tested to supply the proposed fuzzy logic controller with predictions of soot and NOx production. Alternatively, adaptive neuro fuzzy inference system (ANFIS) controllers, which can learn from fuzzy logic controllers, were developed and tested. In the end, the proposed fuzzy logic controllers were compared with PI controllers using the transient engine model
On the analysis and design of genetic fuzzy controllers : An application to automatic generation control of large interconnected power systems using genetic fuzzy rule based systems.
Frequency Control of large interconnected power systems is governed by means
of Automatic Generation Control (AGC), which regulates the system frequency
and tie line power interchange at its nominal parameter set points. Conventional
approaches to AGC controller design is centered around the Proportional, Integral
and Derivative (PID) controller structures, which have found widespread
application within industry.
However, the dynamic changes experienced throughout the life cycle of power
systems have many contributing factors, in part attributed to unknown knowledge
of system behavior, neglected process dynamics and a limited knowledge of
system interactions, which makes modeling for AGC systems particularly trying
for conventional AGC controller design approaches.
Therefore, in this study, Genetic - Fuzzy controllers (GA - Fuzzy) are applied as
plausible candidates for Automatic Generation Controller design and application.
In GA - Fuzzy controllers, genetic algorithms which are based on the foundation
of evolutionary heuristics are used as a global search method for FLC design.
This is particularly motivated by the fact that Fuzzy controllers, especially where
there are large data sets, unknown process knowledge and insu cient expert data
available, FLC controller design proves to be a daunting task.
Therefore, this thesis explores the automatic design of FLC controllers through
evolutionary heuristics and applies the designed controller to the AGC problem
of large interconnected power systems. The design methodology followed is to
understand power system interactions through power plant modeling and the
simulation power plant models for the basis for AGC controller design.
It is shown in this study that the performance of the GA - Fuzzy controller
have favourable characteristics in terms of robust performance, robustness properties
and compares favorably with conventional AGC controller techniques. The
analysis of the GA - Fuzzy controller shows that problem formulation and chromosome
encoding of the problem search space forms an important prerequisite
for controller design by evolutionary methods.
Therefore the study concludes by stating that GA - Fuzzy controllers are plausible
for application within the power industry because of its desirable attributes
and that future work would include extending this research into areas of renewable
energy for study and application
Aeronautical Engineering: A continuing bibliography, supplement 120
This bibliography contains abstracts for 297 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1980
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