Abstract
The correct and early detection of incipient faults or severe degradation phenomena in gas turbine systems is essential for safe and cost-effective operations. A multitude of monitoring and diagnostic systems were developed and tested in the last few decades. The current computational capability of modern digital systems was exploited for both accurate physics-based methods and artificial intelligence or machine learning methods. However, progress is rather limited and none of the methods explored so far seem to be superior to others. One solution to enhance diagnostic systems exploiting the advantages of various techniques is to fuse the information coming from different tools, for example, through statistical methods. Information fusion techniques such as Bayesian networks, fuzzy logic, or probabilistic neural networks can be used to implement a decision support system. This paper presents a comprehensive review of information and decision fusion methods applied to gas turbine diagnostics and the use of probabilistic reasoning to enhance diagnostic accuracy. The different solutions presented in the literature are compared, and major challenges for practical implementation on an industrial gas turbine are discussed. Detecting and isolating faults in a system is a complex problem with many uncertainties, including the integrity of available information. The capability of different information fusion techniques to deal with uncertainty are also compared and discussed. Based on the lessons learned, new perspectives for diagnostics and a decision support system are proposed.
Highlights
The growing penetration of renewable energy sources into the grid at a centralized and decentralized level represents a promising step toward a low-carbon society, but brings with it several challenges related to reliability, resiliency, and sustainability [1]
Kyriazis et al [71] proposed, for the first time, an information fusion system based on probabilistic data, and compared for this purpose a probabilistic neural network (PNN) and a dynamic Bayesian belief network (BBN)
A generalized architecture for a health management fusion system was proposed in Reference [80], where a BBN constituted the deeper decision level of information fusion, and its conditional probabilities were based on the maintenance history and the reliability of the diagnostic methods
Summary
The growing penetration of renewable energy sources into the grid at a centralized and decentralized level represents a promising step toward a low-carbon society, but brings with it several challenges related to reliability, resiliency, and sustainability [1]. Small cogeneration systems based on gas turbine technology can provide a more efficient utilization of fossil fuels, as well as a better integration with renewables in residential applications [1]. In this view, it is evident that effective diagnostic and prognostic systems are essential for the sustainable management of gas turbine plants, especially in a future energy market where these machines will experience frequent starts and stops or fast load, following the accommodation of renewable energy fluctuations, which will directly affect component lifetime. The simplest tools are those performing anomaly detection; when a measurement or a monitored parameter exceeds a predefined threshold or its trend differs from what is expected, an anomalous condition is flagged These systems do not give any indication of the problem source, but merely point out that something is not working as expected. An overview of the methods and applications can be found in Reference [33]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
