A Review of Information Fusion Methods for Gas Turbine Diagnostics

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.

Micro Gas Turbines in the Future Smart Energy System: Fleet Monitoring, Diagnostics, and System Level Requirements

“…The fusion of information from different sources in order to address the limitations of different methods and enhance the capabilities of a decision support system has also been the focus of multiple research studies. It has been extensively used on the sensor level, but its use for decision support and diagnostics has been relatively limited (Zaccaria et al, 2019). However, since it offers benefits in dealing with incomplete data, it can be particularly beneficial for small and micro-scale gas turbines, allowing the development of an automated diagnostics and decision support system.…”

Development of the method for estimating the technical condition of gas pumping units by their accelerating characteristic

“…No less scientific papers [13][14][15][16][17][18] are devoted to parametric diagnostic methods. So, in [13] it was shown that information integration methods, such as Bayesian networks, fuzzy logic or probabilistic neural networks, can be used to implement a decision support system that can be used for parametric diagnostics of gas turbines. At the same time, the proposed decision support system does not contain information about the technical condition of the automatic control system of the gas pumping unit and does not allow using it to monitor the operability of the automatic control system.…”

Deep Recurrent Entropy Adaptive Model for System Reliability Monitoring

“…Nevertheless, it has general applicability to other systems from varying domains, such as biomedical data analysis and any high risk technology generating sequential data or sensor data from arrays of sensor networks [22], [23]. A review of other information fusion methods for IGT diagnostics can be found in [24].…”