Publication of an article in the highly reputable journal Applied Energy by members of the Advanced Control Systems Laboratory "ACSL".

According to the public relations office of the university, Fatemeh Negar Irani and Mohammad Javad Soleimani, members of the ACSL laboratory under the guidance of Dr. Meysam Yadegari and in collaboration with Dr. Nader Maskin from Qatar University, have succeeded in publishing a research article in one of the most prestigious journals of Elsevier, titled Applied Energy, with an impact factor of 11.2. This article presents a novel data-driven method for detecting sensor and actuator faults in gas turbine systems. In this approach, by employing a transfer learning strategy and realizing a Koopman linear model using the proposed deep neural network, an adaptive high-accuracy predictor for the gas turbine system has been developed, taking into account the gradual degradation of the system. By utilizing this predictor, dedicated and generalized residual sets have been designed and produced using a geometric approach for fault separation, and a decision analysis has been developed for simultaneous fault detection. You can access the article through the free link below. https://authors.elsevier.com/c/1iyny15eifC4dt