Advanced genetic algorithm-based signal processing for multi-degradation detection in steam turbines

This research contributes to the field of reliability engineering and system safety by introducing aninnovative diagnostic method to enhance the reliability and safety of complex technological systems. Steam turbines are specifically referred to. This study focuses on the integration of advanced signal processing techniques and engineering dynamics in addressing critical issues in the monitoring and maintenance of mechanical systems. By utilizing genetic algorithms, we improve the capability to detect, localize, and ascertain the causes of both singular and intricate degradations, including three-fold and four-fold faults, within steam turbine operations. We can detect degradation with accuracies of 72.6% for three-fold faults and 62.2% for four-fold faults. This significant advancement emphasizes the potential for improved machine and structural health monitoring, especially where non-stationary and random vibrations are common, such as in powertrain and drivetrain systems. This methodology is vital for the maintenance and operational strategies of critical infrastructures like nuclear power plants, chemical plants, and manufacturing facilities where steam turbines play a crucial role. The novelty of this approach lies in the use of genetic algorithms for thermal-flow diagnostics of steam turbines, which had been unaddressed in literature. Moreover, the merger of theoretical and experimental aspects in this study underscores its relevance to practical applications, thereby demonstrating an original contribution to engineering knowledge and showcasing significant advancements over established methods. The research underscores the method’s potential as a universal tool for diagnosing complex systems, representing an advance in reliability engineering practices. By applying genetic algorithms, a noticeable link to improving the safety and reliability of technological systems is established, offering valuable insights into the design, maintenance, and extension of the lifespan of critical infrastructure.