Affiliations: School of Mechanical Engineering, Vignan University,
Vadlamudi, India | Machine Dynamics and Failure Analysis Laboratory,
Bharath Heavy Electricals Limited (B.H.E.L), Hyderabad, India | Department of Mechanical Engineering, Sir C R Reddy
College of Engineering, Eluru, India
Abstract: Health of rotating machines like turbines, generators, pumps and
fans etc., is crucial to reliability in power generation. For real time,
integrated health monitoring of steam turbine, novel fault detection data is
essential to reduce operating and maintenance costs while optimizing the life
of the critical engine components. This paper describes about normal and
abnormal vibration data detection procedure for a large steam turbine (210 MW)
using artificial neural networks (ANN). Self-organization map is trained with
the normal data obtained from a thermal power station, and simulated with
abnormal condition data from a test rig developed at laboratory. The optimum
size of self-organization map is determined using quantization and topographic
errors, which has a strong influence on the quality of the clustering. The Mat
lab 7 codes are applied to generate program using neural networks toolbox.
Keywords: Integrated health monitoring, self-organization map, quantization and topographic errors, clustering
