TEIXEIRA JÚNIOR, A. G.; http://lattes.cnpq.br/3695506891372095; TEIXEIRA JÚNIOR, Adalberto Gomes.
Abstract:
The sound generated by an engine during operation contains information about
its conditions, becoming an important source of information to evaluate its status
without requiring intervention in equipment. The fault diagnosis of the engine
usually is performed by a human, based on his experience in a noisy
environment. As the operation of the engine is a periodic procedure, the
generated signal follows a well-defined pattern, allowing the evaluation of its
operating conditions. On this context, this research deals with modeling the
acoustic signal generated by engines in power plants, using techniques from
digital signal processing and artificial intelligence, with the purpose of assisting
the fault diagnosis, minimizing the human presence at the engine room. The
technique applied is based on the study of engines operation and the acoustic
signal generated by them, extracting signal representative characteristics in
different domains, combined with machine learning methods, to build a multiclassifier
to evaluate the engines status. Signals extracted from engines of
Borborema Energética S.A. power plant, during the REPARAI Project (REPair over
AiR using Artificial Intelligence), ANEEL PD-6471-0002/2012, were used in the
experiments. In this research, the method proposed has demonstrated an
accuracy rate of nearly 100%. The approach has proved itself to be efficient to
fault diagnosis, mainly by not being an invasive method and not requiring human
direct contact with the engine.