Reconhecimento automático de padrões de defeitos em motobombas utilizando análise de sinais de vibração

• Ano de defesa: 2009
• Autor(a) principal: Loureiro, Suelen Marconsini
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Espírito Santo; BR; Mestrado em Informática; Centro Tecnológico; UFES; Programa de Pós-Graduação em Informática
• Palavras-chave em Português: Defeitos em motobombas; Reconhecimento de padrões; Vibração; Processamento de sinais; Classificação; Localização de falhas (Engenharia); Ciência da Computação; 004
• Link de acesso: http://repositorio.ufes.br/handle/10/6385

Predictive maintenance plays an important role for the economy and safety of petroleum systems. Analysis of vibration signals obtained from machines involved in the petroleumextraction process allows subject matter experts to characterize and monitor the situation. However, because of the high cost and the lack of availability of those experts, the existence of automatic systems that support the analysis is desirable. This work presents an automatic procedure to recognize defect patterns in motorpump equipments. A set of techniques previously selected for each stage of the pattern recognition process is applied in the procedure. Signals processing techniques are used to obtain descriptive features from vibration signals. Two approaches are evaluated for the selection of relevant characteristics: using heuristics based on domain specialized knowledge (manual approach) and application of selection algorithms (automatic approach). Real examples are subjected to a supervised learning algorithm in order to compare the manual and the automatic selection approaches.