Portal de Periódicos da CAPES

Artificial intelligence based methods to support motor pump multi-failure diagnostic

2009; Volume: 17; Linguagem: Inglês

0969-1170

Flávia Bernardini, Ana Cristina Bicharra García, Inhaúma Neves Ferraz,

Machine Learning and Data Classification

Early failure detection in motor pumps is an important issue in prediction maintenance. An efficient condition-monitoring scheme is capable of providing warnings and predicting the faults at early stages. Usually, this task is executed by humans, but the logical progression of the condition-monitoring technologies is the automating the diagnosis process. To this end, intelligent diagnosis systems are used. Many researchers have explored artificial intelligence techniques to diagnose failures in general. However, all papers found in literature are related to a specific problem that can appear in many different machines. In real applications, when the expert analyzes a machine, not only one problem appears, but more than one problem may appear together. So, it is necessary to propose new methods to assist diagnosis, looking for a set of occurring faults. In this work, we describe methods to support motor pump failure diagnoses based on parametric net model and ANNs committees, and we propose methods to combine them. We describe a case study realized with a real dataset. The results obtained with these methods are encouraging.