Prognosis of a Degradable Hydraulic System: Application on a Centrifugal Pump

This article proposes a preliminary diagnostic/prognostic method for the identification of a critical system, undergoing a continuous evolutionary degradation, in a production area, and the determination of the component responsible for its degradation, called the failing element. Using for this, a model based on learning by multilayer perception (MLP). The purpose of this paper is to provide a modeling approach that makes it possible to determine the level of degradation reached by the system at any given point of time, in a precise way. Thus, the horizon of the failure will be produced with a minimum error compared to the discrete jump model used in the literature. The proposed approach consists of using a neural network with fewer layers and optimal computing time. We performed data learning (tests) in order to illustrate a regression of good correlation of these data (tests) on a centrifugal pump with satisfactory performance parameters and compared it with other commonly used methods