Prognosis of a Degradable Hydraulic System

Abstract

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.

Work-in-progress: Reliability prediction of API centrifugal pumps using survival analysis

“…bearing failures, cavitation, unbalance, misalignment, etc. The literature contains many examples of proposals for RUL prediction of components applied to centrifugal pumps, some of which can be found in [7][8][9][10][11][12][13][14][15][16][17][18]. On the other hand, Li X. et al in [19] analyse the prognosis techniques of rotating equipment using system-level models, the purpose in this case is the prediction of the RUL at a system level.…”

Integration of future maintenance actions in the prediction parameters of the ATLAS COPCO ZR 200 compressor

“…Although the causes of failure of rotating equipment are very often related to the phenomena of wear of the rotating elements and vibration (El Adraoui, Gziri, and Mousrij, 2020a), performances of the compressors deteriorates due to detrimental effects of fouling on the aerodynamic flow characteristic (Hanachi, Liu, Banerjee, and Chen, 2016;Jombo, Pecinka, Sampath, and Mba, 2018;Zagorowska, Spüntrup, Ditlefsen, Imsland, Lunde, and Thornhill, 2020). Faced with all its causes of degradation, predictive maintenance seems to be the most suitable solution to improve the availability of rotating machines (Hanachi et al, 2016;El Adraoui, Gziri, and Mousrij, 2020b;Jeong, Son, Cho, Baik, and Lee, 2020). By implementing the data-driven approach for the prediction of rotating machine parameters, several works have been done.…”