Development of a Method for Diagnosing Faults in Hydraulic Systems Using Artificial Neural Networks with Deep Learning

Abstract

Abstract The application of artificial intelligence is a recent improvement in the industry, allowing preventive maintenance to be applied as a reliability method for detecting failures in hydraulic systems. This is achieved by using artificial neural networks (ANN) as classifiers to make automatic binary and categorical decisions. Since these systems have multiple conditions and sub-conditions that can be complex for normal analysis, the UCI repository database is relied upon to construct an intelligent algorithm of artificial neural networks with deep learning. This has proven to be a highly effective way of predicting failures, with an overall accuracy rate of 97% when applying the intelligent model to the system. As a result, it can be concluded that deep learning is much more efficient than classical machine learning.

Keywords

• artificial intelligence, predictive maintenance, artificial neural networks, deep learning.