Automatic Anomalies Detection in Hydraulic Devices

TL;DR

This paper introduces AI and machine learning models to detect anomalies in hydraulic cylinders, enhancing proactive maintenance and operational safety in hydraulic systems.

Contribution

The paper presents a novel application of AI/ML techniques specifically for anomaly detection in hydraulic cylinders, addressing a gap in device-health monitoring.

Findings

AI/ML models effectively identify abnormal conditions in hydraulic cylinders

The approach improves early detection of failures, reducing downtime

Enhanced safety and maintenance efficiency in hydraulic systems

Abstract

Nowadays, the applications of hydraulic systems are present in a wide variety of devices in both industrial and everyday environments. The implementation and usage of hydraulic systems have been well documented; however, today, this still faces a challenge, the integration of tools that allow more accurate information about the functioning and operation of these systems for proactive decision-making. In industrial applications, many sensors and methods exist to measure and determine the status of process variables (e.g., flow, pressure, force). Nevertheless, little has been done to have systems that can provide users with device-health information related to hydraulic devices integrated into the machinery. Implementing artificial intelligence (AI) technologies and machine learning (ML) models in hydraulic system components has been identified as a solution to the challenge many industries currently face: optimizing processes and carrying them out more safely and efficiently. This paper presents a solution for the characterization and estimation of anomalies in one of the most versatile and used devices in hydraulic systems, cylinders. AI and ML models were implemented to determine the current operating status of these hydraulic components and whether they are working correctly or if a failure mode or abnormal condition is present.