RESONANCE 2023

Session

12 juil. 2023 à 14:00

Toulouse

Présidents de session

Survishino 10 / Data driven condition monitoring 1

• Eltabach Mario ()

Liste des contributions Ordre du jour

197. "DATA1#1 - Vibration based milling diagnostics using Artificial Intelligence"

Knittel Dominique

12/07/2023 14:00

In industrial machining processes, tool failures may result in losses in surface and dimensional accuracy of a finished part, or possible damage to both the work piece and the machine. Consequently, tool condition monitoring has become essential to achieve high-quality machining as well as cost-effective production. Moreover, cutting tool degradation may vary considerably under different...

198. "DATA1#2 - CANCELLED - Multi-Source Information Fusion Fault Diagnosis for Rotating Machinery using Signal and Data Processing"

Makrouf Iman

12/07/2023 14:20

Machine fault diagnosis is crucial in industrial systems to enhance reliability, lifetime, and service availability. Intelligent fault diagnosis (IFD) using artificial intelligence (AI) techniques has emerged as a promising approach for automating machine health assessment and reducing labor costs. One approach to improve fault diagnosis accuracy, which has become a highly relevant research...

199. "DATA1#3 - Wind turbine drivetrain fault detection using physics-informed multivariate deep learning"

Jamil Faras

12/07/2023 14:40

Vibration analysis is a prevalent technique in the predictive maintenance of wind turbines. It is an effective method for early fault detection and enables the creation of cost-effective maintenance strategies. Commonly used vibration analysis methods in the literature rely on signal processing techniques such as time and frequency domain approaches. However, the signal processing techniques...

200. "DATA1#4 - Anomaly Detection in Aircraft Engine Vibration Using Deep Convolutional Autoencoder"

El Hidali Abdallah

12/07/2023 15:00

The useful life of aircraft engines depends on their operating environment (polluted areas, harsh climate, etc.). Detecting signs of degradation and aging can be difficult due to background noise measured on vibrational signals. Statistical methods such as threshold-based monitoring may not be reliable enough. This paper presents a promising method based on learning normal behavior on a...

201. "DATA1#5 - Airplane turbulence detection with hybrid deep learning model"

Dampeyrou Charles

12/07/2023 15:20

Atmospheric turbulence has a significant impact on airplane motions and can induce excessive stress and fatigue damage. The identification of turbulence in aircraft service phase is of particular interest to estimate actual structural fatigue or to define an improved maintenance plan. This article describes a new model to detect turbulence from in-board instrumentation solely. Equations...

202. "DATA1#6 - Ball bearing diagnosis using a homogenous hybrid database in a supervised machine learning"

Sow Souleymane

12/07/2023 15:40

Digital twins (DT) are often described as a virtual and dynamic representation of a system. They guarantee interaction between physical and virtual spaces. In the context of maintenance 4.0, the lack of historical data can be caused by an impossible instrumentation for complex systems. To face it, DT offers the possibility to simulate several operating modes which can serve for a diagnostic....