Palavra-chave: CONDITION MONITORING

STATISTICAL INDICATORS FOUNDATION STRUCTURE HYDRODYNAMIC BEARING WEAR STABILITY AUTOENCODERS DYNAMIC ANALYSIS ARTIFICIAL INTELLIGENCE HYDRODYNAMIC JOURNAL BEARING ROTORDYNAMICS OVALIZATION FAULT ETL FAULT DETECTION INSTRUMENTED RAILWAY VEHICLE HYDRODYNAMIC JOURNAL BEARINGS CONVOLUTIONAL NEURAL NETWORK SIMULATION-DRIVEN DEEP LEARNING HYDRODYNAMIC BEARINGS WEAR FAULT CLASSIFICATION STRUCTURAL HEALTH MONITORING TIME RESPONSE DEEP LEARNING DATA SCIENCE AUTOMATIC OMA FAULT IDENTIFICATION AND ISOLATION WEAR FAULT MACHINE LEARNING