Vibration and time based feature engineering for comprehensive gear health

AI-assisted structural health monitoring for high-performance mechanical components

Abstract. High-performance mechanical component structural health monitoring (SHM) is a vital issue in contemporary engineering, especially in the aerospace, automotive, and industrial turbomachinery sectors where component failure may be disastrous. This article introduces a new AI-aided SHM framework with multimodal...

Vibration-Based Multicomponent Condition Monitoring and Diagnostics Approach for Elevator Door System

Condition monitoring of automatic elevator doors is essential for ensuring operational reliability, passenger safety, and optimized maintenance scheduling. Although scientific literature extensively covers fault diagnostics for some types of machinery (e.g., rotating machinery), complex electromechanical door systems a...

Machine Learning-Based Predictive Maintenance for Mechanical Equipment in Smart Manufacturing

A group-aware and explainable machine learning-based approach to normalized RUL prediction using vibration, current and operating condition features is suggested, which showed that unseen-tool generalization could be measured, but there was a variation between test tools that showed the difficulty of cross-tool degrada...

Machine learning based diagnosis of gearbox wear patterns through lubricant analysis

Artificial Intelligence-Driven Quality Control in Mechanical Manufacturing: Vibration-Based Multiclass Gear Fault Detection Using LightGBM

The results show that vibration-based machine learning can support robust, near-real-time fault identification in mechanical manufacturing environments and highlights the importance of chronological validation, feature engineering over multiple time windows, and the trade-off between predictive performance and deployme...

Structural damage detection in a 3D frame based on acceleration time series and machine learning

This study investigates the performance of ten statistical indicators as input features for ML-based damage detection, applied to an experimentally tested frame structure, using numerically obtained data for training.