TL;DR
The proposed EGFD-FDWSN-VIPINN approach improves gearbox fault detection and recognition performance in noisy and resource-constrained wireless sensor network environments.
Motor drive systems operating in embedded environments are frequently affected by noise, dynamic loading conditions, and electromagnetic interference, making timely fault diagnosis difficult. To improve diagnostic accuracy and real-time performance, this study proposes an intelligent fault diagnosis framework based on...
This study presents an applied comparative evaluation of automated rolling bearing fault identification using Artificial Neural Networks optimized through Bayesian Optimization, Particle Swarm Optimization, and Genetic Algorithm, highlighting optimized ANNs as competitive and computationally efficient solutions for bea...
The findings affirm the efficacy of XGBoost in bearing fault classification and emphasise the diagnostic value of carefully selected time-domain features, as well as suggesting strong potential for deploying such models in real-time condition monitoring and predictive maintenance systems.
Aiming at the difficulty of extracting fault features from nonlinear and non-stationary vibration signals of planetary gearboxes, a planetary gearbox fault diagnosis method based on Refined Composite Multiscale Fuzzy Entropy (RCMFE), Parametric-t-distributed Stochastic Neighbor Embedding (P-t-SNE), and Artificial Jelly...
In response to the serious noise interference in the fault signals obtained by the vibration sensors and the difficulty in effectively extracting the fault characteristics, a rolling bearing fault diagnosis method based on adaptive modal decomposition and correlation kurtosis feature enhancement is proposed. This met...
Accurate diagnosis of rolling bearing faults is critical to the reliability of industrial equipment. However, rolling bearings often operate under complex operating conditions, and with data imbalances and noise interference, fault diagnosis of them remains extremely challenging. To address these issues, a novel mode e...
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