The concept of Acoustic Source Identification (ASI), which refers to the process of identifying
noise sources has attracted increasing attention in recent years. The ASI technology can be …

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel
support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete …

The widespread use of UAVs has revolutionized a variety of industries and altered the way
we approach crucial tasks over the past decade. Commonly known as drones, these …

This research proposes an intelligent method for fault detection and classification (FDC) in
solar based distribution systems using Generative Adversarial Networks (GANs) and Social …

Fault detection and isolation in unmanned aerial vehicle (UAV) propellers are critical for
operational safety and efficiency. Most existing fault diagnosis techniques rely basically on …

Anomaly detection has an important impact on the development of unmanned aerial
vehicles, and effective anomaly detection is fundamental to their utilization. Traditional …

UAVs are commonly utilized for the detection of insulator faults in transmission lines. The
successful execution of such missions depends on two pivotal factors; the detection of …

The paper proposes an anomaly detection method for UAVs based on wavelet
decomposition and stacked denoising autoencoder. This method takes the negative impact …

Intelligent fault diagnostics is an essential part of the concept of Industry 4.0. Most of the
developed techniques are usually tested by the same dataset whose data is acquired under …

To address the problems of external interference during unmanned aerial vehicle (UAV)
flight and the low accuracy and weak generalization ability of the current single fault …