Work

Performance Analysis of Algorithms for Treating Outliers in PdM from UAVs

Year: 2026

Type: article

Abstract: Due to their vast potential, Unmanned Aerial Vehicles (UAVs) are increasingly being utilized in various applications. To prevent in-flight failures and loss of control, implementing Internet of Things (IoT)-based Predictive Maintenance (PdM) systems is crucial. However, data collected from PdM systems often contains outliers, which can significantly degrade the accuracy and performance of predictive models. In this paper, we present a comparative performance analysis of several outlier detection methods, namely K-Nearest Neighbors (KNN), Autoencoder (AE), and Isolation Forest (IForest). The datasets used to evaluate these methods were acquired from a UAV predictive maintenance system designed to estimate the Remaining Useful Life (RUL) of Li-ion batteries and detect faults in Brushless DC (BLDC) motors. Ultimately, this study aims to determine the most effective outlier detection method for UAV predictive maintenance datasets.

Source: Processes

DOI: 10.3390/pr14071038

Authors: Dragos Alexandru Andrioaia, Petru Gabriel Puiu, George Culea, Ioan Viorel Banu, Sorin-Eugen Popa, Enachi Andrei

Institution: University of Bacău

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896