RESEARCH PAPER
Hyperparameter optimization of isolation forest using a variable neighborhood search approach for anomaly detection in electric motors
1
Antioquia, Universidad de Antioquia, Colombia
2
Paraná, Universidade Tecnológica Federal do Paraná, Brazil
3
Antioquia, Universidad Nacional de Colombia, Sede Medellín, Colombia
A – Conceptualization; B – Methodology; C – Software; D – Validation; E – Formal analysis; F – Investigation; G – Resources; H – Data curation; I – Writing – original draft; J – Writing – review & editing; K – Visualization; L – Supervision; M – Project administration; N – Funding acquisition
Submission date: 2025-12-09
Final revision date: 2026-02-19
Acceptance date: 2026-04-21
Online publication date: 2026-07-08
Corresponding author
KEYWORDS
Isolation ForestHyperparameter OptimizationAnomaly DetectionVariable Neighborhood Search (VNS)BLDC MotorsTwo-wheeled EVs
TOPICS
ABSTRACT
Anomaly detection in electric motors supports reliability in electric mobility systems. Isolation Forest is commonly used for unsupervised detection of abnormal operating conditions; however, its performance depends on hyperparameter configuration under non-stationary real-world conditions.
This study proposes a systematic hyperparameter optimization framework based on VNS to enhance iForest robustness in BLDC motor monitoring. The proposed VNS–IF–IQR framework integrates structured hyperparameter exploration with adaptive threshold calibration. The methodology is evaluated using 78,886 records from rear-hub BLDC motors operating under real urban conditions in Medellín, Colombia, and validated through a synthetic dataset with controlled anomaly injection. Results show that VNS-based optimization reduces anomaly score variability compared to default configurations. Sensitivity analysis across multiple scenarios indicates marginal hyperparameter influence below 6%, confirming convergence toward broad near-optimal regions and supporting the robustness of the proposed approach.
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| eISSN: | 2956-3860 |
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