Noise Injection Framework for Development of Noise-Robust Predictive Maintenance Solutions

Urszula Jachymczyk, Paweł Knap, Adam Szulecki

Abstract: In predictive maintenance (PdM) applications, sensor signals are often affected by various forms of noise that can degrade model accuracy and reliability. While many studies use Gaussian noise to simulate environmental disturbances, real-world sensor data is typically impacted by a broader range of noise types, including impulsive, frequency-dependent, and drift-based distortions. This paper presents a modular noise injection framework designed to augment PdM datasets with diverse synthetic and measured noise signals. The framework simulates realistic sensor and system-level disturbances and is evaluated on vibration data from an online shaft monitoring test stand. We assess the impact of noise-based augmentation on both limited and comprehensive datasets using six machine learning models. Results demonstrate that properly designed noise injection can substantially enhance model robustness and performance, particularly in data-constrained settings. Notably, we observed that models trained on raw time-domain data, while offering greater potential for learning rich signal characteristics, also exhibit higher sensitivity to aggressive augmentation. Our findings highlight the importance of carefully tuning noise augmentation strategies to match the measurement context and model architecture.

External IDs:doi:10.1109/metroxraine66377.2025.11340190