A noise-robust over-sampling method for imbalanced transmission production data classification

Abstract In the automotive industry, quality assurance is crucial for ensuring safety and performance. An integral part of quality assurance is Noise, Vibration and Harshness (NVH) testing in transmission production. NVH testing utilizes sensors such as accelerometers, microphones, and encoders to capture acoustic and dynamic characteristics under simulated road conditions. Traditionally, these characteristics are evaluated against manually defined thresholds set by NVH engineers—a process that is labor intensive, costly, and complex. Although advances in machine learning (ML) offer the potential to improve efficiency in NVH testing, challenges such as severe class imbalance, outliers, and limited access to production data hinder its adoption. To address these challenges, we propose Modified Z-Score Cyclic Exponential Averaging of Minority Data ( $$mZ$$ mZ - $$cEAMD$$ cEAMD ) , a noise-robust over-sampling method that integrates exponential averaging with outlier removal to generate balanced and representative samples, while preserving the fundamental characteristics of the original data distribution. This is complemented by a feature extraction approach that integrates spectral order tracking, which captures gear meshing frequencies relative to input rotation speed, with the Short-Time Fourier Transform (STFT), which represents time-frequency characteristics. Together, these features contain critical discriminative information for transmission classification and are used to assess the effectiveness of the proposed over-sampling method. Comparative experiments against established over-sampling methods demonstrate that the proposed $$mZ$$ mZ - $$cEAMD$$ cEAMD approach outperforms alternative techniques in addressing class imbalance in datasets containing outliers. This study advances ML-based NVH testing, thereby fostering the digital transformation of automotive quality assurance.