In-Process Monitoring of Gear Power Honing Using Vibration Signal Analysis and Machine Learning

In modern gear manufacturing, stringent Noise, Vibration, and Harshness (NVH) requirements demand high-precision finishing operations such as power honing. Conventional quality control strategies rely on post-process inspections and Statistical Process Control (SPC), which fail to capture transient machining anomalies and cannot ensure real-time defect detection. This study proposes a novel, data-driven framework for in-process monitoring of gear power honing using vibration signal analysis and machine learning. Our proposed methodology involves continuous data acquisition via accelerometers, followed by time-frequency signal analysis. We investigate and compare the efficacy of three subspace learning methods for features extraction: (1) Principal Component Analysis (PCA) for dimensionality reduction; (2) a two-stage framework combining PCA with Linear Discriminant Analysis (LDA) for enhanced class separation; and (3) Uncorrelated Multilinear Discriminant Analysis with Regularization (R-UMLDA), adapted for tensor data, which enforces feature decorrelation and includes regularization for small sample sizes. These extracted features are then fed into a Support Vector Machine (SVM) classifier to predict four distinct gear quality categories, established through rigorous geometrical inspections and test bench results of assembled gearboxes. The models are trained and validated on an experimental dataset collected in an industrial context during gear power-honing operations, with gears classified into four different quality categories. The proposed framework achieves high classification accuracy (up to 100%) in an industrial setting. The approach offers interpretable spectral features that correlate with process dynamics, enabling practical integration into real-time monitoring and predictive maintenance systems.