Multi-Dimensional Feature Extraction and Classification Method for Industrial Noise Integrating Wavelet Transform and Machine Learning

Abstract

Industrial noise classification plays a crucial role in equipment health monitoring and predictive maintenance, yet existing methods suffer from inadequate feature extraction, limited adaptability, and poor robustness in complex acoustic environments. This paper proposes a novel multi-dimensional feature extraction and classification framework that integrates adaptive wavelet transform with ensemble machine learning techniques, featuring three key methodological innovations: 1) An adaptive wavelet basis selection criterion (Eq. 6-8) that automatically optimizes time-frequency decomposition based on signal energy distribution, achieving 15% higher energy concentration than conventional fixed Daubechies-8 selection without manual parameter tuning; 2) A hierarchical attention-gating fusion mechanism (Eq. 28-30) that dynamically adjusts feature stream contributions according to signal SNR characteristics, improving classification robustness by 12% under noisy conditions compared to simple concatenation strategies; 3) A meta-learning ensemble framework (Eq. 42) that refines base classifier outputs through shallow neural network optimization, reducing misclassification between confused categories such as outer race and ball defects by 34%. Comprehensive evaluation on a dataset comprising 12,500 industrial noise samples across fifteen categories demonstrates that the proposed method achieves 96.5% classification accuracy, representing a 23.4% improvement over traditional approaches, while maintaining real-time processing capability within 312 milliseconds for ten-second audio segments. The method exhibits exceptional generalization performance across diverse equipment types and challenging operational conditions, enabling practical deployment in resource-constrained industrial environments.