Roller Bearing Fault Detection Using Ordinal Pattern-Based Methods for Acoustic and Vibration Analysis

Reliable detection of incipient faults in rotating machinery under noisy environments and varying speeds is essential for ensuring operational safety and noise reduction. This study explores the potential of ordinal pattern (OP)-based methods for acoustic and vibration signal analysis in fault diagnosis, leveraging their ability to capture underlying modulation structures often masked by background noise. Two types of OP-based methods are investigated: 1. Statistical approaches, including Permutation Entropy (PE) and its amplitude-sensitive extensions-Dispersion Entropy, Bubble Entropy, Amplitude-Aware PE, and improved PE. 2. Time-domain decomposition methods, specifically Ordinal Pattern Mode Decomposition (OPMD), utilize OP-based filters to extract dominant oscillatory and narrowband components iteratively. Theoretical analysis confirms that OPMD outperforms Empirical Mode Decomposition (EMD) in preserving modulation patterns. The study was conducted in a noisy industrial environment. Acoustic signals were recorded, while vibration signals were simultaneously acquired for comparative analysis. The results on five bearing conditions (healthy and faulty) demonstrate that both PE-based approaches and OPMD applied to acoustic signals effectively detect faults. However, high noise levels can distort ordinal pattern probability distributions, potentially impacting detection performance. Despite this, the proposed methods outperform classical signal processing techniques and offer valuable insights for noise-aware diagnostics in industrial settings.