A Nonlinear AutoRegressive-Based Noise Cancellation Method for Real-Time Fault Diagnosis of Rolling Bearings

Rolling bearing failures significantly contribute to mechanical breakdowns, underlining the necessity for efficient diagnostic strategies. In this article, I explore signal filtering techniques for rolling bearing fault diagnosis. Conventional filtering methods face challenges in adaptability, robustness, and complexity. To overcome these challenges, a nonlinear AutoRegressive-based noise cancellation method is proposed. First, the raw signal is modeled using the generalized nonlinear AutoRegression (GNAR) model with the aim of describing the nonlinear effects of the rolling bearings under rotating conditions. After that, the local regularization-assisted orthogonal least-squares analysis (LROLS) algorithm is used to develop a filtering model. The combination of GNAR and LROLS can improve the adaptability and robustness when compared with the conventional filtering methods. Finally, a real-time diagnostic framework, based on offline training and online diagnosis, is proposed that can greatly reduce the complexity of filtering processes. The effectiveness of the approach is validated by two case studies covering both low speeds (less than 5 r/min) and high speeds (2000 r/min), and involving vibration and acoustic emission analysis. The comparisons with respect to some popular diagnostic methods are explained in detail, which highlights the superiority of the introduced framework.