New Correlation-function-based Method and Algorithm of Signal Processing for Fault Diagnosis of Rolling Bearings

Rolling bearings are an important part of the mechanical systems used in various industries. Their reliable operation has a significant impact on equipment performance and safety of production processes. However, bearings wear, damage and other defects can lead to serious consequences, including accidents, equipment downtime and increased maintenance costs. Effective diagnosis and monitoring of the technical condition of rolling bearings is strategically important for preventing accidents, improving equipment efficiency and reducing maintenance costs. This paper presents a short critical review of the existing methods of rolling bearings diagnostics, and proposes new correlation-function-based method and algorithm of signal processing for the diagnosis of rolling bearings defects. Also, the paper presents the results of experimental testing of the proposed method and algorithm on real experimental data, which validate the high reliability of rolling bearings diagnosis.