Work

A review on bearing fault diagnosis methods with emphasis on machine learning and deep learning applications

Year: 2026

Type: article

Abstract: To improve plant efficiency, reduce unnecessary costs, and enhance safety, industries must adopt effective predictive strategies for supply management optimization. Since more than 90 % of machines rely on rolling element bearings, their failure often results in significant machinery breakdowns. In fact, a considerable share of failures in manufacturing systems can be attributed to bearing defects. This review focuses on examining and evaluating various diagnostic methods for rolling element bearings, comprising conventional approaches, machine learning techniques, and advanced deep learning methods. By delivering a comprehensive understanding of these detection strategies, the study aims to support better implementation practices and offer fresh insights for future research directed toward enhancing the reliability and performance of rolling element bearings.

Source: Next Materials

DOI: 10.1016/j.nxmate.2026.102040

Authors: Prachi Dixit, Pawan Pingle, Adinath Funde, Kedarnath Chaudhary, Vikas Kanake

Institutions: Savitribai Phule Pune University, Bharati Vidyapeeth (Deemed to be University), COEP Technological University

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,280