Work

Mixed fault dataset of bearings and gearboxes under variable operating conditions

Year: 2025

Type: data-paper

Abstract: Bearings and gears, as key rotating components, are prone to damage when operating under varying conditions with heavy loads, leading to equipment chain failures. In response to the demand for health monitoring based on vibration signals, this paper has constructed a vibration signal dataset for multiple fault types under varying operating conditions: the bearing dataset covers single faults such as inner ring, outer ring, rolling elements, and cage, as well as combined faults of inner and outer rings; the gearbox dataset includes typical defects such as tooth surface wear, broken teeth, and eccentricity; an additional mixed fault dataset of bearings and gearboxes is also provided. These datasets simulate real industrial scenarios through multiple dimensions, aiming to provide a reliable verification benchmark for intelligent diagnostic algorithms and improve the fault identification accuracy and model generalization ability under complex operating conditions.

Source: Data in Brief

DOI: 10.1016/j.dib.2025.112187

Authors: Yinchuan Hou, Jian Tang, Jing Zhang, Pengfei Pang, Hao Zhang, Xinxin Li

Institution: PLA Army Engineering 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: $1,220