Dataset of Vibration, Temperature and Speed Measurements for Multiple Types of Localized Defects on Spherical Roller Bearings across Multiple Operating Conditions

Dataset of Vibration, Temperature and Speed Measurements for Multiple Types of Localized Defects on Spherical Roller Bearings across Multiple Operating Conditions 核心 · 已核验

atlas:dataset-of-vibration-temperature-and-speed-measurements-for-mult

Description:

This dataset has been created by the ISED group of Politecnico di Torino to address the lack of data available for developing fault detection models and/or predictive maintenance systems for medium/large-sized spherical roller bearings, commonly used in industrial settings.

The data were collected using SKF 22240 CCK/W33 spherical roller bearings through an extensive experimental campaign on the medium/large-scale bearing test rig (capable of testing bearings with an outer diameter up to 420 mm), developed by the ISED research group at Politecnico di Torino (ISED Research Group). The technical details of the test rig can be found in this Paper.

The dataset contains data for individual localized defects applied to one of the four bearings tested simultaneously on the rig, which is capable of independently applying both axial and radial loads.

Dataset Structure:

The dataset is organized into four folders:

Undamaged

InnerRaceDamage

OuterRaceDamage

RollerDamage

The Undamaged folder contains data for all bearings in healthy condition. The other folders contain data from tests where one of the bearings presents a localized defect. The defects, introduced by chip removal, have a diameter of 2 mm and a depth of 0.5 mm, affecting either the inner race (IR), outer race (OR), or roller elements (B). More detailed information about the defect geometry and location can be found in the following publications:

Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification

Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring

Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks

Each folder contains .mat files named according to the following format:

(Nominal_Rotation_Speed)rpm_(Radial_Force)kN_(Axial_Force)k

落地页: https://dx.doi.org/10.5281/zenodo.13913254
许可证: Restricted Use Agreement for Dataset Access and Sharing (判读置信:inferred)
国内可访问性: 国内直连:可达 (2026-07-11 检测) 代理通道:可达 (2026-07-11 检测)
检测口径:lychee 双通道单轮探测;「直连超时」表示检测窗口内未完成,系慢或不稳定证据,不构成封锁证据。
发布年份: 2024
发布方: Zenodo
设备类型: rolling_bearing
PHM 任务: fault_diagnosis

故障工况

传感器

关联论文(13 篇,候选区未经人工核验;candidate_citation = 共引启发式候选关联,非使用断言)

Design of an Innovative Test Rig for Industrial Bearing Monitoring with Self-Balancing Layout introduces
Novelty Detection in Rotating Machinery: Assessment of Unsupervised Machine Learning Models for Medium-Sized Industrial Bearings 2025 · 候选关联(启发式)
Vibration Characteristics in Ball Bearing due to Unbalanced Mass 2025 · 候选关联(启发式)
Toward Autonomous LLM-Based AI Agents for Predictive Maintenance: State of the Art, Challenges, and Future Perspectives 2025 · 候选关联(启发式)
Dynamic Multibody Modeling of Spherical Roller Bearings with Localized Defects for Large-Scale Rotating Machinery 2025 · 候选关联(启发式)
Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring 2023 · uses
Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks 2023 · uses
Novel measurement and control system of universal journal bearing test rig for marine applications 2023 · 候选关联(启发式)
Explainable AI for Machine Fault Diagnosis: Understanding Features’ Contribution in Machine Learning Models for Industrial Condition Monitoring 2023 · 候选关联(启发式)
Zero-Shot Generative AI for Rotating Machinery Fault Diagnosis: Synthesizing Highly Realistic Training Data via Cycle-Consistent Adversarial Networks 2023 · 候选关联(启发式)
Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification 2022 · uses_for_transfer
Screening of Discrete Wavelet Transform Parameters for the Denoising of Rolling Bearing Signals in Presence of Localised Defects 2022 · 候选关联(启发式)
Intelligent Fault Diagnosis of Industrial Bearings Using Transfer Learning and CNNs Pre-Trained for Audio Classification 2022 · 候选关联(启发式)

溯源(PROV,6 条)