NOVIC+ Motor compound fault dataset (part 1) 核心 · 已核验

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https://www.sciencedirect.com/science/article/pii/S0888327025014876?dgcid=coauthor

Please cite above paper when you use this dataset...!

There are part1, part2 and part3. Please download all the dataset.

Part2: https://zenodo.org/records/15743009

Part3: https://zenodo.org/records/15743374

Submitted to Mechanical Systems and Signal Processing on May 9th, 2025

The increasing complexity of rotating machinery and the diversity of operating conditions, such as rotating speed and varying torques, have amplified the challenges in fault diagnosis in scenarios requiring domain adaptation, particularly involving compound faults. This study addresses these challenges by introducing a novel multi-output classification (MOC) framework tailored for domain adaptation in partially labeled (PL) target datasets. Unlike conventional multi-class classification (MCC) approaches, the proposed MOC framework classifies the severity levels of compound faults simultaneously. Furthermore, we explore various single-task and multi-task architectures applicable to the MOC formulation-including shared trunk and cross-talk-based designs-for compound fault diagnosis under PL conditions. Based on this investigation, we propose a novel cross-talk layer structure that enables selective information sharing across diagnostic tasks, effectively enhancing classification performance in compound fault scenarios. In addition, frequency-layer normalization was incorporated to improve domain adaptation performance on motor vibration data. Compound fault conditions were implemented using a motor-based test setup, and the proposed model was evaluated across six domain adaptation scenarios. The experimental results demonstrate its superior macro F1 performance compared to baseline models. We further showed that the proposed mode's structural advantage is more pronounced in compound fault settings through a single-fault comparison. We also found that frequency-layer normalization fits the fault diagnosis tas

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落地页

https://zenodo.org/records/15743425

许可证

CC-BY-4.0 (置信:verified_official)

国内可访问性

国内直连:可达 (2026-07-11 检测) 非直连:可达 (2026-07-11 检测)
「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

2025

发布方

Zenodo

设备类型

rolling_bearing rotor_system

PHM 任务

fault_diagnosis fault_severity_estimation

分发点

故障工况

传感器

运行工况