Bearing Datasets 候选 · 未审核

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"These datasets include four distinct tests designed to validate an algorithm's ability to assess the health of bearings in rotating machines, focusing on condition monitoring. The targets in these tests indicate the moment when the condition of the bearing changes. The four tests consist of: synthetically generated signals, real-world measurements from a rotating machine at the Federal University of Uberl\u00e2ndia, and benchmark datasets from Case Western Reserve University (CWRU) and the HUST bearing dataset.Each test follows a similar structure: the system maintains a constant condition for a period, then transitions to a new condition, or returns to a previously observed one, and stays in that new state for another period before changing again. The aim of these tests is to evaluate the model's ability to correctly identify distinct conditions and detect the emergence of new conditions promptly, i.e., to determine if there is a significant delay in recognizing condition changes.Test 1 involves a synthetically generated signal by the finite element method with a proximity sensor, sample frequency of 1000 Hz, and duration of 3600 seconds, specifically designed to exhibit unbalance and misalignment variations at some specific times. The defect intensities vary across eight distinct conditions, with one of the conditions repeated to evaluate the clustering algorithm\u2019s memory capacity. Additionally, the test includes a run-up and run-down period during which the signal remains unstable.Test 2 is composed of a signal with a proximity sensor, sample frequency of 10,000 Hz, and duration of 1370 seconds, showcasing variations in speed. This signal was collected from a magnetic bearing bench located in the LMEst laboratory at the Federal University of Uberl\u00e2ndia (UFU).The machine operates under five different rotational speed conditions, including a baseline state. After reaching the fifth condition, it returns to two previously observed states.Test 3 comprises

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落地页

https://ieee-dataport.org/documents/bearing-datasets

许可证

CC-BY-4.0 (置信:未知)

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

IEEE DataPort

设备类型

rolling_bearing

PHM 任务

condition_monitoring anomaly_detection

故障工况

传感器

运行工况