A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings
By Akhand Rai, S. Upadhyay. 2016
735 citations recorded by the source corpus.
Subfields: Automotive Engineering, Control and Systems Engineering, Mechanics of Materials
View Paper: https://www.semanticscholar.org/paper/473ce3679a1f927e327ef8ea30801058c389b76c
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In Control and Systems Engineering
In Mechanics of Materials
Citations in the corpus
- XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial (2019, neutral): “布的《工业大数据标准化白皮书》强调指出,结合 设备状态监测技术、故障诊断技术和计算机网络技 术,开展故障预警、远程监控、远程运维、质量诊 断等大数据分析和预测,形成“制造+服务”的新模 式,是未来提升制造业生产力和竞争力的重要因 素。由此可见,机械装备的 PHM 可有效降低其全 寿命周期成本,提升产品附加值,助力工业产业转 型升级。 机械装备全寿命周期数据是 PHM 领域研究的 基础性资源,这些数据承载着反映装备服役性能完 整退化过程与规律的关键信息。但是,实际中机械 装备全寿命周期数据通常难以获取,主要原因如下: 1 目前我国工业界开展健康监测的意识不足:监测 数据的采集与存储存在着起步晚、”
- Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “In recent years, sensor technology has developed rapidly, and different types of sensors are widely used in bearing fault monitoring [7].”