Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics
By Hai Qiu, Jay H. Lee, Jing Lin, Gang Yu. 2006
1,408 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering
View Paper: https://www.semanticscholar.org/paper/9fd2c9d752dc06acbdc896193a9b428cea9c11c1
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In Computer Vision and Pattern Recognition
In Control and Systems Engineering
Citations in the corpus
- Wavelets for fault diagnosis of rotary machines: A review with applications (2014, methodology): “performed to detect periodicity of the signal for appropriate decomposition scale selection [31].”
- Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, methodology): “[51] proposed a two-step process to optimise the parameters of a Morlet wavelet filter originally introduced by Lin and Qu [52] for the early detection of bearing defects in an accelerated bearing degradation”
- A smoothness index-guided approach to wavelet parameter selection in signal de-noising and fault detection (2007, result): “Though the comparison of the two methods seems in favor of the filter-based approach [9,10], its performance largely relies on the proper selection of parameters, i.”
- Deep Convolutional Transfer Learning Network: A New Method for Intelligent Fault Diagnosis of Machines With Unlabeled Data (2019, methodology): “2) B: Intelligent Maintenance System (IMS) Bearing Dataset: IMS bearing dataset was generated by the Center for Intelligent Maintenance Systems, which was also collected from a bearing experiment platform [27].”
- XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial (2019, neutral): “XJTU-SY 数据集是国内首次面向全球公开发 布的滚动轴承加速寿命试验数据,在充分分析国外 研究机构发布数据集特点的基础上,进一步优化了 试验设计,发布的全寿命周期数据记录内容详实、 信息丰富,有助于从业人员充分了解与挖掘机械 装备的退化规律,具有较高的理论研究与工程应 用价值。 (1) 对比 L10与真实寿命,验证预测性维护的必 要性。由表 3 的基本额定寿命 L10与真实寿命对比 可以看出,因受到材料、加工工艺和热处理等诸多 随机因素的影响,同一厂家、同一批次生产的滚动 轴承的 L10 寿命与真实寿命存在较大偏差,因此针 对具体的机械装备开展剩余寿命预测是个性问题, 基于大样本条件并依赖”
- Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network (2015, neutral): “For more detailed information about this experiment, interested readers shall refer to [29–31].”