PRONOSTIA : An experimental platform for bearings accelerated degradation tests.

By P. Nectoux, R. Gouriveau, Kamal Medjaher, E. Ramasso, Brigitte Chebel-Morello, Noureddine Zerhouni, C. Varnier. 2012

1,105 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/45e53dbcd53ded7134b7c296e119ad455ce11dae

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1. Jinyang Jiao
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Citations in the corpus

• Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings (2015, methodology): “Bearing support shaft of the PRONOSTIA platform [38].”
• A Model-Based Method for Remaining Useful Life Prediction of Machinery (2016, methodology): “Therefore, a score in the IEEE PHM 2012 challenge [37] is used to comprehensively evaluate the performance of the prediction method, which is defined as follows:”
• Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction (2014, unknown): “3b shows a non-degraded bearing were the experiment stopped at the same time of the degraded bearing (Nectoux et al. 2012).”
• Machinery health prognostics: A systematic review from data acquisition to RUL prediction (2018, methodology): “7, which are from the milling dataset [96], the FEMTO bearing dataset [42] and the IMS bearing dataset [81] in Section 2, respectively.”
• Prognostics and Health Management: A Review of Vibration Based Bearing and Gear Health Indicators (2018, background): “First, as shown in Table 1, there are many more studies on health indicators for bearings than for gears, perhaps due to the availability of public bearing run-to-failure data provided by the prognostics Data Repository [27], [134].”
• Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network (2019, methodology): “Therefore, to comprehensively assess the performance of the estimation approach, a score function in the IEEE PHM 2012 challenge [37] is used”
• Bearing Health Monitoring Based on Hilbert–Huang Transform, Support Vector Machine, and Regression (2015, methodology): “In Section III, we investigate the described approach on experimental bearing degradation data taken from the PRONOSTIA platform [36].”
• A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings (2020, methodology): “, PRONOSTIA bearing datasets [39], is used in”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, methodology): “FEMTO dataset [27] was acquired from the PRONOSTIA experimental platform designed by Franche-Comté Electronics, Mechanics, Thermal Processing, Optics-Sciences and Technologies institute (FEMTO), which aimed to provide the experimental data to characterize the degradation of bearings.”
• XJTU-SY Rolling Element Bearing Accelerated Life Test Datasets: A Tutorial (2019, unknown): “P C n L 60 106 10 (1) 式中，L10为基本额定寿命，C 为额定动载荷，n 为 轴承的工作转速，ε 为寿命指数，测试轴承为球轴 承，参考标准取为 3，P 为当量动载荷，当轴承只 承受径向载荷时，可由式(2)计算得到 rPFfP (2) 式中， Pf 为载荷系数， rF 为径向载荷，当无冲击或 轻微冲击时， Pf 取值范围为 1.0～1.2。 2.2 轴承失效形式与振动信号分析 图 3 给出了典型失效类型的轴承图片，从中可 以看出，测试轴承的失效原因包括内圈磨损、保持 架断裂、外圈磨损、外圈裂损等。在 3 种试验工况 下各选取 1 个测试轴承样本，绘制其全寿命周期内 水平、竖”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “health management (PHM) 2012 Data Challenge dataset [95], the Xi’an Jiaotong University and Changxing Sumyoung Tech-”
• An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings (2015, methodology): “An experimental system named PRONOSTIA [36] is shown in Fig.”