2018 IEEE Industry Applications Society Annual Meeting (IAS) 2018
DOI: 10.1109/ias.2018.8544586
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A Data-Driven Approach for Bearing Fault Prognostics

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Cited by 45 publications
(3 citation statements)
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References 28 publications
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“…Ahmad et al (2017) implemented a hybrid PHM approach by training an adaptive predictive model on the NASA bearing degradation data and then adopting a regression-based approach to predict the RUL. Other researchers such as Jin et al (2018) used a self-organizing map (SOM) to train the degradation model for the bearings data from the FEMTO-ST PRONOSTIA test bed and then adopted an unscented Kalman Filters to estimate RUL using the trained model. In general, the hybrid approach combines the use of degradation data to train an AI algorithm to learn the parameters of a physical model, and then uses the learned model along with statistical or other approaches to make extrapolations or predictions.…”
Section: Hybrid/fusionmentioning
confidence: 99%
“…Ahmad et al (2017) implemented a hybrid PHM approach by training an adaptive predictive model on the NASA bearing degradation data and then adopting a regression-based approach to predict the RUL. Other researchers such as Jin et al (2018) used a self-organizing map (SOM) to train the degradation model for the bearings data from the FEMTO-ST PRONOSTIA test bed and then adopted an unscented Kalman Filters to estimate RUL using the trained model. In general, the hybrid approach combines the use of degradation data to train an AI algorithm to learn the parameters of a physical model, and then uses the learned model along with statistical or other approaches to make extrapolations or predictions.…”
Section: Hybrid/fusionmentioning
confidence: 99%
“…Therefore, anomaly detection and fault diagnosis of wind turbines through SCADA data analysis becomes a hot topic. Generally, based on the SCADA data, a normal behavior model (NBM) [9] can be built and quantitative anomaly indicators [10] can be constructed for condition monitoring of wind turbines. For example, a NBM based on principal component analysis and support vector regression was established to detect generator fault based on power, voltages, and currents SCADA data [11].…”
Section: Introductionmentioning
confidence: 99%
“…Categorizing these methods in classes is quite difficult because of a wide variety of applications. However, Vachtsevanos et al, in [3], classified prognostic methods into three main categories, prognosis based on: (i) experience, (ii) data, and (iii) model [4], [5], [6], [7], [8], [9], [10]. However, it is possible to integrate experience-based prognostic into data-driven prognosis since it handles system data.…”
Section: Introductionmentioning
confidence: 99%