A particle-filtering approach for on-line fault diagnosis and failure prognosis

By Marcos Eduardo Orchard, George J. Vachtsevanos. 2009

503 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/153c8e4cb3e26b4d681edd7692695659ad18d766

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jing Lin
2. Donghua Zhou
3. Steven X. Ding
4. Wei Qiao
5. Changhua Hu
6. Yaguo Lei
7. Wenbin Wang
8. Naipeng Li

In Control and Systems Engineering

1. Yaguo Lei
2. Naipeng Li
3. Jing Lin
4. Donghua Zhou
5. Steven X. Ding
6. Wei Qiao
7. Changhua Hu
8. Wenbin Wang

Citations in the corpus

• A Survey on Wind Turbine Condition Monitoring and Fault Diagnosis—Part II: Signals and Signal Processing Methods (2015, methodology): “The Bayesian method or particle filtering technique has been used for blade fault diagnosis [93], [94],”
• An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings (2015, methodology): “Based on the Bayesian theory and SIS, PF is particularly effective in estimating states of nonlinear and/or non-Gaussian systems, and it has been widely used in the fields of diagnosis and prognosis [25]–[31].”
• Remaining useful life estimation - A review on the statistical data driven approaches (2011, background): “Wang , 2002b ; Batzel and Swanson , 2009 ; Orchard and Vachtsevanos , 2009 ; Tang et al .”