Data-driven Fault Detection and Diagnosis for Complex Industrial Processes
By S. Qin. 2009
101 citations recorded by the source corpus.
Subfields: Control and Systems Engineering, Mechanical Engineering, Statistics and Probability
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Control and Systems Engineering
In Mechanical Engineering
In Statistics and Probability
- Steven X. Ding
- Shen Yin
- Hao Luo
- Xiaochen Xie
- S. Qin
- Evangelos A. Theodorou
- Qingchao Jiang
- Xue-feng Yan
Citations in the corpus
- Ensemble Bayesian Decision Making with Redundant Deep Perceptual Control Policies (2018, background): “exists an extensive literature on sensor failure detection [12], [13], [14] that demonstrates its application in various fields.”
- A Review on Basic Data-Driven Approaches for Industrial Process Monitoring (2014, background): “A combined index [26], [38] which simultaneously uses both test statistics is proposed, i.”
- Survey on data-driven industrial process monitoring and diagnosis (2012, methodology): “It is further shown in Alcala and Qin (2009) that RBC for SPE differs from the traditional contributions for SPE by a variable-dependent scaling coefficient, thus making RBC and traditional contributions fundamentally different.”