@article{li_data-driven_2016,
	title = {Data-driven root cause diagnosis of faults in process industries},
	volume = {159},
	issn = {0169-7439},
	url = {https://www.sciencedirect.com/science/article/pii/S0169743916303203},
	doi = {10.1016/j.chemolab.2016.09.006},
	abstract = {Data driven fault detection and diagnosis methods become more and more attractive in modern industries especially process industries. They can not only guarantee safe operation but also greatly improve product quality. For example, dynamic principal component analysis models and reconstruction based contribution are widely applicable in many occasions. However, there is one issue which does not receive enough attention, namely locating the root cause of a fault when it occurs. In this paper, a framework of root cause location is proposed to address this issue, including both stationary faults and nonstationary faults. A case study on Tennessee Eastman process is used to demonstrate the usage and effectiveness of these approaches. Results show the proposed framework is valid.},
	language = {en},
	urldate = {2022-05-02},
	journal = {Chemometrics and Intelligent Laboratory Systems},
	author = {Li, Gang and Qin, S. Joe and Yuan, Tao},
	month = dec,
	year = {2016},
	keywords = {Dynamic principal component analysis, Dynamic time warping, Granger causality analysis, Reconstruction based contribution, Root cause diagnosis},
	pages = {1--11},
}

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