Diagnostic monitoring of high-dimensional networked systems via a LASSO-BN formulation

ABSTRACT

Quality control of multivariate processes has been extensively studied in the past decades; however, fundamental challenges still remain due to the complexity and the decision-making challenges that require not only sensitive fault detection but also identification of the truly out-of-control variables. In existing approaches, fault detection and diagnosis are considered as two separate tasks. Recent developments have revealed that selective monitoring of the potentially out-of-control variables, identified by a variable selection procedure combined with the process monitoring method, could lead to promising performances. Following this line, we propose the diagnostic monitoring that takes an additional step on from the selective monitoring idea and directs the monitoring effort on the potentially out-of-control variables. The identification of the truly out-of-control variables can be achieved by integrating the process monitoring formulation with process cascade knowledge represented by a Bayesian Network. Computationally efficient algorithms are developed for solving the optimization formulation with connection to the Least Absolute Shrinkage and Selection Operator (LASSO) problem being identified. Both theoretical analysis and extensive experiments on a simulated data set and real-world applications are conducted that show the superior performance.

KEYWORDS:

• Multivariate process monitoring
• diagnosis
• LASSO
• Bayesian network

Funding

The authors acknowledge support from the National Science Foundation under Grant CMMI-1505260.

Additional information

Notes on contributors

Yan Jin

Yan Jin received his B.S. degree in geophysics from the University of Science and Technology of China, Hefei, China, in 2011. He is currently pursuing a Ph.D. degree in industrial and systems engineering at the University of Washington Seattle. His research interests include machine learning, optimization, and quality engineering.

Shuai Huang

Shuai Huang received his B.S. in statistics from the University of Science and Technology of China in 2007 and his Ph.D. in industrial engineering from the Arizona State University in 2012. He is currently an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Washington. His research interest lies in statistical learning and data mining with applications in healthcare and manufacturing. He is a member of INFORMS, IIE, IEEE, and ASQ.

Guan Wang

Guan Wang received his B.S. in computer science from the University of Science and Technology of China in 2007 and his Ph.D. in computer science from the University of Illinois at Chicago in 2014. He is now a Staff Machine Learning Engineer at NIO Automotive. Previously he was a senior data scientist at LinkedIn. His major work is on large-scale training and small-scale model deployment for various statistical inference and deep models.

Houtao Deng

Houtao Deng received his B.S. in automation from the Central South University in 2004, his M.S. in systems engineering from Huazhong University of Science & Technology in 2007, and his Ph.D. in industrial engineering from the Arizona State University in 2011. He is currently a Data Scientist at Instacart. He works on problems related to data mining, system monitoring, and optimization.