This is an interim version of our Electronic Legal Deposit Catalogue-eJournals and eBooks while we continue to recover from a cyber-attack.
Distributionally Robust Fault Detection by using Kernel Density Estimation⁎This work was supported in part by the National Natural Science Foundation of China under Grants (61873149 and 61733009), the Research Fund for the Taishan Scholar Project of Shandong Province of China, the Beijing Natural Science Foundation under grant 4202045, and the Fundamental Research Funds for the Central Universities under Grant FRF-TP-19-032A2. Issue 2 (2020)
Record Type:
Journal Article
Title:
Distributionally Robust Fault Detection by using Kernel Density Estimation⁎This work was supported in part by the National Natural Science Foundation of China under Grants (61873149 and 61733009), the Research Fund for the Taishan Scholar Project of Shandong Province of China, the Beijing Natural Science Foundation under grant 4202045, and the Fundamental Research Funds for the Central Universities under Grant FRF-TP-19-032A2. Issue 2 (2020)
Main Title:
Distributionally Robust Fault Detection by using Kernel Density Estimation⁎This work was supported in part by the National Natural Science Foundation of China under Grants (61873149 and 61733009), the Research Fund for the Taishan Scholar Project of Shandong Province of China, the Beijing Natural Science Foundation under grant 4202045, and the Fundamental Research Funds for the Central Universities under Grant FRF-TP-19-032A2.
Abstract: In this paper, a method of distributionally robust fault detection (FD) is proposed for stochastic linear discrete-time systems by using the kernel density estimation (KDE) technique. For this purpose, an H2 optimization-based fault detection filter is constructed for residual generation. Towards maximizing the fault detection rate (FDR) for a prescribed false alarm rate (FAR), the residual evaluation issue regarding the design of residual evaluation function and threshold is formulated as a distributionally robust optimization problem, wherein the so-called confidence sets are constituted to model the ambiguity of distribution knowledge of residuals in fault-free and faulty cases. A KDE based solution, robust to the estimation errors in probability distribution of residual caused by the finite number of samples, is further developed to address the targeting problem such that the residual evaluation function, threshold as well as the lower bound of FDR can be achieved simultaneously. A case study on a vehicle lateral control system demonstrates the applicability of the proposed FD method.