Name
Title | ||
|---|---|---|
A Brief Survey Of Different Statistics For Detecting Multiplicative Faults In Multivariate Statistical Process Monitoring |
Abstract | ||
|---|---|---|
The recent explosion in different statistics for fault detection has meant that the practitioner is faced with the unenviable job of determining which to use in a given situation. Thus, this paper seeks to investigate the different test statistics that can be applied to detect multiplicative faults for multivariate Gaussian-distributed processes in order to provide the practitioner with some guidance. Three groups of methods are: traditional methods (e.g., T-2 and Q statistics) and their extensions; the Wishart distribution-based methods; and those methods that are created in information and communication fields to describe the characteristics of measurement variance and covariance (e.g., mutual information and Kullback-Leibler divergence). Then, greater details on their interconnections and comparisons are presented and their performance for detecting multiplicative faults is evaluated and demonstrated using numerical simulations. |
Year | Venue | Field |
|---|---|---|
2016 | 2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC) | Multiplicative function,Multivariate statistics,Fault detection and isolation,Computer science,Mutual information,Artificial intelligence,Statistics,Wishart distribution,Statistical hypothesis testing,Principal component analysis,Machine learning,Covariance |
DocType | ISSN | Citations |
Conference | 0743-1546 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kai Zhang | 1 | 71 | 7.38 |
| Yuri A. W. Shardt | 2 | 37 | 7.10 |
| Zhiwen Chen | 3 | 42 | 12.85 |
| Steven X. Ding | 4 | 1792 | 124.79 |
| Kaixiang Peng | 5 | 19 | 2.80 |