期刊
ANNUAL REVIEWS IN CONTROL
卷 36, 期 2, 页码 220-234出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.arcontrol.2012.09.004
关键词
Statistical process monitoring; Quality monitoring; Fault detection; Fault diagnosis; Principal component analysis; Partial least squares; Machine learning
资金
- Texas-Wisconsin-California Control Consortium
- Fundamental Research Funds for the Central Universities [RC1101]
This paper provides a state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades. The scope of the problem is described with reference to the scale and complexity of industrial process operations, where multi-level hierarchical optimization and control are necessary for efficient operation, but are also prone to hard failure and soft operational faults that lead to economic losses. Commonly used multivariate statistical tools are introduced to characterize normal variations and detect abnormal changes. Further, diagnosis methods are surveyed and analyzed, with fault detectability and fault identifiability for rigorous analysis. Challenges, opportunities, and extensions are summarized with the intent to draw attention from the systems and control community and the process control community. (C) 2012 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据