The process faults of shaft furnace roasting processes, e.g. fire-emitting, flame-out, under-reduction, and over-reduction are undesirable for stable operation of the processes. The processes share multiple complexities such as multi-variate and strong correlations, which make it difficult to diagnose the faults using model-based or knowledge-based methods. In this paper, a data-driven fault diagnosis method for shaft furnace roasting processes is presented based on reconstruction and reconstruction-based contribution. The proposed method exploits historical faulty data to derive fault directions to identify ongoing faults with the help of additional explanation from contribution plots. A case study on a simulation system of shaft furnace roasting processes illustrates the effectiveness of the proposed method.
|Number of pages||6|
|Journal||IFAC Proceedings Volumes|
|Publication status||Published - Aug 2014|
|Event||19th IFAC World Congress on International Federation of Automatic Control, IFAC 2014 - , South Africa|
Duration: 24 Aug 2014 → 29 Aug 2014
Bibliographical noteISBN: 9783902823625 <br/>This work was supported in part by the Natural Science Foundation of China (61304107, 61020106003, 61290323), the China Postdoctoral Science Foundation funded project (2013M541242), the 111
Project of Ministry of Education of China (B08015), and the IAPI Fundamental Research Funds (2013ZCX02-01).