Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/35542
Title: Effective Fault Diagnosis in Chemical Plants By Integrating Multiple Methodologies
Authors: KAUSHIK GHOSH
Keywords: Fault Detection and Identification, Process Monitoring, Data driven FDI approaches, Negative Selection Algorithm, Variable selection, Hierarchical FDI
Issue Date: 20-Jan-2012
Source: KAUSHIK GHOSH (2012-01-20). Effective Fault Diagnosis in Chemical Plants By Integrating Multiple Methodologies. ScholarBank@NUS Repository.
Abstract: Fault detection and identification (FDI) are important problems in process engineering. Early detection and precise identification of process faults is essential to prevent off-spec products and also in many cases to prevent serious accidents. A majority of the methods proposed in the literature employ a single monolithic monitoring strategy. But the sheer size and complexity of modern chemical plants make it difficult to apply these monolithic strategies. The core objective of this thesis is to achieve improved FDI performance by combing multiple FDI methods. Various types of multiple FDI methods based approaches consisting of homogeneous (of same types) or heterogeneous (of different types) methods are explored and developed in this thesis. First, an immune system inspired negative selection algorithm (NSA) based approach for fault detection is presented. The proposed approach is a generic one and can be applied for monitoring and fault diagnosis of both continuous as well as batch and transient operations. A scheme for estimation of the self-radius, an important parameter for generating the detectors in NSA, is also proposed. Next, the importance of variable selection for improving the performance of PCA based monitoring method is illustrated through analytical derivations. Based on these insights, a multiple PCA model based approach is proposed, wherein each PCA model uses the most affected subset of variables, called key variables, of a fault. A metric is proposed to systematically identify the key variables for each fault. Next, both flat and hierarchical organizations of FDI methods are studied. In flat architecture, the scopes of all the FDI methods are same and all of them supervise the entire process. The effectiveness of majority voting and Bayesian based decision fusion strategies for combining FDI methods organised in Flat architecture are evaluated. In hierarchical architecture different FDI methods are deployed to supervise the process at different levels of process hierarchy, such as equipment level, section level, unit level. Results from such hierarchical FDI methods cannot be combined suitably through voting or Bayesian based fusion scheme. A Dempster-Shafer evidence theory based decision fusion strategy is proposed to combine the outputs from Hierarchical FDI methods in an efficient manner. All these developments have been tested extensively using various case studies ? simulated CSTR-distillation column, lab scale distillation column, simulated CSTR, simulated fed-batch operation, the Tennessee Eastman challenge problem.
URI: http://scholarbank.nus.edu.sg/handle/10635/35542
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
GhoshK.pdf5.58 MBAdobe PDF

OPEN

NoneView/Download

Page view(s)

175
checked on Dec 2, 2017

Download(s)

424
checked on Dec 2, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.