Abstract
In model-based diagnosis (MBD), structural models can provide useful information for fault diagnosis and fault-tolerant control design. In particular, they are known for supporting the design of analytical redundancy relations (ARRs) which are widely used to generate residuals for diagnosis. On the other hand, systems are increasingly complex whereby it is necessary to develop decentralized architectures to perform the diagnosis task. Decentralized diagnosis is of interest for on-board systems as a way to reduce computational costs or for large geographically distributed systems that require to minimizing data transfer. Decentralized solutions allow proper separation of industrial knowledge, provided that inputs and outputs are clearly defined. This paper builds on the results of [1] and proposes an optimized approach for decentralized fault-focused residual generation. It also introduce the concept of Fault-Driven Minimal Structurally-Overdetermined set (FMSO) ensuring minimal redundancy. The method decreases communication cost involved in decentralization with respect to the algorithm proposed in [1] while still maintaining the same isolation properties as the centralized approach as well as the isolation on request capability.
Highlights
With increasing complexity of industrial processes, the requirement for reliability, availability and security is growing significantly
If a Shared Fault-Driven Minimal Structurally-Overdetermined set (FMSO) or Stored minimal structurally overdetermined (MSO) set is not sent at hierarchical level, it is possible that all minimal TES (MTES) are not obtained
In the case of ACS local diagnoser, the results demonstrate the efficiency of FMSO sets to ensure minimal redundancy compared with MTES sets: using the algorithm, as it discussed in [1], the structural redundancy for this MTES set is equal to 10
Summary
With increasing complexity of industrial processes, the requirement for reliability, availability and security is growing significantly. Along the same idea, [7] proposes a hierarchical framework that exploits different local decisions These ideas are taken into account for our approach of decentralization using the idea of isolation on request. This paper is organized as follows: Section 2 introduces the basic concepts of analytical redundancy and presents the structural approach. Structural analysis is able to identify those components of the system which are or are not able to be monitored, to provide design approaches for analytic redundancy based residuals, to suggest alarm filtering strategies, as well as to identify those components whose failure can be tolerated through reconfiguration [8]. Definition 2 (ARR for M(z,x)): The structure of a system is an abstract representation of which variables are involved in different equations and compose the model of the system. The bipartite graph represents which unknown variables are involved in the equations models of the system
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
