Automatic State Machine Reconstruction From Legacy Programmable Logic Controller Using Data Collection and SAT Solver

Nowadays an increasing number of industries are considering moving toward being Industry 4.0 compliant. But this transition is not straightforward: transfer to new system can lead to significant production downtime, resulting in delays and cost overruns. The best way is systematic seamless transitio...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2020-12, Vol.16 (12), p.7821-7831
Main Authors: Chivilikhin, Daniil, Patil, Sandeep, Chukharev, Konstantin, Cordonnier, Anthony, Vyatkin, Valeriy
Format: Article
Language:English
Subjects:
Algorithms
automata
Automation
Boolean algebra
Control system synthesis
Data collection
Downtime
Hardware
IEC Standards
Industries
inference algorithms
Modular equipment
Noise measurement
PLC
Programmable logic controllers
reverse engineering
SAT
software
Software algorithms
Solvers
State machines
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Summary:Nowadays an increasing number of industries are considering moving toward being Industry 4.0 compliant. But this transition is not straightforward: transfer to new system can lead to significant production downtime, resulting in delays and cost overruns. The best way is systematic seamless transition to newer and advanced technologies that Industry 4.0 offers. This article proposes a framework based on automatic synthesis methods that learns the behavior of an existing legacy programmable logic controller (PLC) and generates state machines that can be incorporated into IEC 61499 function blocks. Proposed algorithms are based on Boolean satisfiability (SAT) solvers. The first algorithm accepts a set of noisy PLC traces and produces a set of candidate state machines that satisfy the traces. The second algorithm accepts error-free traces and synthesizes a modular controller that may be distributed across several physical devices. The toolchain architecture is exemplified on a laboratory scale Festo mechatronic system.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2020.2992235