Abstract
Programmable Logic Controllers (PLC) are widely used in industry. The reliability of the PLC is vital to many critical applications. This paper presents a novel approach to the symbolic analysis of PLC systems. The approach includes, (1) calculating the uncertainty characterization of the PLC system, (2) abstracting the PLC system as a Hidden Markov Model, (3) solving the Hidden Markov Model with domain knowledge, (4) combining the solved Hidden Markov Model and the uncertainty characterization to form a regular Markov model, and (5) utilizing probabilistic model checking to analyze properties of the Markov model. This framework provides automated analysis of both uncertainty calculations and performance measurements, without the need for expensive simulations. A case study of an industrial, automated PLC system demonstrates the effectiveness of our work.
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