Name
Abstract | ||
|---|---|---|
Programmable Logic Controllers are widely used in industry. Reliable PLCs are vital to many critical applications. This paper presents a novel symbolic approach for analysis of PLC systems. The main components of the approach consists of: (1) calculating the uncertainty characterization of the PLC systems, (2) abstracting the PLC system as a Hidden Markov Model, (3) solving the Hidden Markov Model using domain knowledge, (4) integrating the solved Hidden Markov Model and the uncertainty characterization to form an integrated (regular) Markov Model, and (5) harnessing probabilistic model checking to analyze properties on the resultant Markov Model. The framework provides expected performance measures of the PLC systems by automated analytical means without expensive simulations. Case studies on an industrial automated system are performed to demonstrate the effectiveness of our approach. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-24559-6_10 | ICFEM |
Keywords | Field | DocType |
programmable logic controller,novel symbolic approach,resultant markov model,reliable plcs,plc system,domain-driven probabilistic analysis,industrial automated system,uncertainty characterization,automated analytical mean,markov model,hidden markov model,programmable logic controllers | Domain knowledge,Markov model,Computer science,Theoretical computer science,Probabilistic analysis of algorithms,Programmable logic controller,Artificial intelligence,Hidden Markov model,Probabilistic model checking,Markov algorithm | Conference |
Volume | ISSN | Citations |
6991 | 0302-9743 | 1 |
PageRank | References | Authors |
0.38 | 7 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hehua Zhang | 1 | 109 | 12.65 |
| Yu Jiang | 2 | 346 | 56.49 |
| William N. N. Hung | 3 | 304 | 34.98 |
| Xiaoyu Song | 4 | 318 | 46.99 |
| Ming Gu | 5 | 554 | 74.82 |