Symbolic Analysis of Programmable Logic Controllers

被引：14
|
作者：
Zhang, Hehua [1 ]
Jiang, Yu [2 ]
Hung, William N. N. [3 ]
Song, Xiaoyu [4 ]
Gu, Ming [1 ]
Sun, Jiaguang [1 ]
机构：
[1] Tsinghua Univ, Tsinghua Informat Sci & Technol Natl Lab TNLIST, Sch Software, Beijing 100084, Peoples R China
[2] Tsinghua Univ, Tsinghua Informat Sci & Technol Natl Lab TNLIST, Dept Comp Sci & Technol, Beijing 100084, Peoples R China
[3] Synopsys Inc, Mountain View, CA 94035 USA
[4] Portland State Univ, Elect & Comp Engn ECE Dept, Portland, OR 97207 USA
来源：
IEEE TRANSACTIONS ON COMPUTERS | 2014年 / 63卷 / 10期
关键词：
PLC; Hidden Markov model; probabilistic analysis; AUTOMATIC VERIFICATION; MARKOV-MODELS; RELIABILITY;
D O I：
10.1109/TC.2013.124
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：2563 / 2575
页数：13
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