@Article{cmc.2020.09692,
AUTHOR = {Ming Wan, Jinfang Li, Jiangyuan Yao, Rongbing Wang, Hao Luo},
TITLE = {State-Based Control Feature Extraction for Effective Anomaly  Detection in Process Industries},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {63},
YEAR = {2020},
NUMBER = {3},
PAGES = {1415--1431},
URL = {http://www.techscience.com/cmc/v63n3/38884},
ISSN = {1546-2226},
ABSTRACT = {In process industries, the characteristics of industrial activities focus on the 
integrality and continuity of production process, which can contribute to excavating the 
appropriate features for industrial anomaly detection. From this perspective, this paper 
proposes a novel state-based control feature extraction approach, which regards the finite 
control operations as different states. Furthermore, the procedure of state transition can 
adequately express the change of successive control operations, and the statistical 
information between different states can be used to calculate the feature values. 
Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural 
Network), which are optimized by PSO (Particle Swarm Optimization) and GA (Genetic 
Algorithm) respectively, are introduced as alternative detection engines to match with our 
feature extraction approach. All experimental results clearly show that the proposed 
feature extraction approach can effectively coordinate with the optimized classification 
algorithms, and the optimized GA-BPNN classifier is suggested as a more applicable 
detection engine by comparing its average detection accuracies with the ones of PSOOCSVM classifier.},
DOI = {10.32604/cmc.2020.09692}
}