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 detec...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 PSO-OCSVM classifier.展开更多
基金This work is supported by the Program of Hainan Association for Science and Technology Plans to Youth R&D Innovation(Grant No.QCXM201910)the Natural Science Foundation of Liaoning Province(Grant No.2019-MS-149),the Social Science Planning Foundation of Liaoning Province(Grant No.L18AGL007)+1 种基金the National Natural Science Foundation of China(Grant Nos.61802092,51704138 and 61501447)the Scientific Research Setup Fund of Hainan University(Grant No.KYQD(ZR)1837).
文摘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 PSO-OCSVM classifier.
