State-Based Control Feature Extraction for Effective Anomaly Detection in Process Industries

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.

Compared Insights on Machine-Learning Anomaly Detection for Process Control Feature

Anomaly detection is becoming increasingly significant in industrial cyber security,and different machine-learning algorithms have been generally acknowledged as various effective intrusion detection engines to successfully identify cyber attacks.However,different machine-learning algorithms may exhibit their own detection effects even if they analyze the same feature samples.As a sequence,after developing one feature generation approach,the most effective and applicable detection engines should be desperately selected by comparing distinct properties of each machine-learning algorithm.Based on process control features generated by directed function transition diagrams,this paper introduces five different machine-learning algorithms as alternative detection engines to discuss their matching abilities.Furthermore,this paper not only describes some qualitative properties to compare their advantages and disadvantages,but also gives an in-depth and meticulous research on their detection accuracies and consuming time.In the verified experiments,two attack models and four different attack intensities are defined to facilitate all quantitative comparisons,and the impacts of detection accuracy caused by the feature parameter are also comparatively analyzed.All experimental results can clearly explain that SVM(Support Vector Machine)and WNN(Wavelet Neural Network)are suggested as two applicable detection engines under differing cases.

Experimental Study of Monitoring and Controlling of Composite Cure Process in Autoclave Featured with Fiber Optic Sensor

With the aid of the latest fiber optic sensing technology parameters in the cure process of ther- mosetting resin-matrix composite, such as temperature, viscosity,void and residual stress, can be monitored entirely and efficiently.In this paper, experiment results of viscosity measurement in composite cure process in autoclave using fiber optic sensors are presented. Based on the sensed information, a computer program is utilized to control the cure process. With this technology, the cure process becomes more apparent and controllable, which will greatly improve the cured products and reduce the cost.

Measuring air traffic complexity based on small samples

Air traffic complexity is an objective metric for evaluating the operational condition of the airspace. It has several applications, such as airspace design and traffic flow management.Therefore, identifying a reliable method to accurately measure traffic complexity is important. Considering that many factors correlate with traffic complexity in complicated nonlinear ways,researchers have proposed several complexity evaluation methods based on machine learning models which were trained with large samples. However, the high cost of sample collection usually results in limited training set. In this paper, an ensemble learning model is proposed for measuring air traffic complexity within a sector based on small samples. To exploit the classification information within each factor, multiple diverse factor subsets（FSSs） are generated under guidance from factor noise and independence analysis. Then, a base complexity evaluator is built corresponding to each FSS. The final complexity evaluation result is obtained by integrating all results from the base evaluators. Experimental studies using real-world air traffic operation data demonstrate the advantages of our model for small-sample-based traffic complexity evaluation over other stateof-the-art methods.