Dynamic Process Quality Anomaly Recognition Method Based on Feature Reduction and Improved Support Vector Machine
Industrial equipment operating at high speeds generates a large amount of dynamic data streams that reflect fluctuations in its production process and operational status.However,due to the complexity of dynamic process big data,it is difficult to achieve a high recognition accuracy for any type of abnormal pattern relying solely on a single type of data feature.The urgent problem to be solved is how to fully integrate,screen,and efficiently utilize information.At present,intelligent technology has become one of the effective methods to solve complex production processes with characteristics such as nonlinearity,multiple inputs,and multiple outputs,due to its advantage of not requiring the establishment of precise mathematical models.Therefore,classifiers used for dynamic process quality anomaly pattern recognition have evolved from early rule-based expert system judgment to the combination of support vector machines,particle swarm optimization algorithms,rough sets,and other technologies.The RS-GA-SVM model proposed in this article is a computationally simple and highly accu-rate method for identifying quality anomaly patterns,which is beneficial for real-time quality monitoring and fault diagnosis in automated manufacturing industries such as petroleum and chemical.In order to effectively reduce the dimensionality of features and improve the recognition accuracy of abnormal patterns in dynamic processes,this paper combines feature reduction with improved support vector machines to identify abnormal patterns in dynamic process quality.Firstly,it uses Monte Carlo simulation to generate a dynamic data stream of the production process,including training samples and test samples.The test samples are mainly used for estimating the performance of SVM.Next,it extracts 16 statistical features and 7 shape features that can characterize quality anomaly patterns from the raw data.Secondly,this article uses rough sets(RS)to reduce the feature set,eliminate redundant and interfering features,and obtain the optimal attribute set.Subsequently,it uses genetic algorithm(GA)to find the optimal parameters for support vector machine(SVM),which can reduce the subjectivity of SVM in parameter selection.Finally,it uses the GA-SVM model to identify quality anomaly patterns,and compares the recognition accuracy with other similar models to verify the effective-ness and applicability of the proposed recognition model.The results show that the statistical features and shape characteristics used in this article can combine multiple quality mode features,integrating the classification advantages of different features on different quality modes.Secondly,the 12 features obtained after rough set filtering have strong ability to distinguish whether there are abnormal conditions in dynamic processes,which can effectively reduce the input dimension of support vector machines.Finally,this article uses simulation experiments to compare the recognition accuracy with other models of the same type.The results show that the genetic algorithm optimized support vector machine has significantly higher accuracy in identifying quality anomaly patterns than other models,indicating that the RS-GA-SVM model proposed has good recognition accuracy and robustness,and can effectively monitor dynamic processes.In future,due to uncertain factors in the actual production process that may lead to the emergence of new quality anomaly patterns or multiple concurrent quality patterns,it is necessary to conduct in-depth research on these unknown quality anomaly models.Meanwhile,this article only uses the equal frequency interval partitio-ning method to reduce the combination of serial features,and rough sets also include other attribute reduction methods.Therefore,there is still a lot of research space for feature reduction methods based on rough sets.
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