In order to accurately recognize the degradation state of rolling bearing, a hybrid method combining Genetic Algorithm(GA)and a Support Vector Machine (SVM) was proposed,and the model for degradation state recognition of rolling bearing was constructed. Firstly the feature vectors of degradation state were extracted through the combination of GA and SVM from statistical characteristic. Then the degradation state probability distribution and historical remn ant life of rolling bearing are calculated to deter mine the optimal number of degradation state, whi ch is employed to construct the SVM model for deg radation state recognition. Finally extracted the characteristic vectors which have been optimized and deleted by GA from the test data of different degradation states, and then using the character ristic vectors as the input of SVM which parame ters has been optimized by GA to identify the degradation state of rolling bearing.The analytical results for full lifetime datasets of a certain bearing demonstrate the validity of the method.

Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data sparsity and cold start, utilizing the ample semantic relation in knowledge graph. If we use RDF data, which is distributed by the E and P (Exploration and Development) database based on the petroleum E and P, to verify the validity of the algorithm, the result shows that collaborative filtering algorithm based on knowledge graph can build the users’ potential intentions by knowledge graph. It is enlightening to query the information of users. In this way, it expands the mind of users to accomplish the goal of recommendation. In this paper, a collaborative filtering algorithm based on domain knowledge atlas is proposed. By using knowledge graph to effectively classify and describe domain knowledge, the problems are solved including clustering and the cold start in traditional collaborative filtering recommendation algorithm. The better recommendation effect has been achieved.

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