The helath condition of rotor has been greatly concerned in rotating machinery. But for the lack of information, it is very difficult to judge the actual conditon. Based on the fuzzy and grey characteristics between faults and symptoms, a new method integrated with fuzzy clustering and grey relation analysis was put forward to identify the condition of rotor system. Firstly, eight features, such as average value, peak-peak value, variance value, virtual value and etc., were extracted from the vibration signal of rotor system. Then, fuzzy C-means algorithm was used to cluster forty samples into 4 clusters, meanwhile, the clustering center was acquired and regarded as standard pattern matrix. Finally, the grey relation degree was calculated between pattern to be inspected and the standard pattern matrix. Using this method, the unbalanced conditions of rotor system was precisely identified, which shows that the integrated method is valid and practicable.
In oil-delivery pumps, impeller failure is a common cause leading to excessive vibration. This paper is aimed to analyze the fault causation of impeller failure in oil-delivery pumps by using fuzzy Petri net (FPN) theory. The longest path algorithm based on the forward reasoning was put forward and introduced into fuzzy Petri net. First, on the basis of various factors causing impeller failure, an FPN model of impeller failure in oil-delivery pumps was constructed. Then, by using the proposed algorithm, fault causation analysis of impeller failure was completed to calculate the credibility of impeller failure. Finally, the corresponding preventive measure was presented. The results indicate the key causing factor of impeller failure is mechanical impurities, and the credibility of impeller failure is 0.7342, which is consistent with the actual situation. The research finding demonstrates the flexibility and effectiveness of the FPN in fault causation analysis.
A recommendation system is an information filter tool, which uses the ratings and reviews of users to generate a personalized recommendation service for users. However, the coldstart problem of users and items is still a major research hotspot on service recommendations. To address this challenge, this paper proposes a high-efficient hybrid recommendation system based on Fuzzy C-Means (FCM) clustering and supervised learning models. The proposed recommendation method includes two aspects: on the one hand, FCM clustering technique has been applied to the item-based collaborative filtering framework to solve the cold start problem; on the other hand, the content information is integrated into the collaborative filtering. The algorithm constructs the user and item membership degree feature vector, and adopts the data representation form of the scoring matrix to the supervised learning algorithm, as well as by combining the subjective membership degree feature vector and the objective membership degree feature vector in a linear combination, the prediction accuracy is significantly improved on the public datasets with different sparsity. The efficiency of the proposed system is illustrated by conducting several experiments on MovieLens dataset.
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