To enhance competitiveness and protect interest, an increasing number of organizations cooperate on patent applications. Partner selection has attracted much more attention because it directly affects the success of patent cooperation. By collecting some cooperative patents applied for by different categories of organizations in China from 2007 to 2015, an interorganizational patent cooperation network was built for this paper. After analyzing certain basic properties of the network, it was found that the network possessed some typical characteristics of social networks. Moreover, the network could be divided into communities, and three communities were selected to analyze as representative. Furthermore, to explore the partner selection in the patent cooperation network, eight link prediction approaches commonly used in social networks were introduced to run on another interorganizational patent cooperation network built by the patents applied for in 2016. The precision metric results of the eight link prediction approaches show that they are effective in partnership prediction; in particular, the Common Neighbors (CN) index can be effectively applied to the selection of unfamiliar partners for organizations in patent cooperation. Moreover, this paper also verified the trust transitivity based not only on historical cooperation but also on geographical location, and the complementarity of capabilities still plays an important role in partner selection for organizations.
In view of the problems in traditional fault diagnosis method, such as small samples and nonlinear relations, a fault diagnosis method based on improved multi-class classification algorithm and relevance vector machine (RVM) is proposed in the paper. Through improving the majority-vote strategy of traditional One-Against-One (OAO) algorithm and combining the features of OAO and One-Against-Rest (OAR) algorithms, the k-class classification problem is transformed into k(k-1)/2 three-class classification problems based on the proposed majority-vote strategy of double-layer and thereby an improved multi-class classification algorithm of One-Against-One-Against-Rest (OAOAR) is presented. And on each three-class classification issue, OAO and RVM as the binary classifier are adopted to achieve the multi-class classification of RVM. Numerical simulations of UCI datasets and fault diagnostic experiments results of power transformers both demonstrate that the proposed method performs significantly better than other traditional methods in terms of increasing the diagnostic accuracy, optimizing the voting results, strengthening the diagnostic confidence and identifying the hidden classes, and has more practical value in engineering.
Effective filtering and noise reduction, feature extraction and fault diagnosis, and prognostics technology are important to Prognostics and Health Management (PHM) of equipment. Therefore, a multifeature fusion fault diagnosis method based on the combination of quadratic filtering and QPSO-KELM algorithm is proposed. In the quadratic filtering, stable filtering can reduce the impact of noise and fast-kurtogram can filtrate fault frequency bands with rich fault information. Then, the time-domain, frequency-domain, and time-frequency parameters of the secondary filter signal are extracted. MSSST was used to analyze the filtered signal, and the time-frequency image was obtained. The time-frequency parameter was extracted from the time-frequency image by 2DPCA, and all the extracted parameters are taken as the fusion fault feature of the gearbox. Finally, the fault feature parameters are taken as the training sample and testing sample of QPSO-KELM for training and testing to achieve the purpose of fault diagnosis. The experimental results show that the proposed method can effectively filter the noise, complete the fault mode identification of gearbox, and improve the fault diagnosis accuracy better than other methods.
Gearbox is the key component of mechanical transmission system. Accurate fault diagnosis of gearbox is of great significance to ensure the operation of rotating machinery. Based on the comprehensive simulation test-bed in the laboratory, a gearbox fault diagnosis method based on QPSO-KELM is proposed. Firstly, the fault pre planting experiments of gear fault, bearing fault and gear bearing mixed fault are carried out on the comprehensive simulation test-bed. Then, the vibration signals collected are preprocessed by TSA to eliminate noise. The time domain, frequency domain and NASA feature parameters of the preprocessed signals are taken as training samples and test samples of QPSO-KELM. The experimental results show that the proposed method can effectively solve the problem of gearbox fault pattern recognition, and the fault diagnosis accuracy is higher than traditional methods, so the research has certain reference significance and engineering application value.
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