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Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven to be ineffective in terms of dynamic port allocation and packet encapsulation. The signature matching methods, on the other hand, require a known signature set and processing of packet payload, can only handle the signatures of a limited number of IP packets in real-time. A machine learning method based on SVM (supporting vector machine) is proposed in this paper for accurate Internet traffic classification. The method classifies the Internet traffic into broad application categories according to the network flow parameters obtained from the packet headers. An optimized feature set is obtained via multiple classifier selection methods. Experimental results using traffic from campus backbone show that an accuracy of 99.42% is achieved with the regular biased training and testing samples. An accuracy of 97.17% is achieved when un-biased training and testing samples are used with the same feature set. Furthermore, as all the feature parameters are computable from the packet headers, the proposed method is also applicable to encrypted network traffic.

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In this paper, a novel percussion-based bolt looseness monitoring approach using intrinsic multiscale entropy analysis and back propagation (BP) neural network is proposed. The percussion-caused audio signals of bolt connection are decomposed by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain intrinsic mode functions (IMFs). The IMFs are in order of high-to-low instantaneous frequencies and contain underlying dynamical characteristics of audio signals. Multiscale sample entropy (MSE) is improved by smoothed coarse graining process, and the proposed improved multiscale sample entropy (IMSE) values of certain IMFs are adopted as condition indicators in bolt looseness monitoring. The intrinsic multiscale entropy analysis consisting of CEEDMAN and IMSE extracts underlying dynamical characteristics during percussion-caused audio signal processing to identify bolt looseness conditions. The condition indicators, namely IMSE values at smallest scale factors, are employed as input of BP neural network for training and testing, to achieve accurate and stable bolt looseness condition monitoring. The effectiveness and superiority of the proposed approach have been validated by theoretical derivation and practical experimental researches, and the adaptivity and robustness of the proposed approach are also illustrated. The results of the research in this paper demonstrate the proposed approach is promising in practical applications of bolt looseness monitoring.

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Rolling bearings play a crucial role in rotary machinery systems, and their operating state affects the entire mechanical system. In most cases, the fault of a rolling bearing can only be identified when it has developed to a certain degree. At that moment, there is already not much time for maintenance, and could cause serious damage to the entire mechanical system. This paper proposes a novel approach to health degradation monitoring and early fault diagnosis of rolling bearings based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and improved multivariate multiscale sample entropy (MMSE). The smoothed coarse graining process was proposed to improve the conventional MMSE. Numerical simulation results indicate that CEEMDAN can alleviate the mode mixing problem and enable accurate intrinsic mode functions (IMFs), and improved MMSE can reflect intrinsic dynamic characteristics of the rolling bearing more accurately. During application studies, rolling bearing signals are decomposed by CEEMDAN to obtain IMFs. Then improved MMSE values of effective IMFs are computed to accomplish health degradation monitoring of rolling bearings, aiming at identifying the early weak fault phase. Afterwards, CEEMDAN is performed to extract the fault characteristic frequency during the early weak fault phase. The experimental results indicate the proposed method can obtain a better performance than other techniques in objective analysis, which demonstrates the effectiveness of the proposed method in practical application. The theoretical derivations, numerical simulations, and application studies all confirmed that the proposed health degradation monitoring and early fault diagnosis approach is promising in the field of prognostic and fault diagnosis of rolling bearings.

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