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Search Results (6)
  • Open Access

    ARTICLE

    Research on Condenser Deterioration Evolution Trend Based on ANP-EWM Fusion Health Degree

    Hong Qian1,2, Haixin Wang1,*, Guangji Wang3, Qingyun Yan4

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 679-698, 2024, DOI:10.32604/cmes.2023.043377

    Abstract This study presents a proposed method for assessing the condition and predicting the future status of condensers operating in seawater over an extended period. The aim is to address the problems of scaling and corrosion, which lead to increased loss of cold resources. The method involves utilising a set of multivariate feature parameters associated with the condenser as input for evaluation and trend prediction. This methodology offers a precise means of determining the optimal timing for condenser cleaning, with the ultimate goal of improving its overall performance. The proposed approach involves the integration of the… More >

  • Open Access

    ARTICLE

    Fusion Fault Diagnosis Approach to Rolling Bearing with Vibrational and Acoustic Emission Signals

    Junyu Chen1, Yunwen Feng1,*, Cheng Lu1,2, Chengwei Fei2

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.2, pp. 1013-1027, 2021, DOI:10.32604/cmes.2021.016980

    Abstract As the key component in aeroengine rotor systems, the health status of rolling bearings directly influences the reliability and safety of aeroengine rotor systems. In order to monitor rolling bearing conditions, a fusion fault diagnosis method, namely empirical mode decomposition (EMD)-Mahalanobis distance (E2MD) and improved wavelet threshold (IWT) (E2MD-IWT) for vibrational signals and acoustic emission (AE) signals is developed to improve the diagnostic accuracy of rolling bearings. The IWT method is proposed with a hard wavelet threshold and a soft wavelet threshold. Moreover, it is shown to be effective through numerical simulation. EMD is utilized… More >

  • Open Access

    ARTICLE

    A Shadowed Rough-fuzzy Clustering Algorithm Based on Mahalanobis Distance for Intrusion Detection

    Lina Wang1,2,*, Jie Wang3, Yongjun Ren4, Zimeng Xing1, Tao Li1, Jinyue Xia5

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 31-47, 2021, DOI:10.32604/iasc.2021.018577

    Abstract Intrusion detection has been widely used in many application domains; thus, it has caught significant attention in academic fields these years. Assembled with more and more sub-systems, the network is more vulnerable to multiple attacks aiming at the network security. Compared with the other issues such as complex environment and resources-constrained devices, network security has been the biggest challenge for Internet construction. To deal with this problem, a fundamental measure for safeguarding network security is to select an intrusion detection algorithm. As is known, it is less effective to determine the abnormal behavior as an… More >

  • Open Access

    ARTICLE

    ANN Based Novel Approach to Detect Node Failure in Wireless Sensor Network

    Sundresan Perumal1, Mujahid Tabassum1, Ganthan Narayana2, Suresh Ponnan3,*, Chinmay Chakraborty4, Saju Mohanan5, Zeeshan Basit5, Mohammad Tabrez Quasim6

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1447-1462, 2021, DOI:10.32604/cmc.2021.014854

    Abstract A wireless sensor network (WSN) consists of several tiny sensor nodes to monitor, collect, and transmit the physical information from an environment through the wireless channel. The node failure is considered as one of the main issues in the WSN which creates higher packet drop, delay, and energy consumption during the communication. Although the node failure occurred mostly due to persistent energy exhaustion during transmission of data packets. In this paper, Artificial Neural Network (ANN) based Node Failure Detection (NFD) is developed with cognitive radio for detecting the location of the node failure. The ad hocMore >

  • Open Access

    ARTICLE

    Comparisons of MFDFA, EMD and WT by Neural Network, Mahalanobis Distance and SVM in Fault Diagnosis of Gearboxes

    Jinshan Lina*, Chunhong Doub, Qianqian Wanga

    Sound & Vibration, Vol.52, No.2, pp. 11-15, 2018, DOI:10.32604/sv.2018.03653

    Abstract A method for gearbox fault diagnosis consists of feature extraction and fault identification. Many methods for feature extraction have been devised for exposing nature of vibration data of a defective gearbox. In addition, features extracted from gearbox vibration data are identified by various classifiers. However, existing literatures leave much to be desired in assessing performance of different combinatorial methods for gearbox fault diagnosis. To this end, this paper evaluated performance of several typical combinatorial methods for gearbox fault diagnosis by associating each of multifractal detrended fluctuation analysis (MFDFA), empirical mode decomposition (EMD) and wavelet transform More >

  • Open Access

    ARTICLE

    Tracking Features in Image Sequences with Kalman Filtering, Global Optimization, Mahalanobis Distance and a Management Model

    Raquel R. Pinho1, João Manuel R. S. Tavares1

    CMES-Computer Modeling in Engineering & Sciences, Vol.46, No.1, pp. 51-76, 2009, DOI:10.3970/cmes.2009.046.051

    Abstract This work addresses the problem of tracking feature points along image sequences. In order to analyze the undergoing movement, an approach based on the Kalman filtering technique has been used, which basically carries out the estimation and correction of the features' movement in every image frame. So as to integrate the measurements obtained from each image into the Kalman filter, a data optimization process has been adopted to achieve the best global correspondence set. The proposed criterion minimizes the cost of global matching, which is based on the Mahalanobis distance. A management model is employed More >

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