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  • 1
    Language: English
    In: IEEE Transactions on Industrial Electronics, November 2019, Vol.66(11), pp.8760-8771
    Description: Automated early detection and identification of switch faults are essential in high-voltage applications. Modular multilevel converter (MMC) is a new and promising topology for such applications. MMC is composed of many identical controlled voltage sources called modules or cells. Each cell may have one or more switches and a switch failure may occur in anyone of these cells. The steady-state normal and fault behavior of a cell voltage will also significantly vary according to the changes in the load current and the fault timing. This makes it a challenging problem to detect and identify such faults as soon as they occur. In this paper, we propose a real-time and highly accurate MMC circuit monitoring system for early fault detection and identification using adaptive one-dimensional convolutional neural networks. The proposed approach is directly applicable to the raw voltage and current data and thus eliminates the need for any feature extraction algorithm, resulting in a highly efficient and reliable system. Simulation results obtained using a four-cell, eight-switch MMC topology demonstrate that the proposed system has a high reliability to avoid any false alarm and achieves a detection probability of 0.989, and average identification probability of 0.997 in less than 100 ms.
    Keywords: Fault Diagnosis ; Circuit Faults ; Topology ; Fault Detection ; Switches ; Capacitors ; Convolutional Neural Network (CNN) ; Fault Detection ; Fault Identification ; Modular Multilevel Converter (Mmc) ; Engineering
    ISSN: 0278-0046
    E-ISSN: 1557-9948
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
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  • 2
    Language: English
    In: IEEE Transactions on Industrial Electronics, November 2016, Vol.63(11), pp.7067-7075
    Description: Early detection of the motor faults is essential and artificial neural networks are widely used for this purpose. The typical systems usually encapsulate two distinct blocks: feature extraction and classification. Such fixed and hand-crafted features may be a suboptimal choice and require a significant computational cost that will prevent their usage for real-time applications. In this paper, we propose a fast and accurate motor condition monitoring and early fault-detection system using 1-D convolutional neural networks that has an inherent adaptive design to fuse the feature extraction and classification phases of the motor fault detection into a single learning body. The proposed approach is directly applicable to the raw data (signal), and, thus, eliminates the need for a separate feature extraction algorithm resulting in more efficient systems in terms of both speed and hardware. Experimental results obtained using real motor data demonstrate the effectiveness of the proposed method for real-time motor condition monitoring.
    Keywords: Induction Motors ; Feature Extraction ; Fault Detection ; Convolution ; Real-Time Systems ; Neural Networks ; Mathematical Model ; Convolutional Neural Networks (Cnns) ; Motor Current Signature Analysis (Mcsa) ; Engineering
    ISSN: 0278-0046
    E-ISSN: 1557-9948
    Source: IEEE Conference Publications
    Source: IEEE Journals & Magazines 
    Source: IEEE Xplore
    Source: IEEE Journals & Magazines
    Library Location Call Number Volume/Issue/Year Availability
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  • 3
    Language: English
    In: IEEE Transactions on Industrial Electronics, 2016, Vol.63(11), p.7067(9)
    Keywords: Real Time Systems – Research ; Motors – Research ; Artificial Neural Networks – Research
    ISSN: 0278-0046
    Source: Cengage Learning, Inc.
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  • 4
    Language: English
    In: IEEE transactions on industrial electronics (1982. Print), 2019, Vol. 66(10), pp. 8136-8147
    Description: This paper presents a fast, accurate, and simple systematic approach for online condition monitoring and severity identification of ball bearings. This approach utilizes compact one-dimensional (1-D) convolutional neural networks (CNNs) to identify, quantify, and localize bearing damage. The proposed approach is verified experimentally under several single and multiple damage scenarios. The experimental results demonstrated that the proposed approach can achieve a high level of accuracy for damage detection, localization, and quantification. Besides its real-time processing ability and superior robustness against the high-level noise presence, the compact and minimally trained 1-D CNNs in the core of the proposed approach can handle new damage scenarios with utmost accuracy.
    Keywords: Engineering And Technology ; Mechanical Engineering ; Reliability And Maintenance ; Teknik Och Teknologier ; Maskinteknik ; Tillförlitlighets- Och Kvalitetsteknik ; Industriell Ekonomi ; Industrial Economy
    ISSN: 0278-0046
    E-ISSN: 15579948
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