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  • Open Access

    ARTICLE

    Integrated Generative Adversarial Network and XGBoost for Anomaly Processing of Massive Data Flow in Dispatch Automation Systems

    Wenlu Ji1, Yingqi Liao1,*, Liudong Zhang2

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2825-2848, 2023, DOI:10.32604/iasc.2023.039618

    Abstract Existing power anomaly detection is mainly based on a pattern matching algorithm. However, this method requires a lot of manual work, is time-consuming, and cannot detect unknown anomalies. Moreover, a large amount of labeled anomaly data is required in machine learning-based anomaly detection. Therefore, this paper proposes the application of a generative adversarial network (GAN) to massive data stream anomaly identification, diagnosis, and prediction in power dispatching automation systems. Firstly, to address the problem of the small amount of anomaly data, a GAN is used to obtain reliable labeled datasets for fault diagnosis model training based on a few labeled… More >

  • Open Access

    ARTICLE

    A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

    Guangfei Jia*, Fengwei Guo, Zhe Wu, Suxiao Cui, Jiajun Yang

    Structural Durability & Health Monitoring, Vol.17, No.5, pp. 383-405, 2023, DOI:10.32604/sdhm.2023.026885

    Abstract With the development of multi-signal monitoring technology, the research on multiple signal analysis and processing has become a hot subject. Mechanical equipment often works under variable working conditions, and the acquired vibration signals are often non-stationary and nonlinear, which are difficult to be processed by traditional analysis methods. In order to solve the noise reduction problem of multiple signals under variable speed, a COT-DCS method combining the Computed Order Tracking (COT) based on Chirplet Path Pursuit (CPP) and Distributed Compressed Sensing (DCS) is proposed. Firstly, the instantaneous frequency (IF) is extracted by CPP, and the speed is obtained by fitting.… More > Graphic Abstract

    A Noise Reduction Method for Multiple Signals Combining Computed Order Tracking Based on Chirplet Path Pursuit and Distributed Compressed Sensing

  • Open Access

    ARTICLE

    Sparsity-Enhanced Model-Based Method for Intelligent Fault Detection of Mechanical Transmission Chain in Electrical Vehicle

    Wangpeng He1,*, Yue Zhou1, Xiaoya Guo2, Deshun Hu1, Junjie Ye3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896

    Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process. In the algorithm, a novel… More >

  • Open Access

    ARTICLE

    Fault Diagnosis of Power Electronic Circuits Based on Adaptive Simulated Annealing Particle Swarm Optimization

    Deye Jiang1, Yiguang Wang2,*

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 295-309, 2023, DOI:10.32604/cmc.2023.039244

    Abstract In the field of energy conversion, the increasing attention on power electronic equipment is fault detection and diagnosis. A power electronic circuit is an essential part of a power electronic system. The state of its internal components affects the performance of the system. The stability and reliability of an energy system can be improved by studying the fault diagnosis of power electronic circuits. Therefore, an algorithm based on adaptive simulated annealing particle swarm optimization (ASAPSO) was used in the present study to optimize a backpropagation (BP) neural network employed for the online fault diagnosis of a power electronic circuit. We… More >

  • Open Access

    ARTICLE

    Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

    G. Anitha1, N. Supriya2, Fayadh Alenezi3, E. Laxmi Lydia4, Gyanendra Prasad Joshi5, Jinsang You6,*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 221-238, 2023, DOI:10.32604/csse.2023.036479

    Abstract Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief network (DBN) classification model, which… More >

  • Open Access

    ARTICLE

    Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches

    Hong Zhang1,*, Qi Wang1, Lixing Chen1, Jiaming Zhou1, Haijian Shao2

    Energy Engineering, Vol.120, No.8, pp. 1867-1883, 2023, DOI:10.32604/ee.2023.028453

    Abstract Induction motors (IMs) typically fail due to the rate of stator short-circuits. Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories, non-destructive fault detection is universally perceived as a difficult issue. This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs. Based on the publicly available thermal images (IRT) dataset related to condition… More > Graphic Abstract

    Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches

  • Open Access

    ARTICLE

    Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

    Tusongjiang Kari1, Zhiyang He1, Aisikaer Rouzi2, Ziwei Zhang3, Xiaojing Ma1,*, Lin Du1

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 691-705, 2023, DOI:10.32604/iasc.2023.037617

    Abstract Power transformer is one of the most crucial devices in power grid. It is significant to determine incipient faults of power transformers fast and accurately. Input features play critical roles in fault diagnosis accuracy. In order to further improve the fault diagnosis performance of power transformers, a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study. Firstly, the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration, gas ratio and energy-weighted dissolved gas analysis. Afterwards, a kernel extreme learning machine tuned by the Aquila… More >

  • Open Access

    EDITORIAL

    Assessment on Fault Diagnosis and State Evaluation of New Power Grid: A Review

    Bo Yang1, Yulin Li1, Yaxing Ren2, Yixuan Chen3, Xiaoshun Zhang4, Jingbo Wang5,*

    Energy Engineering, Vol.120, No.6, pp. 1287-1293, 2023, DOI:10.32604/ee.2023.027801

    Abstract This article has no abstract. More >

  • Open Access

    ARTICLE

    Fault Diagnosis of Power Transformer Based on Improved ACGAN Under Imbalanced Data

    Tusongjiang. Kari1, Lin Du1, Aisikaer. Rouzi2, Xiaojing Ma1,*, Zhichao Liu1, Bo Li1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4573-4592, 2023, DOI:10.32604/cmc.2023.037954

    Abstract The imbalance of dissolved gas analysis (DGA) data will lead to over-fitting, weak generalization and poor recognition performance for fault diagnosis models based on deep learning. To handle this problem, a novel transformer fault diagnosis method based on improved auxiliary classifier generative adversarial network (ACGAN) under imbalanced data is proposed in this paper, which meets both the requirements of balancing DGA data and supplying accurate diagnosis results. The generator combines one-dimensional convolutional neural networks (1D-CNN) and long short-term memories (LSTM), which can deeply extract the features from DGA samples and be greatly beneficial to ACGAN’s data balancing and fault diagnosis.… More >

  • Open Access

    ARTICLE

    Implementation of Hybrid Particle Swarm Optimization for Optimized Regression Testing

    V. Prakash*, S. Gopalakrishnan

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2575-2590, 2023, DOI:10.32604/iasc.2023.032122

    Abstract Software test case optimization improves the efficiency of the software by proper structure and reduces the fault in the software. The existing research applies various optimization methods such as Genetic Algorithm, Crow Search Algorithm, Ant Colony Optimization, etc., for test case optimization. The existing methods have limitations of lower efficiency in fault diagnosis, higher computational time, and high memory requirement. The existing methods have lower efficiency in software test case optimization when the number of test cases is high. This research proposes the Tournament Winner Genetic Algorithm (TW-GA) method to improve the efficiency of software test case optimization. Hospital Information… More >

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