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  • Open Access


    Data-Driven Approach for Condition Monitoring and Improving Power Output of Photovoltaic Systems

    Nebras M. Sobahi1,*, Ahteshamul Haque2, V S Bharath Kurukuru2, Md. Mottahir Alam1, Asif Irshad Khan3

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5757-5776, 2023, DOI:10.32604/cmc.2022.028340

    Abstract Increasing renewable energy targets globally has raised the requirement for the efficient and profitable operation of solar photovoltaic (PV) systems. In light of this requirement, this paper provides a path for evaluating the operating condition and improving the power output of the PV system in a grid integrated environment. To achieve this, different types of faults in grid-connected PV systems (GCPVs) and their impact on the energy loss associated with the electrical network are analyzed. A data-driven approach using neural networks (NNs) is proposed to achieve root cause analysis and localize the fault to the component level in the system.… More >

  • Open Access


    Fault Pattern Diagnosis and Classification in Sensor Nodes Using Fall Curve

    Mudita Uppal1, Deepali Gupta1, Divya Anand2, Fahd S. Alharithi3, Jasem Almotiri3, Arturo Mansilla4,5, Dinesh Singh6, Nitin Goyal1,*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1799-1814, 2022, DOI:10.32604/cmc.2022.025330

    Abstract The rapid expansion of Internet of Things (IoT) devices deploys various sensors in different applications like homes, cities and offices. IoT applications depend upon the accuracy of sensor data. So, it is necessary to predict faults in the sensor and isolate their cause. A novel primitive technique named fall curve is presented in this paper which characterizes sensor faults. This technique identifies the faulty sensor and determines the correct working of the sensor. Different sources of sensor faults are explained in detail whereas various faults that occurred in sensor nodes available in IoT devices are also presented in tabular form.… More >

  • Open Access


    Swarm-LSTM: Condition Monitoring of Gearbox Fault Diagnosis Based on Hybrid LSTM Deep Neural Network Optimized by Swarm Intelligence Algorithms

    Gopi Krishna Durbhaka1, Barani Selvaraj1, Mamta Mittal2, Tanzila Saba3,*, Amjad Rehman3, Lalit Mohan Goyal4

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2041-2059, 2021, DOI:10.32604/cmc.2020.013131

    Abstract Nowadays, renewable energy has been emerging as the major source of energy and is driven by its aggressive expansion and falling costs. Most of the renewable energy sources involve turbines and their operation and maintenance are vital and a difficult task. Condition monitoring and fault diagnosis have seen remarkable and revolutionary up-gradation in approaches, practices and technology during the last decade. Turbines mostly do use a rotating type of machinery and analysis of those signals has been challenging to localize the defect. This paper proposes a new hybrid model wherein multiple swarm intelligence models have been evaluated to optimize the… More >

  • Open Access


    Sound Signal Based Fault Classification System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classifiers

    T. Jayasree1,*, R. Prem Ananth2

    Sound & Vibration, Vol.54, No.1, pp. 57-74, 2020, DOI:10.32604/sv.2020.08573

    Abstract Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by the combination of feature sets… More >

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