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Investigation of Extreme Learning Machine-Based Fault Diagnosis to Identify Faulty Components in Analog Circuits

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Abstract

Due to the growing complexities in electronic circuits, it is important to find the faults in a circuit and also diagnose since it is a crucial part during integrated circuit design process. In the whole process, it takes a lot of manual effort to extract and select features. Here we have investigated the scope of the extreme learning machine (ELM)-based fault diagnosis technique in the identification of the faulty component in the analog signal conditioning circuits. The fault diagnosis has been done without feature selection and extraction ELM method. As a case study, we have considered a Sallen–Key bandpass filter and a circuit with four-opamp biquad high-pass filter to investigate the proposed methodology. We have used a single pulse as input and collected the raw data for training and testing purpose. The result from the computation experiment gave 100% and 99.82% average accuracy.

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The data supporting this study are available from the corresponding author upon reasonable request.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology, Durgapur, West Bengal, 713209, India

    Suman Biswas, Gautam Kumar Mahanti & Nilanjan Chattaraj

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  1. Suman Biswas
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  2. Gautam Kumar Mahanti
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Biswas, S., Mahanti, G.K. & Chattaraj, N. Investigation of Extreme Learning Machine-Based Fault Diagnosis to Identify Faulty Components in Analog Circuits. Circuits Syst Signal Process 43, 711–728 (2024). https://doi.org/10.1007/s00034-023-02526-9

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