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首页> 外文期刊>Journal of circuits, systems and computers >Multi-Rider Optimization-Based Neural Network for Fault Isolation in Analog Circuits
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Multi-Rider Optimization-Based Neural Network for Fault Isolation in Analog Circuits

机译：基于多骑手优化的模拟电路故障隔离神经网络

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摘要

Fault isolation in electronic circuits is a trending area of interest as analog circuits find valuable application in industry. The failures in circuit systems cause severe issues in the normal functioning of the system that insists on the need for an automatic method of fault isolation in analog circuits. Literature conveys the issues associated with the fault isolation and hence, to address the severity of the faults, a novel model is proposed to isolate the fault causing component in the circuit. The proposed Multi-Rider Optimization-based Neural Network (M-RideNN) isolates the faulty part of the circuit from the fault-free areas such that the fault diagnosis is structured in an effective way. The fault isolation is progressed as four major steps such as establishing the fault dictionary, signal normalization using Linear Predictive Coding (LPC), effective dimensional reduction methodology using Probabilistic Principal Component Analysis (PPCA), and fault isolation using the proposed M-RideNN classifier. Finally, the experimentation using three circuits, namely Triangular Wave Generator (TWG), Bipolar Transistor Amplifier (BTA), differentiator (DIF), and an application circuit, Solar Power Converter (SPC), proves that the proposed M-RideNN classifier offers better classification accuracy of 93.18% with a minimum Mean Square Error (MSE) of 0.0682.
机译：电子电路中的故障隔离是一个兴趣的趋势区域，因为模拟电路在工业中找到了宝贵的应用。电路系统中的故障导致系统正常运行中的严重问题，该系统坚持在模拟电路中需要自动隔离的自动隔离方法。文献传达了与故障隔离相关的问题，从而解决了故障的严重性，提出了一种小说模型来隔离电路中的故障导致组件。所提出的基于多骑手优化的神经网络（M-RINTenn）将电路的故障部分与无故障区域隔离，使得故障诊断以有效的方式构建。故障隔离被进展为四个主要步骤，例如建立故障字典，使用线性预测编码（LPC），使用概率主成分分析（PPCA）的有效尺寸减少方法的信号归一化，以及使用所提出的M-Ridenn分类器的故障隔离。最后，使用三个电路的实验，即三角波发生器（TWG），双极晶体管放大器（BTA），微分器（DIF）和应用电路，太阳能电力转换器（SPC），证明了所提出的M-Ridenn分类器提供更好分类精度为93.18％，最小平均误差（MSE）为0.0682。

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