陀螺仪
计算机科学
核(代数)
人工智能
环形激光陀螺仪
极限学习机
可靠性(半导体)
断层(地质)
小波
网络数据包
特征提取
模式识别(心理学)
机器学习
工程类
人工神经网络
功率(物理)
数学
航空航天工程
计算机网络
地质学
物理
地震学
组合数学
量子力学
作者
Xiaojun Bai,Zhenxi Ma,Wei Chen,Shenhang Wang,Yanfang Fu
标识
DOI:10.1016/j.compeleceng.2023.108956
摘要
Traditional diagnostic models for laser gyroscopes, widely which are commonly employed as high-precision angular velocity sensors in aerospace applications, often encounter challenges in terms of reliability and accuracy. These challenges arise from difficulties in feature extraction, high computational costs, and lengthy training times. In light of these challenges, the present study proposes a new method for diagnosing faults in laser gyroscopes using the Kernel Extreme Learning Machine (KELM). Specifically, the proposed method utilizes Wavelet Packet Decomposition (WPD) to efficiently extract features from the laser gyroscope signal, which are then used as input for our diagnostic model. Furthermore, the KELM model is trained for fault diagnosis. Afterward, we utilize the Improved Dung Beetle Optimizer (IDBO) algorithm to optimize its parameters for improved optimization performance. According to the experimental results, our proposed IDBO-KELM model demonstrates a 3.68% improvement in diagnostic accuracy compared to traditional approaches. Additionally, it offers the advantages of shorter training time and increased precision.
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