粒子群优化
随机森林
断层(地质)
机器人
计算机科学
人工智能
特征(语言学)
小波
特征提取
集合(抽象数据类型)
数据挖掘
模式识别(心理学)
机器学习
哲学
程序设计语言
地震学
地质学
语言学
作者
Yifan Wu,Yun Bai,Shuai Yang,Chuan Li
出处
期刊:Measurement
[Elsevier]
日期:2024-04-01
卷期号:229: 114451-114451
被引量:2
标识
DOI:10.1016/j.measurement.2024.114451
摘要
Feature extraction is a vital step for the fault diagnosis of industrial robots, while large-scale measured signals produce redundant features impairing the diagnosis performance. To address this problem, an improved adaptive particle swarm optimization (IAPSO) is suggested to extract effective features for random forest (RF) diagnosis. Raw data collected under different kinds of complex conditions are first represented by statistical parameters of its wavelet coefficients. A relative permutation order based scaling method with analytic hierarchy process is then used for selecting suitable updated strategies. RF is finally used to measure classification performance of each particle. The proposed method was evaluated by experiments on an industrial robot. Feature set was reduced 52 % from the initial size by using IAPSO, still achieving a superior classification precision over 96 %. The proposed method performs better than other peer methods and exhibits an essential improvement potential for the fault diagnosis of industrial robots.
科研通智能强力驱动
Strongly Powered by AbleSci AI