Fault diagnosis in spur gears based on genetic algorithm and random forest
Fault diagnosis in spur gears based on genetic algorithm and random forest
复制标题
DOI：
10.1016/j.ymssp.2015.08.030
复制
发表时间：
2016-03-01
影响因子：
8.4
通讯作者：
Li, Chuan
中科院分区：
文献类型：
--
作者：
Cerrada, Mariela;Zurita, Grover;Li, Chuan
齿轮箱状态监测的需求越来越大，因此需要评估新的方法来提高齿轮故障检测的可靠性、有效性和准确性。在基于机器学习的诊断中，特征选择仍然是一个重要的方面，以达到诊断模型的良好性能。另一方面，随机森林分类器在工业环境中是合适的模型，在工业环境中，大数据样本通常不能用于训练这样的诊断模型。本研究的主要目的是通过从振动信号中提取最佳的时、频、时频域状态参数集，建立一个稳健的直齿圆柱齿轮多类故障诊断系统。该诊断系统是在有监督的环境下，利用遗传算法和基于随机森林的分类器实现的。通过使用遗传算法，原始条件参数集相对于初始大小减少了66%左右，分类精度仍在97%以上。在不同的负荷和速度运行条件下，通过考虑几种故障类别(其中一种是早期故障)，对实际振动信号进行了测试。(C)2015爱思唯尔有限公司。保留所有权利。
There are growing demands for condition-based monitoring of gearboxes, and therefore new methods to improve the reliability, effectiveness, accuracy of the gear fault detection ought to be evaluated. Feature selection is still an important aspect in machine learning-based diagnosis in order to reach good performance of the diagnostic models. On the other hand, random forest classifiers are suitable models in industrial environments where large data-samples are not usually available for training such diagnostic models. The main aim of this research is to build up a robust system for the multi-class fault diagnosis in spur gears, by selecting the best set of condition parameters on time, frequency and time-frequency domains, which are extracted from vibration signals. The diagnostic system is performed by using genetic algorithms and a classifier based on random forest, in a supervised environment. The original set of condition parameters is reduced around 66% regarding the initial size by using genetic algorithms, and still get an acceptable classification precision over 97%. The approach is tested on real vibration signals by considering several fault classes, one of them being an incipient fault, under different running conditions of load and velocity. (C) 2015 Elsevier Ltd. All rights reserved.