基于随机森林算法的汽车轴承故障检测

针对传统机器学习算法受输入变量限制、且易出现过学习或欠学习,提出不受输入变量限制且存在大量数据缺失时有很好保持精确性的随机森林算法对汽车轴承故障进行检测。对采集到样本数据进行滤波处理,抑制信号中噪声;利用随机森林算法对采集到的时域信号进行分类标识,确定包含故障信息的信号序列;再将信号转换到频域,利用随机森林算法对频域内信号进行检测,确定出故障频率;最后采集试验数据对所提及算法进行验证,结果表明:相比于传统的机器学习算法,随机森林算法响应速度快,且准确率高。

Fault Detection of Automobile Bearing Based on Random Forest Algorithm

Because the traditional machine learning algorithm, which is limited by input variables and easy to have or not to learn, a random forest algorithm with good accuracy is proposed to detect the fault of car bearing without limits of input variables. Firstly, the collected sample data was filtered to suppress the noise in the signal. Secondly, random forest algorithm was used to classify and label the collected time domain signals and to determine the signal sequence containing fault information. And then the signal was converted to the frequency domain, and the signal in the frequency domain was detected by using random forest to determine the fault frequency. Finally, the experimental data were collected to verify the proposed algorithm. The results show that the random forest algorithm has fast response speed and high accuracy as compared with the traditional machine learning algorithm.