CAJ | 학술논문

针对多变量预测模型的模式识别（Variable Predictive Model Based Class Discriminate，VPMCD）方法在参数估计中存在的缺陷，采用分位数回归（Quantile Regression，QR）代替原方法中的最小二乘法进行参数估计，克服最小二乘回归中强假设、易受异常值影响等问题，以此提高模式识别的精度。因此，提出了基于分位数回归的多变量预测模型模式识别方法（Quantile Regression-Variable Predictive Mode Based Cass Discriminate ，QRVPMCD）。采用局部特征尺度分解（Lo-cal Characteristic-Scale Decomposition，LCD）方法对滚动轴承振动信号进行分解得到若干个单分量信号，提取单分量信号的Hilbert谱奇异值组成故障特征向量，并以此作为QRVPMCD的输入进行滚动轴承故障诊断。对不同工作状态和故障类型下的滚动轴承振动信号进行了分析，结果表明了该方法的有效性。
침대다변량예측모형적모식식별（Variable Predictive Model Based Class Discriminate，VPMCD）방법재삼수고계중존재적결함，채용분위수회귀（Quantile Regression，QR）대체원방법중적최소이승법진행삼수고계，극복최소이승회귀중강가설、역수이상치영향등문제，이차제고모식식별적정도。인차，제출료기우분위수회귀적다변량예측모형모식식별방법（Quantile Regression-Variable Predictive Mode Based Cass Discriminate ，QRVPMCD）。채용국부특정척도분해（Lo-cal Characteristic-Scale Decomposition，LCD）방법대곤동축승진동신호진행분해득도약간개단분량신호，제취단분량신호적Hilbert보기이치조성고장특정향량，병이차작위QRVPMCD적수입진행곤동축승고장진단。대불동공작상태화고장류형하적곤동축승진동신호진행료분석，결과표명료해방법적유효성。
Targeting the defects in the parameter estimation of VPMCD (variable predictive model-based class discriminate),Quantile Regression (QR)was used for parameter estimation instead of least-square approach in the original method.The questions such as strong assumptions and easiness of being affected by the outliers in the ordinary least-square regression could be overcome by QR so as to improve the accuracy of pattern recognition.Therefore,the quantile regression-variable predictive mode based on class discriminate (QRVPMCD ) was proposed. The local characteristic-scale decomposition (LCD)was used to decompose the rolling bearing vibration signal into several mono-component signals,and then the Hilbert spectrum singular values were extracted from the mono-component signals to form a fault feature vector,which was then used as input of QRVPMCD for rolling bearing fault diagnosis.The analysis results under different working conditions and different kinds of failures of roller bearings demonstrate the effectiveness of the proposed method.