Application of Improved VPMCD Approach in Roller Bearing Fault Diagnosis

Abstract

Abstract:

According to the drawbacks in the parameter estimation process of a new pattern recognition approach—VPMCD, the VPMCD approach was improved, its parameter estimation approach,least square method, was replaced by principal component estimation. The principal component estimation can eliminate the influences of the multiple linear correlations among the predictive variables. As a result, it can get more stable model parameters and improve the precision of pattern recognition. LCD approach was first adopted herein to decompose the roller bearing vibration
signals into several mono-component signals. Then the approximate entropies were abstracted from the mono-component signals and formed into fault feature vector which will act as input in the improved VPMCD. Finally, the improved VPMCD was used as a classifier to distinguish the different working conditions and failures of roller bearing. The analysis results from vibration signals of the four roller bearing states i.e. normal, outer faults, inner faults and roller faults demonstrate the effectiveness of the proposed method.

Key words: improved variable predictive model based class discriminate(VPMCD);local characteristic-scale decomposition(LCD), principal component estimation;roller bearing;fault diagnosis

摘要：

针对模式识别新方法VPMCD(variable predictive model based class discriminate)在参数估计过程中存在的缺陷，对VPMCD方法进行了改进,用主成分估计法代替原方法中的最小二乘法进行参数估计，消除了预测变量间存在多重线性相关性的影响,可以获得更加稳定的模型参数,从而提高模式识别的精度。采用局部特征尺度分解(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，提取各分量的近似熵组成故障特征向量作为改进VPMCD的输入,以改进VPMCD作为分类器对滚动轴承的工作状态和故障类型进行分类。对正常状态、外圈故障、内圈故障和滚动体故障四种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,结果表明该方法有效。

关键词: 改进VPMCD, 局部特征尺度分解, 主成分估计, 滚动轴承, 故障诊断

CLC Number:

TH113

Yang Yu, Li Jie, Pan Haiyang, Cheng Junsheng. Application of Improved VPMCD Approach in Roller Bearing Fault Diagnosis[J]. China Mechanical Engineering, 2014, 25(4): 491-496.

杨宇, 李杰, 潘海洋, 程军圣. 改进VPMCD方法在滚动轴承故障诊断中的应用[J]. 中国机械工程, 2014, 25(4): 491-496.

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