基于HHT-DDKICA和支持矢量数据描述方法的提升机故障监测

Hoist Machinery Fault Monitoring Based on HHT-DDKICA and Support Vector Data Description Method

分析提升机振动信号特征抽取和故障监控存在的问题,提出基于HHT-DDKICA和支持矢量数据描述(Support vector datadescription,SVDD)相结合的提升机故障监控方法。该方法通过滤波器把振动信号分解到感兴趣的子频带,使用希尔伯特-黄变换(Hilbert-Huang transform,HHT)把子频带信号分解为多个内蕴模式函数(Intrinsic mode functions,IMFs),给出HHT去噪方法以及基于信号能量准则的IMFs选择方法,保证选取IMFs的有效性。针对单个IMF往往包含多个非线性源振动信号成分的问题,提出数据依赖核独立分量分析(Data dependent kernel independent component analysis,DDKICA)算法对源振动信号进行分离。该方法不仅能够根据数据集确定合适的核函数,而且在经验特征空间中使用DDKICA模型选择准则选择最优模型参数。根据从DDKICA抽取的时频特征分布情况,提出使用SVDD模型构造新的统计量并确定其统计控制限。提升机应用研究表明,该方法能够及时发现运转过程出现的异常情况。

The shortcomings of the existing feature extraction and machine fault detection methods are analyzed. Combining HHT-DDKICA with support vector data description （SVDD） method, a new fault monitoring algorithm for hoist machine is proposed. Vibration signals of hoist machine are filtered into multiple interesting frequency bands and intrinsic mode functions （IMFs） are obtained through empirical mode decomposition （EMD）. Then HHT denoising method and a signal energy criterion are adopted to select effective IMFs. Since single IMF may consist of some nonlinear vibration sources, an alternative data dependent kernel independent component analysis （DDKICA） method is presented to separate source signals. The method can determine a proper kernel function according to training samples, and the optimal model parameter can be also achieved by solving a DDKICA model selection criterion in the empirical feature space. Considering distributions of features extracted by DDKICA, SVDD is adopted to extablish new statistics and confidence limits. Hoist machinery application shows the efficiency of the proposed method.