基于细化谱和隐马尔科夫模型的齿轮故障分类方法

摘要

为了快速准确地对齿轮故障作出分类，结合隐马尔科夫模型( HMM)高效的模式分类能力，提出了基于细化谱( ZOOMFFT)和隐马尔科夫模型的故障分类方法。采用时域同步平均法从复杂信号中提取目标齿轮的啮合信号作为分析对象，对其进行细化谱分析，提取基频、倍频及其边频带幅值作为特征量输入到HMM中训练和识别，再通过对比对数似然概率值来确定齿轮故障类型。试验结果表明该方法可以有效地对齿轮故障进行分类。%In order to quickly and accurately classify gear failure, combined with pattern classification capabilities of Hidden Markov model ( HMM) , a novel fault diagnosis approach based on the Zoom Spectrum and Hidden Morkov Model is proposed. First, time-domain synchronous average signal of an interested gear is extracted from original signal and zoom spectrum is analyzed using the TSA signal. Then, the side-frequency bands of fundamental frequency and its harmonious amplitude are processed as a feature vector, which was proved sensitive in fault diagnosis in previous research. The HMMs are trained by maximizing the probability of given feature vectors and the gear failure types are identified by comparing the logarithmic likelihood probability value. Finally, the performance of the fault diagnosis scheme is validated using experimental data collected from gearbox.