基于ELMD与改进SMSVM的机械故障诊断方法

机械振动信号携带大量重要的机械状态信息,然而机械故障振动信号在复杂工作状态下通常呈现非平稳、非线性特性。因此,从振动信号抽取和选择有效的机械故障特征、提高故障识别性能,成为机械故障诊断研究的热点。针对上述问题,本文提出了基于集成局部均值分解(Ensemble local means decomposition,ELMD)与改进的稀疏多尺度支持向量机(Sparse multiscale support vector machine,SMSVM)的机械故障诊断方法。该方法首先使用自适应非线性、非平稳信号处理方法 ELMD把多模态调制故障信号分解成为多个单模态解调信号,有效地增强了故障特征。把压缩感知和多尺度分析技术融合于故障模式分类中,提出改进SMSVM旋转机械故障识别方法,提高多类机械微弱故障数据模式识别性能。该方法融合稀疏表示、多尺度分析和SVM的优点,无需求解复杂的优化问题,易于推广至更多尺度SVM,具有计算量少、泛化性与鲁棒性好、物理意义明显等优点。人工数据和实验设备数据验证了本文算法的优越性。

Novel Machinery Fault Diagnosis Approach via ELMD and Improved SMSVM

Vibration signal contains a large amount of valuable information of machinery working conditions, and faulty vibration signal is generally nonlinear and non-stationary under complex conditions. It is a big challenge to extract effective fault feature and identify faults from vibration signal. A novel machinery fault diagnosis approach via the ensemble local means decomposition (ELMD) and the improved sparse multiscale support vector machine (SMSVM) is proposed in this work. ELMD, adaptive nonlinear and nonstationary signal processing approach, is performed to decompose the multiple modulated faulty components into demodulated mono-components, thus effectively enhancing the faulty features. Improved SMSVM coupled with multiscale analysis and compressive sensing is developed for machinery fault pattern recognition, thus enhancing the performance of multicalss incipient fault identification. The proposed algorithm inherits the merits of sparse representation, multiscale analysis and SVM, and can be generalized to multiclass problem with moderate computation complexity, with better robustness and generalization. The efficiency and effectiveness of the proposed method is validated by synthesis data and experimental data.