摘要
针对自动机振动信号非平稳、非线性的特点,提出基于多尺度关联维数和线性局部切空间排列(linear local tangent space alignment,LLTSA)相结合的自动机故障诊断方法。首先,利用局部特征尺度分解(local characteristicscale decomposition,LCD)将自动机振动信号分解为不同尺度下的内禀尺度分量(intrinsic scale component),提取出反映状态信息的主要分量并计算各分量的关联维数。然后,利用线性局部切空间排列算法挖掘出可区分度更高的特征子集。最后,将得到的低维特征输入支持向量机进行识别,自动机故障诊断实验表明,所提方法具备较高的诊断准确率。此外,将LCD与经验模态分解(empirical mode decomposition,EMD)和局部均值分解(local mean decomposition,LMD)方法的诊断结果进行比较,验证所提方法的优势。
Aiming at the non-stationary and non-linear characteristics of automaton vibration signal, a fault diagnosis method of automaton based on multiseale correlation dimension and linear local tangent space alignment （LLTSA） was proposed. Firstly, the vibration signal of automaton was decomposed with local characteristic-scale decomposition （LCD） to obtain intrinsic scale components in different scales, and the correlation dimension of each principal component reflected the state information was calculated. Then, the mining performance of the feature subset with higher distinguishability was further implemented by using linear local tangent space alignment. Finally, low-dimensional feature was put into SVM to recognize the state types. The results of automaton fault test indicate that the proposed method is of high accuracy. In addition, the diagnostic results calculated with LCD, empirical mode decomposition and local mean decomposition were compared, verifying the advantage of the method.
出处
《中国测试》
北大核心
2017年第10期102-108,共7页
China Measurement & Test
基金
河北省自然科学基金资助项目(E2016506003)
关键词
故障诊断
多尺度关联维数
流形学习
自动机
fault diagnosis
muhiscale correlation dimension
manifold learning
automaton
