Laplacian双联最小二乘支持向量机用于早期故障诊断

李锋1,汤宝平2,郭胤3

振动与冲击 ›› 2017, Vol. 36 ›› Issue (16) : 85-92.

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PDF(943 KB)
振动与冲击 ›› 2017, Vol. 36 ›› Issue (16) : 85-92.
论文

Laplacian双联最小二乘支持向量机用于早期故障诊断

  • 李锋1,汤宝平2,郭胤3
作者信息 +

Early Fault Diagnosis Using Laplacian Twin Least Squares Support Vector Machine

  • LI Feng1,TANG Baoping 2,GUO Yin3
Author information +
文章历史 +

摘要

提出基于Laplacian双联最小二乘支持向量机(Laplacian twin least squares support vector machine, Lap-TLSSVM)半监督模式识别的新型早期故障诊断方法。首先用时、频域特征集广泛收集旋转机械不同早期故障的特征信息,再用提升半监督局部Fisher判别分析(enhanced semi-supervised local Fisher discriminant Analysis, ESSLFDA)将高维时、频域特征集约简为具有更好类区分度的低维特征向量,并输入到Lap-TLSSVM中进行早期故障诊断。Lap-TLSSVM引入了包含大量无标签数据信息的流形规则实现半监督学习;其目标函数只含等式约束条件,且用共轭梯度法求解目标函数的线性方程组以加速训练过程。所提出的方法在训练样本非常稀少的情况下具有较高的诊断精度和计算效率。深沟球轴承早期故障诊断实验验证了该方法的有效性。

Abstract

A novel early fault diagnosis method based on semi-supervised pattern recognition with Laplacian twin least squares support vector machine (Lap-TLSSVM) is proposed in this paper. In this method, the time-frequency domain feature set is first used to widely collect the feature information of various early faults. Then, the enhanced semi-supervised local Fisher discriminant Analysis (ESSLFDA) is utilized to reduce the high-dimensional time-frequency domain feature sets of training and testing samples to the low-dimensional eigenvectors with better category segregation. Finally, the low-dimensional eigenvectors of all samples are input into the introduced Lap-TLSSVM to conduct early fault diagnosis. In Lap-LSTSVM, the manifold regularization with large amounts of unlabeled data information is introduced to achieve semi-supervised learning. In addition, the twin objective functions of Lap-LSTSVM have only equality constraints and an efficient conjugate gradient (CG) algorithm is embedded in Lap-LSTSVM to solve the linear equations of objective functions for speeding up the training procedure. The proposed early fault diagnosis method has high diagnosis accuracy and computation efficiency even if the training sample set is small. Experimental results of early fault diagnosis on deep groove ball bearings confirm the effectiveness of the proposed method.
 

关键词

旋转机械 / 流形学习 / Laplacian双联最小二乘支持向量机 / 半监督学习 / 故障诊断

Key words

Rotating machinery / Manifold learning / Laplacian twin least squares support vector machine / Semi-supervised learning / Fault diagnosis

引用本文

导出引用
李锋1,汤宝平2,郭胤3. Laplacian双联最小二乘支持向量机用于早期故障诊断[J]. 振动与冲击, 2017, 36(16): 85-92
LI Feng1,TANG Baoping 2,GUO Yin3. Early Fault Diagnosis Using Laplacian Twin Least Squares Support Vector Machine[J]. Journal of Vibration and Shock, 2017, 36(16): 85-92

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