快速固有成分滤波特征融合的轴承故障诊断方法

Feature Fusion of Fast Intrinsic Component Filtering for Bearing Fault Diagnosis

稀疏滤波故障特征增强方法依托故障信息固有的稀疏性可以有效实现轴承微弱故障诊断,但其存在两类弊端:经验地设置其输入、输出维度,引起特征提取效果的不确定性;需要利用先验知识从优化的权重矩阵中严格地筛选出特定成分,造成故障特征信息损失。针对上述问题,提出快速固有成分滤波特征融合方法。首先,引入复杂性测度设计自适应的稀疏滤波维度参数选取准则,并采用稀疏滤波优化目标指数遴选出一簇故障信息丰富的融合源;其次,建立故障特征融合源流形学习融合策略,包括改进流形学习方法融合遴选出的融合源,构造融合分量异常幅值检测策略和给出了最大化故障信息的融合分量加权表示。提出方法可解决稀疏滤波维度参数选择、特征筛选造成信息损失和固有流形幅值异常引起包络谱奇异等问题。仿真和试验结果验证所提出方法相较于现有流形学习和稀疏滤波等方法具有更强的轴承微弱故障特征提取能力。

The sparse filtering-based methods can effectively realize the bearing fault diagnosis via considering the inherent sparsity of fault feature. However, two shortcomings exist in these methods. One is that the input and output dimensions are set empirically to cause the uncertainty in feature extraction. Another one is that the loss of fault information might be caused due to that the prior knowledge is required for strict sifting of specific components. Hence, a feature fusion method based on the fast intrinsic component filtering is proposed in this study. First, the complexity measure is introduced to design an adaptive selection criteria of the sparse filtering dimension. Meanwhile, the optimization target of sparse filtering is used as an index to select a cluster of fusion sources with rich fault information. Second, a manifold learning fusion strategy is established, which consists of three parts: improve the manifold learning method to fuse the selected fusion source, construct a detection strategy for abnormal amplitude and weight the fused components to maximize the fault information. As a result, the selection of sparse filtering dimension, the information loss caused by second sifting, and the singularity of envelope spectrum caused by the abnormal amplitude can be solved by the proposed method.Analysis results verify that the proposed method is more effective to extract the bearing weak fault feature than the current manifold learning and sparse filtering-based methods.