基于层叠结构多特征融合的旋转机械早期故障检测方法
An Early Fault Detection Method of Rotating Machines Based on Multiple Feature Fusion with Stacking Architecture
摘要：
旋转机械的早期故障检测对于降低维修成本、提高机械系统的稳定性具有重要意义。EFD的关键点之一是开发一个通用的模型，从不同的设备中提取鲁棒性和区分性的特征，用于早期故障检测。大多数现有的EFD方法侧重于学习故障表示的一种类型的功能。然而，多个特征的组合可以捕获系统状态的更全面的表示。本文提出了一种基于多特征融合堆叠结构（M2FSA）的EFD方法。该方法结合时域、频域和时频域特征，提取具有共性和区分性的特征，实现早期故障检测。为了统一不同领域特征的维度，使用堆叠去噪自动编码器（SDAE）来学习三个领域的深度特征。所提出的M2FSA的架构由两层组成。第一层包含三个基本模型，其对应的输入是不同的深度特征。第一层的输出被连接以生成第二层的输入，第二层由Meta模型组成。所提出的方法进行了测试三轴承数据集。结果表明，该方法在灵敏度和可靠性方面均优于现有方法。
英文摘要：
Early fault detection (EFD) of rotating machines is important to decrease the maintenance cost and improve the mechanical system stability. One of the key points of EFD is developing a generic model to extract robust and discriminative features from different equipment for early fault detection. Most existing EFD methods focus on learning fault representation by one type of feature. However, a combination of multiple features can capture a more comprehensive representation of system state. In this paper, we propose an EFD method based on multiple feature fusion with stacking architecture (M2FSA). The proposed method can extract generic and discriminiative features to detect early faults by combining time domain (TD), frequency domain (FD), and time-frequency domain (TFD) features. In order to unify the dimensions of the different domain features, Stacked Denoising Autoencoder (SDAE) is utilized to learn deep features in three domains. The architecture of the proposed M2FSA consists of two layers. The first layer contains three base models, whose corresponding inputs are different deep features. The outputs of the first layer are concatenated to generate the input to the second layer, which consists of a meta model. The proposed method is tested on three bearing datasets. The results demonstrate that the proposed method is better than existing methods both in sensibility and reliability.
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