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.

翻译：旋转机械的早期故障检测（EFD）对于降低维护成本、提升机械系统稳定性具有重要意义。EFD的关键挑战之一是开发通用模型，从不同设备中提取鲁棒且具有判别性的特征以实现早期故障检测。现有EFD方法大多侧重于通过单一类型特征学习故障表征，但多特征融合能够更全面地捕捉系统状态。本文提出一种基于多重特征融合与堆叠架构（M2FSA）的EFD方法，通过融合时域（TD）、频域（FD）及时频域（TFD）特征，提取通用且具判别性的特征以检测早期故障。为统一不同域特征的维度，采用堆叠去噪自编码器（SDAE）学习三个域的深层特征。所提出的M2FSA架构包含两层：第一层包含三个基模型，其对应输入为不同的深层特征；第一层的输出经拼接后作为第二层（元模型）的输入。该方法在三个轴承数据集上进行了测试，结果表明，所提方法在灵敏度和可靠性上均优于现有方法。