Intelligent fault diagnosis methods based on deep auto-encoder have achieved great success in the past several years. However, these methods cannot effectively handle the data collected under noisy environment. Therefore, this paper proposes a new ensemble deep contractive auto-encoder (EDCAE) to address the problem. First, we design fifteen deep contractive auto-encoders (DCAE) to learn invariant feature representation automatically. Due to the Jacobian penalty term in DCAE and different characteristics, these models can deal with various noisy data effectively. Second, fisher discriminant analysis is applied to select low-dimensional features with the maximum class separability. Softmax classifier is adopted to identify the selected features and produce fifteen classification results. Finally, a new combination strategy is developed to combine these individual results. Benefitting from the combination strategy, it can produce accurate diagnosis results even under strong background noise. Additionally, to prove the effectiveness of EDCAE, theory analysis about error bound is conducted. The proposed method is verified on three case studies including bearing, gear box and self-priming centrifugal pump. Experiments are conducted under seven different signal-to-noise-ratios. Results show that EDCAE is better than other intelligent diagnosis methods, including individual DCAE, deep auto-encoder, sparse deep auto-encoder, deep denoising auto-encoder and several ensemble methods.

在过去的几年中，基于深度自动编码器的智能故障诊断方法取得了巨大的成功。但是，这些方法无法有效处理在嘈杂环境下收集的数据。因此，本文提出了一种新的集成深度压缩自动编码器（EDCAE）来解决该问题。首先，我们设计了十五个深度压缩自动编码器（DCAE），以自动学习不变特征表示。由于DCAE中的雅可比罚分项和不同的特性，这些模型可以有效地处理各种噪声数据。其次，费舍尔判别分析用于选择具有最大类别可分离性的低维特征。采用Softmax分类器来识别所选特征并产生十五个分类结果。最后，开发了一种新的合并策略以合并这些单个结果。得益于组合策略，即使在强烈的背景噪声下，它也可以产生准确的诊断结果。另外，为了证明EDCAE的有效性，对误差范围进行了理论分析。在轴承，齿轮箱和自吸式离心泵三个案例研究中验证了该方法的有效性。实验是在七个不同的信噪比下进行的。结果表明，EDCAE优于其他智能诊断方法，包括单独的DCAE，深度自动编码器，稀疏深度自动编码器，深度去噪自动编码器和几种集成方法。为了证明EDCAE的有效性，对误差范围进行了理论分析。在轴承，齿轮箱和自吸式离心泵三个案例研究中验证了该方法的有效性。实验是在七个不同的信噪比下进行的。结果表明，EDCAE优于其他智能诊断方法，包括单独的DCAE，深度自动编码器，稀疏深度自动编码器，深度去噪自动编码器和几种集成方法。为了证明EDCAE的有效性，对误差范围进行了理论分析。在轴承，齿轮箱和自吸式离心泵三个案例研究中验证了该方法的有效性。实验是在七个不同的信噪比下进行的。结果表明，EDCAE优于其他智能诊断方法，包括单独的DCAE，深度自动编码器，稀疏深度自动编码器，深度去噪自动编码器和几种集成方法。