Deep data-driven methods for fault diagnosis, as an engineering-oriented approach, rely heavily on target data. For engineering applications, the working conditions of rotating machinery fluctuate from time to time and a collection for any working conditions is impossible. To tackle this problem, a robust network with optimal kernels named EnvelopeNet is proposed for extracting solid information and eliminating the influence of fluctuations. In the EnvelopeNet, a feature evaluation building block named envelope module is constructed based on optimal band selection theory to optimize the kernels. Compared with the kurtogram, the learned optimal kernels and features show strong semantics which helps the network become robust. The EnvelopeNet is validated under the approximate speed and mixed speed scenarios. The results show that the EnvelopeNet could provide admirable generalization ability for fluctuating working conditions and an average improvement of about 4% and 3% over existing approaches under two scenarios respectively.

作为面向工程的方法，深度数据驱动的故障诊断方法在很大程度上依赖于目标数据。对于工程应用，旋转机械的工作条件有时会波动，因此无法收集任何工作条件。为了解决这个问题，提出了一个具有最佳内核的强大网络EnvelopeNet来提取可靠的信息并消除波动的影响。在EnvelopeNet中，基于最佳频带选择理论构造了一个名为信封模块的特征评估构件，以优化内核。与峰图相比，学习到的最优内核和特征显示出强大的语义，有助于网络变得更健壮。EnvelopeNet在近似速度和混合速度情况下进行了验证。