A faster response with commendable accuracy in intelligent systems is essential for the reliability and smooth operations of industrial machines. Two main challenges affect the design of such intelligent systems: (i) the selection of a suitable model and (ii) domain adaptation if there is a continuous change in operating conditions. Therefore, we propose an evolutionary Net2Net transformation (EvoN2N) that finds the best suitable DNN architecture with limited availability of labeled data samples. Net2Net transformation-based quick learning algorithm has been used in the evolutionary framework of Non-dominated sorting genetic algorithm II to obtain the best DNN architecture. Net2Net transformation-based quick learning algorithm uses the concept of knowledge transfer from one generation to the next for faster fitness evaluation. The proposed framework can obtain the best model for intelligent fault diagnosis without a long and time-consuming search process. The proposed framework has been validated on the Case Western Reserve University dataset, the Paderborn University dataset, and the gearbox fault detection dataset under different operating conditions. The best models obtained are capable of demonstrating an excellent diagnostic performance and classification accuracy of almost up to 100% for most of the operating conditions.

翻译：智能系统以可观的精度实现快速响应，对于工业机械的可靠性与平稳运行至关重要。此类智能系统的设计面临两大挑战：(i) 合适模型的选择；(ii) 若运行工况持续变化时的领域适应问题。为此，我们提出一种进化式Net2Net转换方法（EvoN2N），该方法能够在标记数据样本有限的情况下，寻找到最合适的深度神经网络架构。研究将基于Net2Net转换的快速学习算法，应用于非支配排序遗传算法II的进化框架中，以获取最优深度神经网络架构。该快速学习算法利用代际知识迁移的概念，实现了更快速的适应度评估。所提框架无需漫长耗时的搜索过程，即可获得适用于智能故障诊断的最佳模型。该框架已在凯斯西储大学数据集、帕德博恩大学数据集以及不同运行工况下的齿轮箱故障检测数据集上得到验证。所获得的最佳模型在多数运行条件下，能够展现出卓越的诊断性能与接近100%的分类准确率。