Deep learning based diagnostic methods have obtained great success in intelligent fault diagnosis of machines. However, most of the existing methods are developed for fault diagnosis task under stable working conditions, and cannot handle the data generated under sharp speed variation favorably. Besides, the large model parameters and high computing costs of these methods fail to meet the requirement of small memory and fast-precise prediction in practical applications. To address these challenges, a lightweight pyramid attention residual network (PARNet) is proposed in this paper. First, an efficient pyramid squeeze convolution module is proposed to capture multi-scale information from raw signals. Then, a soft squeeze-and-excitation attention is designed to establish long-range channel dependency, and fuse features of different scales. Based on the above improvements, a pyramid attention residual block is proposed as basic feature learning unit, and a concise CNN-based model PARNet is built using PAR block. Extensive experiments in two case studies are conducted to validate the effectiveness and adaptability of PARNet under different speed variation conditions. The test results demonstrate the superiority of the proposed method for machine fault diagnosis and reliability assessment in terms of recognition accuracy and model parameter complexity, compared with other advanced diagnostic methods.

中文翻译：

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速度急剧变化下机器智能故障诊断的轻量级金字塔注意力残差网络


基于深度学习的诊断方法在机器智能故障诊断方面取得了巨大成功。然而，现有的方法大多是针对稳定工作条件下的故障诊断任务而开发的，不能很好地处理速度急剧变化下产生的数据。此外，这些方法的模型参数大、计算成本高，无法满足实际应用中小内存和快速精确预测的要求。为了解决这些挑战，本文提出了一种轻量级金字塔注意力残差网络（PARNet）。首先，提出了一种高效的金字塔挤压卷积模块来从原始信号中捕获多尺度信息。然后，设计软挤压和激励注意力来建立长程通道依赖性，并融合不同尺度的特征。基于上述改进，提出了金字塔注意力残差块作为基本特征学习单元，并使用 PAR 块构建了一个简洁的基于 CNN 的模型 PARNet。通过两个案例研究进行了大量实验，以验证 PARNet 在不同速度变化条件下的有效性和适应性。测试结果表明，与其他先进诊断方法相比，该方法在机器故障诊断和可靠性评估方面在识别精度和模型参数复杂度方面均具有优越性。