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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轻量化金字塔注意力残差网络，用于速度急剧变化下机器的智能故障诊断


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，现有的方法大多是为稳定工况下的故障诊断任务而开发的，不能很好地处理急剧速度变化下产生的数据。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。为了应对这些挑战，该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，用于从原始信号中捕获多尺度信息。然后，设计了一种软挤压和激励注意力，建立长距离通道依赖性，并融合不同尺度的特征。基于上述改进，提出了一个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。在两个案例研究中进行了广泛的实验，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，与其他先进的诊断方法相比，所提方法在识别精度和模型参数复杂度方面具有优越性。