Edge computing equipment is a new tool that has been widely used to monitor the operation state of industrial equipment and to diagnose and analyze faults. Therefore, the fault diagnosis algorithm used in the edge computing device plays an especially significant role in fault diagnosis. The application of deep learning method in mechanical fault diagnosis has been gradually popularized, because it has many advantages, such as strong classification ability and accurate feature extraction ability. However, many of the completed papers and models are based on single label system and are used to diagnose single target fault. The validation set is not rigorous enough, and it is difficult to accurately simulate the faults that may occur in the actual production process. Nowadays, in the era of big data, the single label system ignores the joint relationship of different fault types, and it is difficult to make a correct judgment for the location, type and degree of mechanical failure. Hence, in the process of experiment, we used the bearing data of Case Western Reserve University(CWRU) to ensure the wide range and large quantity of data sets. A fault diagnosis method of gear and bearing in the gear-box based on multi-task deep learning model is put forward. In this method, gear and bearing faults can be diagnosed simultaneously. Through a separate task layer, this method can adaptively extract the characteristics of distinct targets from the same signal, and add a Batch Normalization layer(BN) to accelerate the convergence speed of the network. Through experiments, we conclude that it is an effective method which can judge the fault situation of gear and bearing accurately in a variety of working conditions.

中文翻译：

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齿轮箱在边缘设备上运行的多故障诊断方法

边缘计算设备是一种新工具，已广泛用于监视工业设备的运行状态以及诊断和分析故障。因此，边缘计算设备中使用的故障诊断算法在故障诊断中起特别重要的作用。深度学习方法由于具有分类能力强，特征提取准确等优点，在机械故障诊断中的应用已逐渐普及。但是，许多已完成的论文和模型都基于单标签系统，并用于诊断单个目标故障。验证集不够严格，并且很难准确模拟实际生产过程中可能发生的故障。如今，在大数据时代，单标签系统忽略了不同故障类型的联合关系，很难对机械故障的位置，类型和程度做出正确的判断。因此，在实验过程中，我们使用了凯斯西储大学（CWRU）的方位数据来确保数据集的广度和数量。提出了一种基于多任务深度学习模型的齿轮箱齿轮轴承故障诊断方法。通过这种方法，可以同时诊断齿轮和轴承故障。通过单独的任务层，该方法可以自适应地从同一信号中提取不同目标的特征，并添加批处理归一化层（BN）以加快网络的收敛速度。通过实验，