Intelligent mechanical fault diagnosis has developed very fast in recent years due to the advancement and application of deep learning technologies. Thus, there are many deep learning network models that have been explored in fault classification and diagnosis. However, there are still limitations in research on the relationship between fault location, fault type, and fault severity. In this paper, a novel method for diagnosis of bearing fault using hierarchical multitask convolution neural networks (HMCNNs) is proposed, taking into account the mentioned relationships. The HMCNN model includes a main task and multiple subtasks. In the HMCNN model, a weighted probability is used to reduce the classification error propagation among multitasks to improve the fault diagnosis accuracy. The validity of the proposed method is verified on bearing datasets. Experimental results show that the proposed method is very effective and superior to the existing methods.

中文翻译：

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分层多任务卷积神经网络的轴承故障诊断新方法

由于深度学习技术的进步和应用，近年来智能机械故障诊断的发展非常迅速。因此，在故障分类和诊断中已经探索了许多深度学习网络模型。但是，关于故障位置，故障类型和严重性之间关系的研究仍存在局限性。提出了一种基于层次多任务卷积神经网络（HMCNN）的轴承故障诊断新方法，并考虑了上述关系。HMCNN模型包括一个主要任务和多个子任务。在HMCNN模型中，使用加权概率来减少分类错误在多任务之间的传播，从而提高故障诊断的准确性。在轴承数据集上验证了该方法的有效性。