Motors, which are one of the most widely used machines in the manufacturing field, take charge of a key role in precision machining. Therefore, it is important to accurately estimate the health state of the motor that affects the quality of the product. The research outlined in this paper aims to improve motor fault severity estimation by suggesting a novel deep learning method, specifically, feature inherited hierarchical convolutional neural network (FI-HCNN). FI-HCNN consists of a fault diagnosis part and a severity estimation part, arranged hierarchically. The main novelty of the proposed FI-HCNN is the special inherited structure between the hierarchy; the severity estimation part utilizes the latent features to exploit the fault-related representations in the fault diagnosis task. FI-HCNN can improve the accuracy of the fault severity estimation because the level-specific abstraction is supported by the latent features. Also, FI-HCNN has ease in practical application because it is developed based on stator current signals which are usually acquired for a control purpose. Experimental studies of mechanical motor faults, including eccentricity, broken rotor bars, and unbalanced conditions, are used to corroborate the high performance of FI-HCNN, as compared to both conventional methods and other hierarchical deep learning methods.

马达是制造领域中使用最广泛的机器之一，在精密加工中起着关键作用。因此，准确估算影响产品质量的电动机的健康状态非常重要。本文概述的研究旨在通过提出一种新颖的深度学习方法，特别是特征继承层次卷积神经网络（FI-HCNN），来改善电机故障的严重性估计。FI-HCNN由故障诊断部分和严重性估计部分组成，它们按层次排列。所提出的FI-HCNN的主要新颖之处在于层次结构之间的特殊继承结构。严重性估计部分利用潜在特征来开发故障诊断任务中与故障相关的表示。FI-HCNN可以提高故障严重性估计的准确性，因为潜在功能支持特定级别的抽象。另外，由于FI-HCNN是基于通常为控制目的而获取的定子电流信号而开发的，因此在实际应用中非常容易。与常规方法和其他分层深度学习方法相比，通过对机械电机故障（包括偏心率，转子条断裂和不平衡状况）进行的实验研究可证实FI-HCNN的高性能。