Wang Lv, Chen Dingliang, Mao Yongfang, Qin Yi
State Key Laboratory of Mechanical Transmission for Advanced Equipment, Chongqing University, Chongqing 400044, PR China.
School of Automation, Chongqing University, Chongqing 400044, PR China.
ISA Trans. 2025 Feb;157:451-465. doi: 10.1016/j.isatra.2024.12.026. Epub 2024 Dec 18.
The detection of machinery compound faults has always been a great challenge. Most of the current compound fault diagnosis methods require a large number of compound fault data to participate in training. However, in actual engineering, it is impractical to collect abundant fault samples, especially compound fault samples. To address the issue of lacking the compound fault data for training, this paper proposes a zero-shot attribute-embedded model for compound fault diagnosis (ZSAECFD). This model only uses the data of various single faults for training, but the trained model is able to diagnose the unseen compound faults. Using the data of single faults, the attribute prototypes for single and compound faults of bearings and gearbox are first constructed. By calculating the Euclidean distances between attributes and attribute prototypes, the compound fault types can be distinguished. Moreover, considering that the traditional sigmoid has the limited ability to map the difference of features in multi-label classification tasks, we propose a new activation function, feature difference mapping sigmoid (F-sigmoid). It can effectively amplify the differences between features, which is helpful for improving the accuracy of attribute recognition. It is also proven that F-sigmoid can effectively alleviate the problem of gradient vanishing compared to sigmoid. The performance of the proposed ZSAECFD is validated through the compound fault diagnosis experiments on bearings and gearboxes. Without using the compound fault data for training, the diagnostic accuracy of bearing faults reaches 81.82 %, and the diagnostic accuracy of gear faults is up to 88.17 %. The experimental results show that the proposed model can effectively diagnose the unseen compound faults, and has advantages over the classical and advanced zero-shot learning methods.
机械复合故障的检测一直是一个巨大的挑战。当前大多数复合故障诊断方法都需要大量的复合故障数据参与训练。然而，在实际工程中，收集大量的故障样本，尤其是复合故障样本是不切实际的。为了解决训练中缺乏复合故障数据的问题，本文提出了一种用于复合故障诊断的零样本属性嵌入模型（ZSAECFD）。该模型仅使用各种单一故障的数据进行训练，但训练后的模型能够诊断未见的复合故障。利用单一故障的数据，首先构建了轴承和齿轮箱单一故障及复合故障的属性原型。通过计算属性与属性原型之间的欧几里得距离，可以区分复合故障类型。此外，考虑到传统的sigmoid函数在多标签分类任务中映射特征差异的能力有限，我们提出了一种新的激活函数——特征差异映射sigmoid（F-sigmoid）。它可以有效地放大特征之间的差异，有助于提高属性识别的准确率。同时也证明了与sigmoid函数相比，F-sigmoid能够有效缓解梯度消失问题。通过对轴承和齿轮箱的复合故障诊断实验验证了所提出的ZSAECFD的性能。在不使用复合故障数据进行训练的情况下，轴承故障的诊断准确率达到81.82%，齿轮故障的诊断准确率高达88.17%。实验结果表明，所提出的模型能够有效地诊断未见的复合故障，并且优于经典和先进的零样本学习方法。
