Abstract The tightness inspection for the slot wedges is significant for the safe operation of large generators. One of the traditional methods is analysis of the acoustic signals of knocking on the surface of the slot wedge by inspection experts. Nowadays the slot wedge inspecting robot is an effective way to measure the tightness of the slot wedges and classify the level of the slot wedges into different groups. However, there are many types of generators and the precision cannot be guaranteed if the model of one type of generators is applied to another. Although the machine learning methods such as CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) are widely used for classification, they are not suitable for model transfer between different generators. In this paper, a transfer learning based structure is introduced to solve the problem and also the mixture of RNN and CNN is designed to fulfill the system. The structure is tested to transfer models with the acoustic signal sampled by the inspecting robot between the 500 MW and 600 MW generators. Experiment results show that the transfer learning structure can transfer models from one type of generators to another. Compared with the state-of-the-art methods, the proposed structure can improve the inspection precision by at least 36.7% and obtain the average precision over 79.0%.

中文翻译：

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槽楔密封性检测机器人迁移学习结构设计

摘要 槽楔的密封性检查对大型发电机的安全运行具有重要意义。传统方法之一是由检测专家对槽楔表面的敲击声信号进行分析。槽楔检测机器人是目前测量槽楔松紧度和将槽楔水平分为不同组别的有效方法。但是，发电机的种类很多，如果把一种发电机的型号应用到另一种发电机上，精度是无法保证的。尽管CNN（卷积神经网络）和RNN（循环神经网络）等机器学习方法被广泛用于分类，但它们并不适合不同生成器之间的模型迁移。在本文中，引入了基于迁移学习的结构来解决该问题，并且还设计了 RNN 和 CNN 的混合来实现该系统。测试该结构以在 500 MW 和 600 MW 发电机之间使用检查机器人采样的声学信号传递模型。实验结果表明，迁移学习结构可以将模型从一种类型的生成器迁移到另一种类型的生成器。与最先进的方法相比，所提出的结构可以将检测精度提高至少 36.7%，并获得超过 79.0% 的平均精度。实验结果表明，迁移学习结构可以将模型从一种类型的生成器迁移到另一种类型的生成器。与最先进的方法相比，所提出的结构可以将检测精度提高至少 36.7%，并获得超过 79.0% 的平均精度。实验结果表明，迁移学习结构可以将模型从一种类型的生成器迁移到另一种类型的生成器。与最先进的方法相比，所提出的结构可以将检测精度提高至少 36.7%，并获得超过 79.0% 的平均精度。