Cai Wenan, Zhang Qianqian, Cui Jie
School of Mechanical Engineering, Jinzhong University, Jinzhong 030619, China.
School of Automation and Software Engineering, Shanxi University, Taiyuan 030006, China.
Comput Intell Neurosci. 2022 Jul 21;2022:5077134. doi: 10.1155/2022/5077134. eCollection 2022.
Digital twin (DT) is an important method to realize intelligent manufacturing. Traditional data-based fault diagnosis methods such as fractional-order fault feature extraction methods require sufficient data to train a diagnosis model, which is unrealistic in a dynamically changing production process. The ultrahigh-fidelity DT model can generate fault state data similar to the actual system, providing a new paradigm for fault diagnosis. This paper proposes a novel digital twin-assisted fault diagnosis method for denoising autoencoder. First, in order to solve the problem of limited or unavailable fault state data for machines in dynamically variable production scenarios, a DT model of the machine is established. The model can simulate a dynamically changing production process, thereby generating data for different failure states. Second, a novel denoising autoencoder (NDAE) with Mish as the activation function is proposed and trained using the source domain data generated by DT. Finally, in order to verify the effectiveness and feasibility of the proposed method, the method is applied to a fault diagnosis example of a triplex pump, and the results show that the method can realize intelligent fault diagnosis when the fault state data are limited or unavailable.
数字孪生（DT）是实现智能制造的一种重要方法。传统的基于数据的故障诊断方法，如有分数阶故障特征提取方法，需要足够的数据来训练诊断模型，这在动态变化的生产过程中是不现实的。超高保真DT模型可以生成与实际系统相似的故障状态数据，为故障诊断提供了一种新的范例。本文提出了一种用于去噪自编码器的新型数字孪生辅助故障诊断方法。首先，为了解决动态可变生产场景中机器故障状态数据有限或不可用的问题，建立了机器的DT模型。该模型可以模拟动态变化的生产过程，从而生成不同故障状态的数据。其次，提出了一种以Mish为激活函数的新型去噪自编码器（NDAE），并使用DT生成的源域数据对其进行训练。最后，为了验证所提方法的有效性和可行性，将该方法应用于三缸泵的故障诊断实例，结果表明该方法在故障状态数据有限或不可用时能够实现智能故障诊断。
