Fault diagnosis method based on the multi-head attention focusing on data positional information

Abstract

In order to make full use of the absolute position information of fault signal, this paper designs a new multi-head attention (MHA) mechanism focusing on data positional information, proposes a novel MHA-based fault diagnosis method and extends it to the fault diagnosis scenario with missing information. Based on the absolute positional information and the trainable parameter matrix of the fault data, a novel attention weight matrix is generated, and the fault features are extracted by a fully connected network with the attention mechanism. By integrating the positional information into the weight matrix, the new MHA mechanism has the ability to extract more effective data features, compared with the traditional MHA method. Furthermore, the proposed method is also developed for the fault diagnosis scenarios with missing information. A special attention weight modified method is designed to reduce the impact of missing data on fault diagnosis results. In the experiment simulations, the data sampled from ZHS-2 multi-function motor flexible rotor test bed and the Tennessee- Eastman process data are utilized to test the performance of the algorithm. The results show that the proposed method can effectively extract fault features and reduce the impact of missing data.

A multi-objective UAV fault diagnosis framework based on attention joint multi-spatial shared knowledge

“…The attention mechanism pays attention to signal characteristics of different fault types at different periods. The authors of (Feng et al 2022) designed a novel multi-attention mechanism and introduced it into fault diagnosis methods, aiming at extracting fault feature information more efficiently. The authors of (Zhang et al 2023b) enabled the model to have higher fault identification capability by constructing a channel-space attention mechanism.…”