Log In Sign Up

A new rotating machinery fault diagnosis method based on the Time Series Transformer

by   Yuhong Jin, et al.

Fault diagnosis of rotating machinery is an important engineering problem. In recent years, fault diagnosis methods based on the Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) have been mature, but Transformer has not been widely used in the field of fault diagnosis. To address these deficiencies, a new method based on the Time Series Transformer (TST) is proposed to recognize the fault mode of bearings. In this paper, our contributions include: Firstly, we designed a tokens sequences generation method which can handle data in 1D format, namely time series tokenizer. Then, the TST combining time series tokenizer and Transformer was introduced. Furthermore, the test results on the given dataset show that the proposed method has better fault identification capability than the traditional CNN and RNN models. Secondly, through the experiments, the effect of structural hyperparameters such as subsequence length and embedding dimension on fault diagnosis performance, computational complexity and parameters number of the TST is analyzed in detail. The influence laws of some hyperparameters are obtained. Finally, via t-Distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction method, the feature vectors in the embedding space are visualized. On this basis, the working pattern of TST has been explained to a certain extent. Moreover, by analyzing the distribution form of the feature vectors, we find that compared with the traditional CNN and RNN models, the feature vectors extracted by the method in this paper show the best intra-class compactness and inter-class separability. These results further demonstrate the effectiveness of the proposed method.


A Novel Deep Parallel Time-series Relation Network for Fault Diagnosis

Considering the models that apply the contextual information of time-ser...

Graph neural network-based fault diagnosis: a review

Graph neural network (GNN)-based fault diagnosis (FD) has received incre...

Power Transformer Fault Diagnosis with Intrinsic Time-scale Decomposition and XGBoost Classifier

An intrinsic time-scale decomposition (ITD) based method for power trans...

Transformers predicting the future. Applying attention in next-frame and time series forecasting

Recurrent Neural Networks were, until recently, one of the best ways to ...

Adversarial adaptive 1-D convolutional neural networks for bearing fault diagnosis under varying working condition

In recent years, intelligent fault diagnosis algorithms using machine le...

Deep Metric Learning Model for Imbalanced Fault Diagnosis

Intelligent diagnosis method based on data-driven and deep learning is a...

Multi-Stage Fault Warning for Large Electric Grids Using Anomaly Detection and Machine Learning

In the monitoring of a complex electric grid, it is of paramount importa...