Graph neural network architecture search for rotating machinery fault diagnosis based on reinforcement learning

Prediction of remaining fatigue life of metal specimens using data-driven method based on acoustic emission signal

Development of Deep Residual Neural Networks for Gear Pitting Fault Diagnosis Using Bayesian Optimization

A sequence-to-sequence remaining useful life prediction method combining unsupervised LSTM encoding-decoding and temporal convolutional network

Abstract Remaining useful life (RUL) prediction methods based on deep neural networks (DNNs) have received much attention in recent years. The collected time-series signals are usually processed by the sliding time window method into several segments with the same sequence length as the input. However, the signal processing is not only time-consuming, but also relies too much on personal experience. Moreover, the length of the time window affects the prediction results and the prediction range. Obviously, it is more desirable to remove the data processing and use an entire time series signal as the input for predicting the RUL, i.e. sequence-to-sequence RUL prediction. In order to remove the shortcomings of signal processing, this paper uses a long short-term memory (LSTM) and encoding-decoding framework to construct an unsupervised sequence data processing model. Then, a temporal convolutional network, based on a convolutional neural network, is used to further process the output data of the unsupervised sequence data processing model. The proposed sequence-to-sequence RUL prediction method not only maintains the complete sequence of the data, but has a good capability for data processing. The open access C-MAPSS simulation datasets are used for validation. The validation results show that the proposed method can realize unsupervised sequence signal reconstruction. Moreover, it has better prediction results and prediction efficiency.

Unsupervised rotating machinery fault diagnosis method based on integrated SAE–DBN and a binary processor