In the mechatronic system, rolling bearing is a frequently used mechanical part, and its failure may result in serious accident and major economic loss. Therefore, the remaining useful life (RUL) prediction of rolling bearing is greatly indispensable. To accurately predict the RUL of the rolling bearing, a new kind of gated recurrent unit neural network with dual attention gates, namely, gated dual attention unit (GDAU), is proposed. With the acquired life-cycle vibration data of a rolling bearing, a series of root mean squares at different time instants are calculated as the health indicator (HI) vector. Next, the to-be HI sequence is predicted by GDAU according to the existing HI vector, and then the RUL of the rolling bearing is estimated. The experimental results show that the proposed GDAU can effectively predict the RULs of rolling bearings, and it has higher prediction accuracy and convergence speed than the conventional prediction methods.

In the mechanical transmission system, the gear is one of the most widely used transmission components. The failure of the gear will cause serious accidents and huge economic loss. Therefore, the remaining life prediction of the gear is of great importance. In order to accurately predict the remaining life of the gear, a new type of long-short-term memory neural network with macro-micro attention is proposed. First, some typical time-domain and frequency-domain characteristics of vibration signals are calculated respectively, such as the maximum value, the absolute mean value, the standard deviation, the kurtosis and so on. Then, the principal component of these characteristics is extracted by the isometric mapping method. The importance of fusional characteristic information is filtered via a proposed macro-micro attention mechanism, so that the input weight of neural network data and recursive data can reach multi-level real-time amplification. With the new long short-term memory neural network, the health characteristics of gear vibration signals can be predicted based on the known fusion features. The experimental results show that this method can predict the remaining life of gears and bearings well, and it has higher prediction accuracy than the conventional prediction methods.

In recent years, artificial neural networks (ANNs) have significantly advanced in both health estimation and lifetime prediction of lithium-ion batteries. The great success of ANNs stems primarily from their scalability in encoding large-scale data and manoeuvre billions of model parameters. However, there are still many challenges in balancing predictive accuracy and deployment feasibility. For instance, shallow ANNs are often more efficient but may sometimes sacrifice accuracy, whereas deep hybrid ANNs often achieve strong generalization capabilities, this comes with the trade-off of increased computational demands. To this end, this paper presents a comprehensive survey of ANN-based paradigms for estimating State-of-Health (SOH) and predicting the Remaining-Useful-Life (RUL) of lithium-ion batteries. It covers battery aging mechanisms, available datasets, network architecture, training schemes, advanced machine learning (AML) algorithms, and performance comparison. Furthermore, challenges in battery health diagnosis are reviewed in detail, and comments on future research prospects are discussed and forwarded.

In this article, facing the gaps that the traditional long short-term memory (LSTM) and convolution neural network (CNN) cannot differentially deal with the input data based on the corresponding trend and stage information in remaining useful life (RUL) prediction, a more accurate and robust RUL prediction model is constructed. First, a temporally multidifferential LSTM (TMLSTM) with the multitrend division unit and multicellular unit is proposed, and a spatially multidifferential CNN (SMCNN) with the multistage division unit and differentiated convolutions is designed. Then, by combining TMLSTM and SMCNN, a spatiotemporally multidifferential deep neural network is developed for predicting the equipment RUL, which enhances the ability of feature extraction from the spatiotemporal perspective by using the multitrend and multistage information. Via several evaluation indexes, the commercial modular aero propulsion system simulation dataset and the wind turbine gearbox bearing dataset are used to validate the superiority of the proposed method over several existing prediction methods.