Prognostics and health management is an emerging discipline to scientifically manage the health condition of engineering systems and their critical components. It mainly consists of three main aspects: construction of health indicators, remaining useful life prediction and health management. Construction of health indicators aims to evaluate the system’s current health condition and its critical components. Given the observations of a health indicator, prediction of the remaining useful life is used to infer the time when an engineering systems or a critical component will no longer perform its intended function. Health management involves planning the optimal maintenance schedule according to the system’s current and future health condition, its critical components and the replacement costs. Construction of health indicators is the key to predicting the remaining useful life. Bearings and gears are the most common mechanical components in rotating machines, and their health conditions are of great concern in practice. Because it is difficult to measure and quantify the health conditions of bearings and gears in many cases, numerous vibration based methods have been proposed to construct bearing and gear health indicators. This paper presents a thorough review of vibration based bearing and gear health indicators constructed from mechanical signal processing, modelling and machine learning. This review article will be helpful for designing further advanced bearing and gear health indicators and provides a basis for predicting the remaining useful life of bearings and gears. Most of the bearing and gear health indicators reviewed in this paper are highly relevant to simulated and experimental run-to-failure data rather than artificially seeded bearing and gear fault data. Finally, some problems in the literature are highlighted and areas for future study are identified.

State-of-charge (SOC), which indicates the remaining capacity at current cycle, is key to driving range prediction of electric vehicles and optimal charge control of rechargeable batteries. In this paper, we propose a combined convolutional neural network (CNN) – long short-term memory (LSTM) network to infer battery SOC from measurable data, such as current, voltage, and temperature. The proposed network shares the merits of both CNN and LSTM networks and can extract both spatial and temporal features from input data. The proposed network is trained using data collected from different discharge profiles, including dynamic stress test, federal urban driving schedule and US06 test. The performance of the proposed network is evaluated using data collected from a new combined dynamic loading profile in terms of estimation accuracy and robustness against unknown initial state. Experimental results show that the proposed CNN-LSTM network well captures the nonlinear relationships between SOC and measurable variables and presents better tracking performance than the LSTM and CNN networks. In case of unknown initial SOCs, the proposed network fast converges to true SOC and then presents smooth and accurate results, with maximum mean average error under 1% and maximum root mean square error under 2%. Moreover, the proposed network well learns the influence of ambient temperature and can estimate battery SOC under varying temperatures with maximum mean average error under 1.5% and maximum root mean square error under 2%.