Bearing remaining useful life (RUL) prediction plays a crucial role in guaranteeing machinery safe operation and reducing maintenance loss. In this paper, we present a new deep feature learning method for RUL estimation approach through time frequency representation (TFR) and multi-scale convolutional neural network (MSCNN). TFR can reveal non-stationary property of bearing degradation signal effectively. After acquiring time-series degradation signals, we get TFRs which contain plenty of useful information using wavelet transform (WT). Owing to high dimensionality, the size of these TFRs is reduced by bilinear interpolation, which are further regarded as inputs for deep learning model. Here, we introduce a MSCNN model structure, which keeps the global and local information synchronously compared to traditional convolutional neural network (CNN). The salient features, which contribute for RUL estimation, can be learned automatically by MSCNN. The effectiveness of the presented method is validated by the experiment data. Compared to traditional data-driven and different CNN-based feature extraction methods, the proposed method shows enhanced performance in the prediction accuracy.

Deep-learning-based health prognostics is receiving an ever-increasing attention. Most existing methods leverage advanced neural networks for prognostics performance improvement, providing mainly point estimates as prognostics results without addressing prognostics uncertainty. However, uncertainty is critical for both health prognostics and subsequent decision-making, especially for safety-critical applications. Inspired by the idea of Bayesian machine learning, a Bayesian deep-learning-based (BDL-based) method is proposed for health prognostics with uncertainty quantification. State-of-the-art deep learning models are extended into Bayesian neural networks (BNNs), and a variational inference based method is presented for the BNNs learning and inference. The proposed method is validated through a ball bearing dataset and a turbofan engine dataset. Other than point estimates, health prognostics using the BDL-based method is enhanced with uncertainty quantification. Scalability and generalization ability of state-of-the-art deep learning models can be well inherited. Stochastic regularization techniques, widely available in mainstream software libraries, can be leveraged to efficiently implement the BDL-based method for practical applications.

State of health (SOH) is critical to the efficient and reliable use of lithium-ion batteries (LIBs), especially in electric vehicle (EV) applications. Recently, electrochemical impedance spectroscopy (EIS)-based technique has been proven to be effective for SOH estimation of LIB. However, existing EIS-based methods failed to consider the impact of ambient temperature and battery state of charge (SOC), leading to the limited flexibility of these methods under dynamic environments. In this work, a novel EIS-based method is proposed for battery SOH estimation considering variations of temperature and SOC. An equivalent circuit model (ECM) is first introduced, in which the solid electrolyte interface (SEI) resistance and charge transfer resistance are employed to map their relationship with SOH under variant temperature and SOC. Subsequently, a probabilistic model, taking charge transfer resistance, temperature, and SOC as input variables, is developed for LIB SOH estimation. Experimental study indicates that the estimation error of the proposed method is around 4% when simultaneously considering the temperature and SOC effects. Moreover, the estimation error can reach 1.29% under certain conditions (e.g., 80% SOC at 30 °C). Both results of estimation error are better than the existing EIS-based methods, which indicates that the proposed method is more flexible for SOH estimation with higher precision.