Rolling element bearing is a critical component in rotating machinery that reduces friction between moving pairs. Bearing fault diagnosis is always considered as a research hotspot in the field of prognostics and health management, especially with the application of deep learning. Deep learning, such as convolutional neural network (CNN), can extract features automatically compared with traditional methods. However, construction of CNN model and training process still need a lot of prior knowledge, and it takes a lot of time to build an optimal model to achieve a high classification accuracy. In addition, great challenges of universal applicability exist when different input forms (e.g., different sampling lengths or signal forms) are considered. This study presents a universal bearing fault diagnosis model transferred from a well-known Alexnet model, and only the last fully connected layer needs to be replaced, which could reduce prior knowledge and extra time in establishing a new model. Accordingly, it is necessary to convert raw acceleration signal to uniform-sized time-frequency image, even when these data have different sizes. Further, standardized images created by eight time-frequency analysis methods are applied to validate effectiveness of the proposed method in two case studies. The results indicate that this method can be applied in bearing fault diagnosis, and t-SNE helps to understand the process of feature extraction and condition classification.

With the development of unmanned aerial vehicle (UAV) technology, a UAV is gradually applied to a variety of civil fields, such as photography, power line inspection, and environmental monitoring. At the same time, the safety and reliability of a UAV also attract wide attention. Anomaly detection is one of the key technologies to improve the safety of an UAV. The structure of the UAV system is complex, and there are complex spatio-temporal correlations among the high-dimensional flight data with many parameters. However, the existing methods often ignore the spatio-temporal correlation of data and lack parameter selection, which is used to abandon the parameters without a positive impact on anomaly detection results. This article proposes a spatio-temporal correlation based long short-term memory (LSTM) method for anomaly detection and recovery prediction of UAV flight data. First, an artificial neural network correlation analysis is proposed to preliminarily mine the spatio-temporal correlation in flight data and to obtain the correlation parameter sets. Second, the LSTM model is established, and the mapping among different parameters is realized. Finally, anomaly detection and recovery prediction are carried out based on parameter sets mapping model. The effectiveness of the proposed method is verified by generating sample sets with anomaly injection on real UAV flight data.

Lithium-ion rechargeable batteries are widely used in various electronic products and equipment due to their immense benefits in power supplying. The exact remaining useful life (RUL) prediction of lithium-ion batteries has shown excellent achievements in preventing severe economic and security consequences incurred in failing to provide necessary power levels. Recently, the nonlinear-drifted fractional Brownian motion made quite a splash in RUL prediction, since its first hitting time distribution can be approximated by weak convergence theorem and time-space transformation. However, the previous RUL prediction methods based on fractional Brownian motion only considered current state measurement. In this paper, a prediction framework based on nonlinear-drifted fractional Brownian motion with multiple hidden state variables is put forward to estimate RUL. Specifically, all the parameters of nonlinear function are defined as specific hidden state variables of lithium-ion battery degradation model, and all the state measurements are used to posteriorly estimate the distribution of the multiple hidden state variables by unscented particle filter algorithm. Four sets of lithium-ion battery degradation data provided by NASA Ames Research Center are used to validate the proposed prediction framework. According to comparison study with other methods, the proposed prediction framework demonstrates greater precision in the RUL prediction.