Nowadays, sensing and measurement are going to play an integral role in artificial intelligence (AI). With the rapid development of intelligent manufacturing and frontier equipment, there is an urgent need for providing a dedicated and unusual forum for researchers, scientists, and engineers to present their latest research findings in the area of sensing technology, measurement methodology, and data analytics approaches in the fast-changing era of AI.

Prognostics and remaining useful life (RUL) estimation for lithium-ion batteries play an important role in intelligent battery management systems (BMS). The capacity is often used as the fade indicator for estimating the remaining cycle life of a lithium-ion battery. For spacecraft requiring high reliability and long lifetime, in-orbit RUL estimation and reliability verification on ground should be carefully addressed. However, it is quite challenging to monitor and estimate the capacity of a lithium-ion battery on-line in satellite applications. In this work, a novel health indicator (HI) is extracted from the operating parameters of a lithium-ion battery to quantify battery degradation. Moreover, the Grey Correlation Analysis (GCA) is utilized to evaluate the similarities between the extracted HI and the battery’s capacity. The result illustrates the effectiveness of using this new HI for fading indication. Furthermore, we propose an optimized ensemble monotonic echo state networks (En_MONESN) algorithm, in which the monotonic constraint is introduced to improve the adaptivity of degradation trend estimation, and ensemble learning is integrated to achieve high stability and precision of RUL prediction. Experiments with actual testing data show the efficiency of our proposed method in RUL estimation and degradation modeling for the satellite lithium-ion battery application.

With the wide applications of unmanned aerial vehicle (UAV), the operating safety becomes a critical issue. Thus, fault detection has been focused, which can realize fault alarm and schedule maintenance in time. Since the accurate physical model of UAV is usually difficult to obtain, and flight data with random noise has both spatial and temporal correlation, a huge challenge is posed to fault detection. In this paper, a data-driven multivariate regression approach based on Long Short-Term Memory with residual filtering (LSTM-RF) is proposed to fulfil UAV flight data fault detection and recovery. Firstly, an LSTM network is designed as a regression model, which can extract the spatial-temporal features from the flight data and obtain an estimation of the monitored parameter. Secondly, a filter is utilized to smooth the residuals between real flight data and estimated values, which mitigates the effect of random noise and dramatically improves the detection performance. Finally, fault detection is achieved by comparing the smoothed residual with a statistical threshold. Then, fault recovery is fulfilled by replacing fault data with the estimated value. To validate the effectiveness of the proposed method, experiments are conducted based on simulation data and real flight data. The experimental results demonstrate that the proposed method has good performance in fault detection and recovery of UAV flight data.

The harmonic reducer is a key component of the industrial robot. Its reliability has significant influence on the consecutive operation of the industrial robot. However, its failure rate is high due to the complicated internal structure and long-term working conditions of large load and high torque. To identify the fault type of the harmonic reducer under different working conditions, this article proposes the joint wavelet regional correlation threshold denoising (WRCTD) algorithm and convolutional neural network-long short term memory (CNN-LSTM) fault detection method. Firstly, the WRCTD algorithm utilizes the regional correlation of the wavelet decomposition coefficients and the $3\sigma $ criterion to suppress noise in the raw sensor data. Then, the CNN-LSTM model could mine hidden features of the processed sensor data to identify fault correctly. To evaluate the proposed method, the vibration signals of the harmonic reducer under different working conditions are collected by establishing a test rig. Comparative experimental results show that the proposed WRCTD algorithm can significantly improve the accuracy of the fault detection method. The specific values for the CNN-LSTM method and the LSTM method are 9.5% and 12.2%, respectively. In addition, the proposed CNN-LSTM method has better performance than traditional methods. It achieves the fault detection over 94.0%, which is 1.5%, 2.0%, 2.0%, 1.9%, and, 1.9% higher than the highest accuracy values of CNN, LSTM, and SVM methods in five experiments.