Lithium-ion battery packs are critical to ensure the operational reliability and service life of spacecraft. The battery pack health diagnosis is meaningful for reasonable mission plan and proper control actions of spacecraft. However, the characters of space application scenarios include periodic charge and discharge, nearly-constant discharge current, low electric current rate and fixed depth-of-discharge which bring challenges to battery pack state-of-health estimation. Considering these characters, this paper proposes a health diagnosis approach for lithium-ion battery pack based on an improved-equivalent-circuit-model. Firstly, an empirical model is introduced into the standard first-order equivalent-circuit-model to improve the accuracy of discharge process modeling under constant current. Further, a parameter look-up-table is developed to simulate the complete discharge based on the in-orbit partial discharge. Finally, the battery pack complete discharge process is virtually conducted to realize battery pack capacity estimation. The proposed method just satisfies the special operating conditions in space applications. The proposed method is validated based on a real tested lithium-ion battery pack. And the results indicate that the proposed method can realize the high-accurate health diagnosis for both battery pack and its inner cells under space operating conditions, which illustrate the adaptability and applicability of the proposed method in space applications.

The accurate prognostics of lithium-ion battery state of health (SOH) and remaining useful life (RUL) have great significance for reducing the costs of maintenance. The methods based on the physical models cannot perform satisfactorily as the systems become more and more complex. With the development of digital acquisition and storage technology, the data of battery cells can be obtained. This makes the data-driven methods get more and more attention. In this paper, to overcome the problem that the trend fitting deteriorates rapidly when test data are far from the training data for multiple-step-ahead estimation, a prognostic method fusing the wavelet de-noising (WD) method and the hybrid Gaussian process function regression (HGPFR) model for predicting the RUL of the lithium-ion battery is proposed. Gaussian process regression (GPR) is a typical representative for the Bayesian structure with non-parameter expression and uncertainty presentation. In this case, the effects on predictive results are compared and analyzed using the proposed method and the HGPFR model with different lengths of training data. Besides, in consideration of the degradation characteristics for the lithium-ion battery data set, the selections of the wavelet de-noising method are performed with corresponding experimental analyses. Furthermore, we set the hype-parameter for the mean function and co-variance function, and then develop a method for parameter optimization to make the proposed model suitable for the data. Moreover, a numerical simulation based on the data repository of Department of Engineering Science (DES) university of Oxford and Center for Advanced Life Cycle Engineering (CALCE) of University of Maryland is carried out, and the results are analyzed. For the data repository, an accuracy of 2.2% is obtained compared with the same value of 6.7% for the HGPFR model. What is more, the applicability and stability are verified with the prognostic results by the proposed method.

Condition Monitoring (CM) has garnered extensive attention in the era of Industry 4.0 and digital manufacturing. It is crucial to monitor the health status of machinery to ensure reliability, safety, production quality, and effectiveness. Advanced predictive maintenance strategies increase equipment availability and effectiveness by accurately predicting failures, thus facilitating maintenance engineering decisions and preventing unplanned machinery breakdowns. In this research, a novel predictive maintenance strategy is proposed by integrating anomaly detection and fault prognostics, two significant challenges in smart maintenance, into one CM system. For anomaly detection, we developed an intelligent methodology based on the Skip Convolution Generative Adversarial Network (SCGAN). This network combines a Convolutional Autoencoder (CAE), Generative Adversarial Network (GAN), and skip connections, forming a robust system to construct health indicators, effectively and efficiently tracking the degradation status of rolling element bearings with fault identified using the 3σ criterion. Validation on real experimental datasets demonstrates that the developed health indicators show a stable trend during the healthy stage and a marked increase when deterioration is detected. Subsequently, we employ an advanced Graph Isomorphic Network (GIN) for Remaining Useful Life (RUL) prediction. GIN utilizes graph data and graph convolutions to map complex relationships between degradation evolution and RUL. This approach outperforms existing deep learning models, such as Convolutional Neural Networks (CNN), Long-Short Term Memory networks (LSTM), and CNN-LSTM, providing more accurate RUL prediction.

Power system condition prediction is to predict key parameters such as voltage and current of its main components to ensure the reliability and safety of electrical system operation. However, as satellite missions and operating conditions become more complex, the state of electrical systems is affected by satellite loads and platforms. Therefore, it is difficult for offline-trained state prediction models to dynamically adapt to changing operating conditions and changing relationships between parameters. To overcome these problems, an online deep learning method, On-line Rules-limited Temporal Convolutional Network (O-R-TCN ), is proposed to improve the adaptability of the state prediction method. Firstly, a temporal convolutional network (TCN) online weight update method based on a recursive extreme learning machine is proposed, which realizes the self-learning of dynamic parameter relations under dynamic operation conditions to obtain higher prediction accuracy. Moreover, considering that anomalies within the monitored data may lead to undesired model updating, an association rule mining approach is proposed based on the Frequent Pattern-Growth (FP-Growth) fusion with trend sign aggregation approximation (TSAX). Avoid model updates caused by system exceptions or failures. Experiments are implemented based on the actual satellite power system telemetry data. The results illustrate that the proposed method can self-learn the varying relationship between the parameters and update the state prediction model during the working state change. At the same time, abnormal data can be effectively detected by the association rule, which ensures the effectiveness of the proposed method.