Modern aerospace industry is migrating from reactive to proactive and predictive maintenance to increase platform operational availability and efficiency, extend its useful life cycle and reduce its life cycle cost. Multiphysics modeling together with data-driven analytics generate a new paradigm called “Digital Twin.” The digital twin is actually a living model of the physical asset or system, which continually adapts to operational changes based on the collected online data and information, and can forecast the future of the corresponding physical counterpart. This paper reviews the overall framework to develop a digital twin coupled with the industrial Internet of Things technology to advance aerospace platforms autonomy. Data fusion techniques particularly play a significant role in the digital twin framework. The flow of information from raw data to high-level decision making is propelled by sensor-to-sensor, sensor-to-model, and model-to-model fusion. This paper further discusses and identifies the role of data fusion in the digital twin framework for aircraft predictive maintenance.

Composite materials have been widely used in the aerospace industry and are critical for safe operations. However, the delamination, caused by cyclic loads and corrosive service environment, poses a serious threat to the structural integrity of composite laminates. The acoustic emission technique has been adopted to assess the structural integrity by characterizing damage location, type, and size. This paper proposes and compares data-driven prognostic methods to quantify the delamination area efficiently and accurately. To address the problem of insufficient inspection data, the prediction model adopts the path length across the delamination area as the target value. The delamination area can then be estimated with the predicted path length based on formulated geometric relationships. This solution will augment the model training data sets and consequently avoid the overfitting problem during the training process. Experimental results on composite coupons demonstrate that the proposed ensemble learning based model outperforms other state-of-the-art methods in terms of prediction accuracy and efficiency.

Although the importance of remaining useful life (RUL) prediction is widely recognized in industries, its implementation in real scenarios is highly restricted by the complexity of the degradation mechanism, uncertainty of machinery, and insufficiency of prior knowledge. To address such a challenge, this article proposes a model-based framework, which has the capability to integrate multiple predictive models via a probabilistic mechanism. When a new observation is fed into each predictive model, the posterior distribution of each model will be updated via Bayesian inference. Then, a grid-sampling strategy is applied to their posterior distributions for identifying the “peak” and “profile,” which are used for RUL prediction and reliability assessment, respectively. The effectiveness of this framework is validated with the experiments on a set of steel tension specimens. Theoretical interpretations and comparative studies demonstrate the superiority of the proposed framework. Besides, the proposed framework can not only reduce human workload on trivial parameter setting but also be effective with insufficient prior knowledge, making the intelligent RUL prediction easier.

This article presents a probabilistic framework for remaining useful life (RUL) prediction of bearings. This framework comprises two phases: 1) early fault detection, i.e., modeling the vibration signal as a series of graphs and then conducting graph spectrum analysis to detect the topological change of graph for early fault detection and 2) RUL prediction, i.e., adopting a probabilistic model for bearing RUL prediction given the detected early fault. Specifically, the least square method and noninformation distribution are employed to set the prior knowledge within this model. Meanwhile, a state-of-the-art Markov chain Monte Carlo method, No-U-Turn sampler, is investigated in posterior sampling for predicting RUL and outputting uncertainty. As a result, this framework can work without the training data from other bearings. Besides, it is an unsupervised framework that makes operators free from manual parameters setting and costly tuning runs. In addition to theoretical derivation, the proposed framework is validated using both synthetic data and real-world data and compared with the representative methods in this field. Excellent results show the effectiveness of this probabilistic framework in the RUL prediction of bearings.

Geomagnetic data forecasting plays a critical role for natural disaster institutions to respond promptly or make decisions for the magnetic storm warning, earthquake early warning, and so on. However, the forecasting accuracy is not always reliable due to the spatial correlations among different sites and the temporal correlations from each site measurement update. To address the correlation problems, a novel sparse geomagnetic data forecasting (SGCast) matrix framework is proposed in this study. To be specific, a coupled matrix factorization is proposed to model the sparse multilocation geomagnetic data and spatial correlation, which is demonstrated with an example from seven Chinese cities. After the factorization, two subspaces, location subspace, and temporal subspace are derived. Finally, the future geomagnetic signals are reconstructed based on forecasting temporal subspace and matrix reconstruction. The experimental results from the extensive comparison studies demonstrate the superiority of the proposed SGCast approach compared to the state-of-the-art approaches, with an approximate improvement of the forecasting accuracy as 10%~15%.