With the development of technologies, such as big data, cloud computing, and the Internet of Things (IoT), digital twin is being applied in industry as a precision simulation technology from concept to practice. Further, simulation plays a very important role in the healthcare field, especially in research on medical pathway planning, medical resource allocation, medical activity prediction, etc. By combining digital twin and healthcare, there will be a new and efficient way to provide more accurate and fast services for elderly healthcare. However, how to achieve personal health management throughout the entire lifecycle of elderly patients, and how to converge the medical physical world and the virtual world to realize real smart healthcare, are still two key challenges in the era of precision medicine. In this paper, a framework of the cloud healthcare system is proposed based on digital twin healthcare (CloudDTH). This is a novel, generalized, and extensible framework in the cloud environment for monitoring, diagnosing and predicting aspects of the health of individuals using, for example, wearable medical devices, toward the goal of personal health management, especially for the elderly. CloudDTH aims to achieve interaction and convergence between medical physical and virtual spaces. Accordingly, a novel concept of digital twin healthcare (DTH) is proposed and discussed, and a DTH model is implemented. Next, a reference framework of CloudDTH based on DTH is constructed, and its key enabling technologies are explored. Finally, the feasibility of some application scenarios and a case study for real-time supervision are demonstrated.

Integration of each aspect of the manufacturing process with the new generation of information technology makes industrial manufacturing systems more flexible and intelligent. Industrial big data (IBD), recording all aspects of the industrial production process, contain the key value for industrial intelligence. For industrial manufacturing, an essential and widely used electronic device is the lithium-ion battery. However, accurately predicting the remaining useful life of LIB is urgently needed to reduce unexpected maintenance and avoid accidents. Due to insufficient amount of degradation data, the prediction accuracy of data-driven methods is greatly limited. Besides, mathematical models established by model-driven methods to represent degradation process are unstable because of external factors like temperature. To solve this problem, a new Lithium-ion battery RUL prediction method, namely Auto-CNNLSTM, is proposed in this work. This method is developed based on deep convolution neural network and long short-term memory to mine deeper information in finite data.

Accurate prediction of Remaining Useful Life (RUL) of lithium-ion battery plays an increasingly crucial role in the intelligent battery health management systems. The advances in Deep Learning introduce new data-driven approaches to this problem. This paper proposes an integrated deep learning approach for RUL prediction of lithium-ion battery by integrating autoencoder with Deep Neural Network (DNN). Firstly, we present a multi-dimensional feature extraction method with autoencoder model to represent battery health degradation. Then, the RUL prediction model based DNN is trained for multi-battery remaining cycle life estimation. The proposed approach is applied to the real dataset of lithium-ion battery cycle life from NASA, and the experiment results show that the proposed approach can improve the accuracy of RUL prediction.

Cyber-physical-social system (CPSS) has drawn tremendous attention in industrial applications such as industrial Internet of Things (IIoT). As the fundamental component of IIoT, bearings play an increasingly important role in CPSS for IIoT. Better understanding of bearing working conditions and degradation patterns so as to more accurately predict the remaining useful life (RUL), becomes an urgent demand for industrial prognostics in IIoT. The data-driven approach has indicated good potential, but the prediction accuracy is still not satisfactory. This paper proposes a new method for the prediction of bearing RUL based on deep convolution neural network (CNN). A new feature extraction method is presented to obtain the eigenvector, named the spectrum-principal-energy-vector. The eigenvector is suitable for deep CNN. In the prediction phase, we propose a smoothing method to deal with the discontinuity problem found in the prediction results. To the best of our knowledge, we are the first to propose such a smoothing method for bearing RUL prediction. Experiments show that our method can significantly improve the prediction accuracy of bearing RUL.

Time-series prediction plays a crucial role in the Industrial Internet of Things (IIoT) to enable intelligent process control, analysis, and management, such as complex equipment maintenance, product quality management, and dynamic process monitoring. Traditional methods face challenges in obtaining latent insights due to the growing complexity of IIoT. Recently, the latest development of deep learning provides innovative solutions for IIoT time-series prediction. In this survey, we analyze the existing deep learning-based time-series prediction methods and present the main challenges of time-series prediction in IIoT. Furthermore, we propose a framework of state-of-the-art solutions to overcome the challenges of time-series prediction in IIoT and summarize its application in practical scenarios, such as predictive maintenance, product quality prediction, and supply chain management. Finally, we conclude with comments on possible future directions for the development of time-series prediction to enable extensible knowledge mining for complex tasks in IIoT.