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Industrial Internet of Things data incorporate the fundamental elements of industrial processes, providing novel paradigms of predictive maintenance for complex industrial equipment. Remaining useful life prediction is critical in the predictive maintenance task of product lifecycle management, which has attracted increasing research attention. However, most existing prediction methods cannot effectively extract complex multiscale temporal patterns and cannot meet the real-time requirements of industrial sites. To address these issues, we propose a $T^{2}$ -Tensor-aided multiscale transformer for accurate and effective prediction in this article. We defined the $T^{2}$ -tensor to represent the multiscale temporal pattern by reconstructing the time series. Besides, a high-order transformer for multiscale feature extraction is proposed. Particularly, the multiscale characteristics can be captured through intertoken and intratoken. In addition, a transformer parameter lightweighting method with tensor ring decomposition is developed. Experiments demonstrate the accuracy and efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

A key step in remanufacturing is disassembly of the “core” or the returned product to be remanufactured. Disassembly sequence planning is challenging due to uncertainties in the conditions of the cores. Rust, corrosion, deformation, and missing parts may require disassembly plans to be changed and adapted frequently. Conventional industrial automation that usually serves in repetitive and structured activities may fail when it is applied to disassembly. This research investigates the flexible sequencing of robotic disassembly in the presence of failed automation operations and develops online recovery by incorporating backup actions. It starts with modeling the time and success rate of a backup action. The expected disassembly time and completion rate of a disassembly plan are deduced according to the failure probability of both the operations and their backup actions. A biobjective optimization model for robotic disassembly sequence planning is established using a dual-selection multiobjective evolutionary algorithm. Two solution selection criteria are combined to produce potential offspring candidates in each evolutionary generation. Experimental results show that the backup actions allow efficient recovery from automation and can potentially improve the robustness of robotic disassembly. Note to Practitioners—This research was motivated by the development of automated disassembly techniques. Industrial automation techniques usually use predetermined operation motions. Robotic disassembly using such an approach may fail due to uncertainties in the condition of the products (e.g., positioning and geometry). This article introduces backup actions for disassembly sequence planning and describes the logic and reasoning of their implementation. Our proposed method can theoretically increase the completion rate of automated robotic disassembly. Experimental studies suggested that backup actions are efficient in providing a reliable disassembly sequence and, thus, can improve the robustness of robotic disassembly. In future research, we will implement typical backup actions and establish an automated disassembly process with a replanning module. [ABSTRACT FROM AUTHOR]

This is the editorial of the SS entitled ‘'Digital Twinning: Integrating AI-ML and Big Data Analytics for Virtual Representation’'. [ABSTRACT FROM AUTHOR]

In the modern industry, the information has been sufficiently shared among the production equipment, intelligent subsystems, and mobile devices via advanced network technology. For this purpose, many challenges on plant-wide performance evaluation such as product quality prediction have been received considerable attention in complex industrial Internet of Things systems. In this article, an efficient and effective soft sensor based on the semisupervised parallel deepFM model is proposed for the product quality prediction. First, a label broadcasting method is presented to augment labeled samples from unlabeled samples. Then, a data binning method is introduced to discretize process variables for an unbiased estimation. Based on the modified deepFM model, quality information can be separately extracted from different components of the model while high- and low-dimensional features can be obtained. Manifold regularization is embedded into the back propagation algorithm, in which unlabeled samples issue can be further resolved. Experiments on a real-world dataset demonstrate the effectiveness and performance of the proposed methods. [ABSTRACT FROM AUTHOR]

Integration of each aspect of the manufacturing process with the new generation of information technology such as the Internet of Things, big data, and cloud computing makes industrial manufacturing systems more flexible and intelligent. Industrial big data, 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 (LIB). However, accurately predicting the remaining useful life (RUL) 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 LIB RUL prediction method based on improved convolution neural network (CNN) and long short-term memory (LSTM), namely Auto-CNN-LSTM, is proposed in this article. This method is developed based on deep CNN and LSTM to mine deeper information in finite data. In this method, an autoencoder is utilized to augment the dimensions of data for more effective training of CNN and LSTM. In order to obtain continuous and stable output, a filter to smooth the predicted value is used. Comparing with other commonly used methods, experiments on a real-world dataset demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Industrial Internet-of-Things (IIoT) has revolutionized almost every aspect of industrial manufacturing through industrial intelligence by incorporating production equipment, mobile terminals, and smart devices with wireless or wired networks. However, industrial visual information, such as images, videos, graphs, and texts, generated and collected from the industrial processes, contains various kinds of hidden value for industrial intelligence. Therefore, for the trend of providing ubiquitous industrial intelligence, new paradigms of perception and processing technologies of visual information such as recognition methods are required. However, industrial visual information is heterogeneous and complex with multiattributes, which presents significant challenges on visual information perception and processing technologies such as multiattributes recognition method. In this article, to provide industrial intelligence, a tensor-based visual feature recognition method is used to recognize the object from the perspective of multiattributes with the combination of attributes. To demonstrate its practical implementation, a case study about the industrial intelligence on the faulty location and diameter of bearings in the IIoT is described. Also, experiments on object recognition are carried out on the public image set COIL-100 to demonstrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

The industrial Internet of Things (IIoT) is growing quickly due to increasing deployment and integration of smart sensors, instruments, and devices, and software using wired or wireless networks. Through this integrated hardware–software approach, industrial practices will improve significantly, resulting in industrial intelligence for more efficient manufacturing. To realize such industrial intelligence, significant developments in IIoT big data processing and analysis are required to uncover and use hidden essential and valuable information of the production process. But large-scale, streaming, multiattribute IIoT data from production processes are noisy and have redundancies. Therefore, a suitable data processing technique such as tensor-train that can handle these IIoT data is needed. However, existing tensor-train decomposition methods are inefficient and cannot meet the processing demands of the large-scale IIoT big data. In this article, we propose an advanced (improved and highly efficient) distributed tensor-train (ADTT) decomposition method with its incremental computational method for processing IIoT big data. Finally, experiments are carried out on a typical and publicly available IIoT dataset—the bearing test data to verify and measure the performances of the proposed ADTT method. [ABSTRACT FROM AUTHOR]

Telecommunication networks are evolving toward a data-center-based architecture, which includes physical network functions, virtual network functions, as well as various types of management and orchestration systems. The primary purpose of this type of heterogeneous network is to provide efficient and convenient communication services for users. However, the diverse factors of a heterogeneous network such as bandwidth, delay, and communication protocol, bring great challenges for routing recommendations. In addition, the growing volume of big data and the explosive deployment of heterogeneous networks have started a new era of applying big data technologies to implement routing recommendations. In this article, a tensor-based big-data-driven routing recommendation framework, including the edge plane, fog plane, cloud plane, and application plane, is proposed. In this framework, a tensor-based, holistic, hierarchical approach is introduced to generate efficient routing paths using tensor decomposition methods. Also, a tensor matching method including the controlling tensor, seed tensor, and orchestration tensor is employed to realize routing recommendation. Finally, a case study is used to demonstrate the key processing procedures of the proposed framework. [ABSTRACT FROM AUTHOR]

The full duplex (FD) two-way relay channel (TWRC) has recently been shown to be a feasible way to improve the network throughput in practical communications. In this correspondence, we consider the convolutionally coded physical-layer network coding over the TWRC in which two sources work in the FD mode and there exists a direct link between them. Every source receives two packets, one packet from the other source and one network coded packet from the relay. A near-maximum-likelihood decoding algorithm for the sources is proposed. A salient feature of the proposed algorithm is that it exploits the code structure to mitigate the error propagation induced by the relay. Furthermore, we derive the near maximum-likelihood decoding bound on end-to-end BER for the system. Simulation results indicate that the performance of the proposed algorithm matches well the bound. [ABSTRACT FROM AUTHOR]

Telecommunication networks are evolving toward a data-center-based architecture, which includes physical network functions, virtual network functions, as well as various types of management and orchestration systems. The primary purpose of this type of heterogeneous network is to provide efficient and convenient communication services for users. However, the diverse factors of a heterogeneous network such as bandwidth, delay, and communication protocol, bring great challenges for routing recommendations. In addition, the growing volume of big data and the explosive deployment of heterogeneous networks have started a new era of applying big data technologies to implement routing recommendations. In this article, a tensor-based big-data-driven routing recommendation framework, including the edge plane, fog plane, cloud plane, and application plane, is proposed. In this framework, a tensor-based, holistic, hierarchical approach is introduced to generate efficient routing paths using tensor decomposition methods. Also, a tensor matching method including the controlling tensor, seed tensor, and orchestration tensor is employed to realize routing recommendation. Finally, a case study is used to demonstrate the key processing procedures of the proposed framework. [ABSTRACT FROM AUTHOR]

Cyber-physical-social systems (CPSSs) represent an emerging paradigm encompassing the cyber world, physical world and social world. One of the main purposes of CPSSs is to provide high-quality, proactive, and personalized services for humans. For CPSSs to realize this purpose, a novel services framework is needed. In this article, we present a tensor-based cloud-edge computing framework that mainly includes the cloud and edge planes. The cloud plane is used to process large-scale, long-term, global data, which can be used to obtain decision making information such as the feature, law, or rule sets. The edge plane is used to process small-scale, short-term, local data, which is used to present the real-time situation. Also, personalized services will be directly provided for humans by the edge plane according to the obtained feature, law, or rule sets and the local high-quality data obtained in the edge plane. Then a tensor-based services model is proposed to match the requirement of users in the local CPSS. Finally, a case study about CPSS services is proposed to demonstrate the application features of the proposed framework. [ABSTRACT FROM PUBLISHER]

The growing volume of network traffic and gradual deployment of SDN devices initiate a new era in which one distinguished feature is the application of big data technology to SDNs for construction of flexible, scalable, and self-managing networks. The primary purpose of this article is to develop a novel tensor-based model for efficient provisioning of QoS in software defined networks. First, a forwarding tensor model is proposed to formalize the networking functions in the data plane; then a controlling tensor model is presented for routing path recommendation in the control plane. Finally, the article introduces a transition tensor model for network traffic prediction and QoS provisioning. The three models can automatically monitor the network state, recommend routing paths and predict network traffic, respectively. A case study to recommend routing paths is investigated in the article. [ABSTRACT FROM PUBLISHER]