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The papers in this special section focus on increasing interests in the development and implementation of advanced artificial intelligence (AI) and machine learning (ML) methods for tackling the reliability and system health prognostics challenges in various industrial applications.

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The papers in this special section focus on security and privacy issues associated with Industry 4.0. In 2011, a group of delegates from business and academia, and politics in German initially proposed the conception of the Fourth Industrial Revolution (or Industry 4.0), which aims to improve the competitive ability in the manufacturing industry of their country. Along with the emergence of the Industry

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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Advanced industrial communication systems are fundamental pillars of the ongoing digital transformation of industrial systems. Initiatives like the Industry 4.0 and the Industrial Internet Consortium aim at increasing the overall production efficiency leveraging on fast, reliable, and deterministic communication technologies. The improvements of information and communication technologies (ICT), driven

Intelligent diagnosis based on deep learning can reveal the health status of running equipment and is attracting attention for an increasing number of industrial systems. However, two challenges, namely, the construction of deep models and the accommodation of different data distributions, restrict the effective application of such methods. To bridge these gaps, this article proposes an intelligent

Globally, 14%–20% of road accidents are mainly due to driver fatigue, the causes of which are instance sickness, travelling for long distance, boredom as a result of driving along the same route consistently, lack of enough sleep, etc. This article presents a flexible analytic wavelet transform (FAWT)-based advanced machine learning method using single modality neurophysiological brain electroencephalogram

Automated measurement of the machine reliability parameters for a production system enables a continuous update of the mathematical model of the system, which can be used for various analyses toward productivity improvement. However, the continuous update may be impeded by some machines of which automated parameter measurements are out of order. Such a situation has been observed, for instance, when

N OWADAYS, billions of devices are connected to the In-ternet, enabling Internet of Things (IoT) systems widely deployed, such as smart city, smart healthcare and intelligent plant, to capture a great quantity of sensing data. Consequently, the data transmission, processing and analysis in IoT applications bring a great pressure to the central server. Fortunately, distributed intelligence becomes one

This article proposes a deep neural network (DNN) framework for multivariate deterministic power forecasting in the context of the high penetration of variable and uncertain renewable energy sources. The deep learning model is organized based on the 1-D convolutional neural network to lessen the computational burden, typical of recurrent neural network based models, and combines WaveNet and EfficientNet

Accurately predicting the short- and long-term variations of total nitrogen (TN) is vital for operating the wastewater treatment plants (WWTPs), considering the critical role TN plays in reflecting the eutrophication of wastewater. However, only a few relevant water quality parameters with limited samples can be obtained in WWTPs, which tremendously increases the difficulty in precisely predicting

Federated learning (FL) is capable of analyzing tremendous data from smart edge devices in Industrial Internet of Things (IIoTs), empowering numerous industrial applications. However, the increasing privacy concerns and deployment costs of IIoT environment have been posing new challenges for FL. This article proposes PCFed, a novel privacy-enhanced and communication-efficient FL framework to provide

Crowd counting based on density maps is generally regarded as a regression task. Deep learning is used to learn the mapping between image content and crowd density distribution. Although great success has been achieved, some pedestrians far away from the camera are difficult to be detected. And the number of hard examples is often larger. Existing methods with simple Euclidean distance algorithm indiscriminately

In next-generation network architecture, the Cybertwin drove the sixth generation of cellular networks sixth-generation (6G) to play an active role in many applications, such as healthcare and computer vision. Although the previous sixth-generation (5G) network provides the concept of edge cloud and core cloud, the internal communication mechanism has not been explained with a specific application

Recently, federated learning has received widespread attention, which will promote the implementation of artificial intelligence technology in various fields. Privacy-preserving technologies are applied to users’ local models to protect users’ privacy. Such operations make the server not see the true model parameters of each user, which opens wider door for a malicious user to upload malicious parameters

In this article, we study the online pricing and charging scheduling problem for a public electric vehicle (EV) charging station under stochastic electricity prices and renewable generation. We formulate the sequential decision making problem as a partially observable Markov decision process with continuous state and action spaces, with an objective of profit or social welfare maximization. The joint

Infrared thermography provides abundant spatiotemporal degradation information, facilitating non-contact condition monitoring. Reducing domain shift between simulated and industrial infrared images is significantly desired for leveraging labeled simulated data to tackle practical insufficiency of run-to-failure samples. Recently, adversarial-based domain adaptation (DA) techniques have aroused broad

This articleproposes an intelligent algorithm for wind turbine faults identification based on the image model of the dynamic process and the deep convolutional neural network. First, feature engineering is designed to generate the image model of a dynamic process. We performed variable refinement and data normalization preprocessing on the dataset. Then the time series data are reconstructed in the

Integrated around 2004, the concept of Energy Internet (EI) could provide new windows for the industrial power system society by incorporating the features of physical systems and cyber systems simultaneously. Technically, physical systems like electricity generation resources must be controlled and managed according to the instructions received from the cyber-systems. Being equipped by the smart grid

In this article, deep learning (DL) has attracted increasing attention for remaining useful life (RUL) prediction. However, most DL-based prognostics methods only provide deterministic RUL values while ignoring the associated epistemic and aleatoric uncertainties. In practice, it is important to know the exact confidence in model predictions for decision making. In this article, a Bayesian deep learning

Advances in communication technologies and artificial intelligence are accelerating the paradigm of industrial Internet of Things (IIoT). With IIoT enabling continuous integration of sensors and controllers with the network, intelligent analysis of the generated Big Data is a critical requirement. Although IIoT is considered a subset of IoT, it has its own peculiarities in terms of higher levels of

Mobile network operators (MNOs) allocate computing and caching resources for mobile users by deploying a central control system. Existing studies mainly use programming and heuristic methods to solve the resource allocation problem, which ignores the energy cost problem that is really significant to the MNO. To solve this problem, in this article, we design a joint computing and caching framework by

It is difficult to obtain expensive labeled data in industrial fault diagnosis applications, which easily leads to overfitting of deep learning and restricts its extensive usage. Aiming at this issue, this article proposed an improved few-shot semisupervised learning method, called semisupervised momentum prototype network (SSMPN), to realize gearbox fault diagnosis under limited labeled samples. First

With the emergence of various convolutional neural network (CNN)-based applications and the rapid growth of CNN model scale, the resource-constricted end devices can hardly deploy CNN-based applications. Current work optimizes the CNN model on edge servers and deploys the optimized model on devices in an edge computing environment. However, most of them only optimize the resource consumption within

Since smart healthcare systems are highly connected to advanced wearable devices, internet of things (IoT) and mobile internet, valuable patient information and other significant medical records are easily transmitted over the public network. The patient information and clinical records are also stored on the existing databases and local servers of hospitals and healthcare centres. These materials

The aim of this Special Issue is to share the state-of-the-art research and developments on the emerging Industry 5.0 concepts, technologies, use cases and future applications. The outstanding benefits of Industry 5.0 in terms of cost and efficiency facilitate a reality sooner than expected. However, the benefits of Industry 5.0 must not come at a price-any negative social or economic impact must be

Eco-efficient anaerobic ammonium oxidation (ANAMMOX) can eliminate toxic nutrients from wastewater and has been used in several nutrient removal technologies. However, its implementation for robust operation remains challenging because of process nonlinearity and time-variant characteristic, higher energy consumption, excess sludge produced, and biomass loss during sludge pumping. Also, sensor failure

Cloud computing has emerged as a new technological domain in the IT industry. Currently, several organizations working in various domains, such as healthcare, finance, manufacturing, smart grid, Internet of Things (IoT), and IT, are increasingly integrating cloud computing with their traditional applications. The key idea behind the usage of cloud computing in IoT is to increase efficiency without

Saliency prediction models aim to mimic the human visual system’s attention process, and the research has made significant progress due to recent advancements in deep convolution neural networks. However, the high memory requirements and intensive computational demands make these approaches less suitable for Internet-of-Things (IoT) devices, and there is a need for an improved computational efficiency

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The papers in this special section focus on artificial intelligence-enabled software defined industrial networks. With the development of intelligent manufacturing, new manufacturing modes such as personalized customization and networked collaboration have been widely developed. These new manufacturing modes require frequent data exchanges between manufacturing machines and industrial information systems

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With the expected explosive use of the Internet of Everything in sixth generation (6G), the cybertwin network is able to convert user information to digital assets and provide extensive services. However, protecting and enhancing privacy of the processed and transmitted data in cybertwin-driven 6G is still in its infancy. Federated learning (FL) is a nascent distributed machine learning paradigm that

TO BUILD a sustainable ecosystem, healthcare reinforced by the Internet of Things (IoT-Health) is a sector that makes a very useful contribution to society. With the aging of the world's population, the ability to monitor and protect people at home reduces costs and increases the quality of life. IoT healthcare has become a market with great potential, and IT giants such as IBM, Microsoft, and GE Healthcare

The mobile traffic data and resources using IoE in wireless networking have raised numerous problems in terms of performance monitoring in edge-connected devices [1]. Next-generation networks, such as 6G and cybertwin, are implemented to address these problems. Sixth-generation (6G) communication would play a vital role in supporting complex wireless interconnectivity. In order to allow millions of

This article studies the dynamic transmission-scheduling problem of rate-limited networked control systems with multiple loops. It is critical to develop an optimal-scheduling policy because, due to limited spectrum resources and energy budgets, only partial subsystems may have access to shared channels at each time step to update the plant states affected by stochastic disturbances. An adaptive event-triggered

In this article, we describe a methodology and associated models to evaluate a time sensitive collaborative robotics application enabled by wireless time sensitive networking (WTSN) capabilities. We also present a method to configure WTSN scheduling to meet the application time budget and validate it in a realistic industrial use case. We detail the methodologies for implementing and characterizing

Particle field measurement is an important topic in many industrial branches. However, there are always complex imaging scenes in the engineering experiments, resulting in severe imaging artifact, noise, and blur, such as the optical holography. In this article, we propose a primary-auxiliary coupled neural network (PANet) for 3-D holographic particle field characterization, which can obtain a comprehensive

Practical industrial processes usually have nonstationary properties, which make the monitoring more challenging because the fault information may be buried by nonstationary trends. For nonstationary processes, many methods have been proposed for fault detection based on continuous variables. However, binary variables may appear together with continuous variables in modern industrial processes. To

An adaptive machine learning (ML) based smart manufacturing interactive cyber physical human system (ICPHS) is conceptualized, designed, and implemented. One of its significant properties is an ML model that during deployment self-evolves with the streaming data in a self-labeling manner. This automated adaptive ML system is realized by leveraging the underlying causality during human machine interactions

While promoting the intelligence in industrial production, Industry 4.0 has also caused privacy leaks concurrently. As a possible solution, the existing personalized federated learning relies too much on a good global model to fine-tune or limit local drift, which lacks intelligent cognitive ability. When faced with heterogeneous data or poisoning attacks, even a few low-quality local models will affect

In this paper, we develop a deep generative approach for detecting foreign objects appearing on the rail track site without pre-defining the scope of objects. The detection procedure consists of three steps: 1) The model composed of an autoencoder and a discriminator is developed via unsupervised training based on normal rail images only; 2) The detection of abnormal rails is implemented based on the

For industrial Internet of Things (IIoT) network operators, undesired and abrupt network failure is a critical problem to be resolved quickly. In order to provide reliable communication services to devices in faulty cells, this paper proposes a distributed autonomous self-healing mechanism which allows random access based instantaneous communication to the neighbor cells. The design of self-healing