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Industrial development has led to the widespread production of toxic materials, including carcinogenic, mutagenic, and toxic chemicals. Even with strict management and control measures, such materials still pose threats to human health. Therefore, convenient chemical sensors are required for toxic chemical monitoring, such as optical, electrochemical, nanomaterial-based, and biological-system-based sensors. Many existing and new chemical sensors have been developed, as well as new methods based on novel technologies for detecting toxic materials. The emergence of material sciences and advanced technologies for fabrication and signal-transducing processes has led to substantial improvements in the sensing elements for target recognition and signal-transducing elements for reporting interactions between targets and sensing elements. Many excellent reviews have effectively summarized the general principles and applications of different types of chemical sensors. Therefore, this review focuses on chemical sensor advancements in terms of the sensing and signal-transducing elements, as well as more recent achievements in chemical sensors for toxic material detection. We also discuss recent trends in biosensors for the detection of toxic materials. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial digitalization and intelligence, there is an urgent need to accurately depict the physical world in digital space, and, in turn, regulate and optimize the behavior of physical entities based on massive data collection and analysis. As a technology that combines virtual space and physical space, digital twin can satisfy all of the above needs, and has attracted widespread attention. Due to the promising application prospects of digital twins, both academia and industry have launched research in this field, and related studies have been conducted from different perspectives. Accordingly, some articles summarizing the existing work have also been published, but they are all from a single perspective, lacking a systematic introduction and summary. Based on this, this paper conducts a comprehensive review of the existing work on digital twins from four perspectives: data, model, network and application, and strives to gain a better understanding of the development of the field from the physical to the virtual and back to the physical. Meanwhile, current research challenges and future directions for the development of digital twins are all discussed. [ABSTRACT FROM AUTHOR]

In the realm of Industry 4.0, diverse technologies such as AI, Cyber-Physical Systems, IoT, and advanced sensors converge to shape smarter future factories. Mobile manipulators (MMs) are pivotal, fostering flexibility, adaptability, and collaboration in industrial processes. On one hand, MMs offer a remarkable level of flexibility, adaptability, and collaboration in industrial processes, facilitating swift production line changes and efficiency enhancements. On the other hand, their integration into real manufacturing environments requires meticulous considerations, such as safety, human–robot interaction, and cybersecurity. This article delves into MMs' essential role in achieving Industry 4.0's automation and adaptability by integrating mobility with manipulation capabilities. The study reviews MMs' industrial applications and integration into manufacturing systems. The most observed applications are logistics (49%) and manufacturing (33%). As Industry 4.0 advances, the paper emphasizes updating and aligning MMs with the smart factory concept by networks of sensors and the real-time analysis of them, especially for an enhanced human–robot interaction. Another objective is categorizing considerations for MMs' utilization in Industry 4.0-aligned manufacturing. This review methodically covers a wide range of considerations and evaluates existing solutions. It shows a more comprehensive approach to understanding MMs in Industry 4.0 than previous works. Key focus areas encompass perception, data analysis, connectivity, human–robot interaction, safety, virtualization, and cybersecurity. By bringing together different aspects, this research emphasizes a more integrated view of the role and challenges of MMs in the Industry 4.0 paradigm and provides insights into aspects often overlooked. A detailed and synthetic analysis of existing knowledge was performed, and insights into their future path in Industry 4.0 environments were provided as part of the contributions of this paper. The article also appraises initiatives in these domains, along with a succinct technology readiness analysis. To sum up, this study highlights MMs' pivotal role in Industry 4.0, encompassing their influence on adaptability, automation, and efficiency. [ABSTRACT FROM AUTHOR]

With the development of industrial automation, articulated robots have gradually replaced labor in the field of bolt installation. Although the installation efficiency has been improved, installation defects may still occur. Bolt installation defects can considerably affect the mechanical properties of structures and even lead to safety accidents. Therefore, in order to ensure the success rate of bolt assembly, an efficient and timely detection method of incorrect or missing assembly is needed. At present, the automatic detection of bolt installation defects mainly depends on a single type of sensor, which is prone to mis-inspection. Visual sensors can identify the incorrect or missing installation of bolts, but it cannot detect torque defects. Torque sensors can only be judged according to the torque and angel information, but cannot accurately identify the incorrect or missing installation of bolts. To solve this problem, a detection method of bolt installation defects based on multiple sensors is proposed. The trained YOLO (You Only Look Once) v3 network is used to judge the images collected by the visual sensor, and the recognition rate of visual detection is up to 99.75%, and the average confidence of the output is 0.947. The detection speed is 48 FPS, which meets the real-time requirement. At the same time, torque and angle sensors are used to judge the torque defects and whether bolts have slipped. Combined with the multi-sensor judgment results, this method can effectively identify defects such as missing bolts and sliding teeth. Finally, this paper carried out experiments to identify bolt installation defects such as incorrect, missing torque defects, and bolt slips. At this time, the traditional detection method based on a single type of sensor cannot be effectively identified, and the detection method based on multiple sensors can be accurately identified. [ABSTRACT FROM AUTHOR]

The development of prognostics and health management solutions in the manufacturing industry has lagged behind academic advances due to a number of practical challenges. This work proposes a framework for the initial development of industrial PHM solutions that is based on the system development life cycle commonly used for software-based applications. Methodologies for completing the planning and design stages, which are critical for industrial solutions, are presented. Two challenges that are inherent to health modeling in manufacturing environments, data quality and modeling systems that experience trend-based degradation, are then identified and methods to overcome them are proposed. Additionally included is a case study documenting the development of an industrial PHM solution for a hyper compressor at a manufacturing facility operated by The Dow Chemical Company. This case study demonstrates the value of the proposed development process and provides guidelines for utilizing it in other applications. [ABSTRACT FROM AUTHOR]

Accuracy becomes progressively important in the wake of development in advanced industrial equipment. A key position sensor to such a quest is the optical linear encoder. Occasionally, inappropriate mounting can cause errors greater than the accuracy grade of the optical linear encoder itself, especially for open-type optical linear encoders, where the mounting distance between the reading head and main scale must be accurately controlled. This paper analyzes the diffraction fields of a traditional scanning reticle made by amplitude grating and a newly designed combined grating; the latter shows a more stable phase in mathematical calculation and simulations. The proposed combined gratings are fabricated in a laboratory and assembled into the reading heads. The experimental results indicate that the mounting tolerance between the reading head and the main scale of the optical linear encoder can be improved. [ABSTRACT FROM AUTHOR]

As industrial development increases, electric machine systems are more widely used in industrial production. Rolling bearings play a key role in machine systems and so the prevention of faults in rolling bearings is more important than ever before. Recently, with the development of artificial intelligence, neural networks have been used to monitor the remaining useful life of rolling bearings. However, there are two problems with this technique. First, a network trained by data for a single operating condition (source domain) cannot predict the remaining useful life of bearings under a different operating condition (target domain), such as a different load or speed. Second, a large number of labeled data are needed for network training, but the acquisition of labeled data for different operating conditions is a challenging task. To address these problems, this paper proposes a domain-adaptive adversarial network, in which a transfer learning strategy and maximum mean discrepancy algorithm are used for network optimization, so that remaining useful life can be predicted without labeled data in target domain training. Our results confirm that a model trained by source domain data alone cannot predict the remaining useful life of bearings under different conditions, but the domain-adaptive adversarial network can accurately predict remaining useful life for varying operating conditions. The method proposed also exhibits good performance even if there are noises in the signals. [ABSTRACT FROM AUTHOR]

With the development of the Industrial Internet of Things (IIoT), industrial wireless sensors need to upload the collected private data to the cloud servers, resulting in a large amount of private data being exposed on the Internet. Private data are vulnerable to hacking. Many complex wireless-sensor-authentication protocols have been proposed. In this paper, we proposed an efficient authentication protocol for IIoT-oriented wireless sensor networks. The protocol introduces the PUF chip, and uses the Bloom filter to save and query the challenge–response pairs generated by the PUF chip. It ensures the security of the physical layer of the device and reduces the computing cost and communication cost of the wireless sensor side. The protocol introduces a pre-authentication mechanism to achieve continuous authentication between the gateway and the cloud server. The overall computational cost of the protocol is reduced. Formal security analysis and informal security analysis proved that our proposed protocol has more security features. We implemented various security primitives using the MIRACL cryptographic library and GMP large number library. Our proposed protocol was compared in-depth with related work. Detailed experiments show that our proposed protocol significantly reduces the computational cost and communication cost on the wireless sensor side and the overall computational cost of the protocol. [ABSTRACT FROM AUTHOR]

Network slicing (NS) is one of the most prominent next-generation wireless cellular technology use cases, promising to unlock the core benefits of 5G network architecture by allowing communication service providers (CSPs) and operators to construct scalable and customized logical networks. This, in turn, enables telcos to reach the full potential of their infrastructure by offering customers tailored networking solutions that meet their specific needs, which is critical in an era where no two businesses have the same requirements. This article presents a commercial overview of NS, as well as the need for a slicing automation and orchestration framework. Furthermore, it will address the current NS project objectives along with the complex functional execution of NS code flow. A summary of activities in important standards development groups and industrial forums relevant to artificial intelligence (AI) and machine learning (ML) is also provided. Finally, we identify various open research problems and potential answers to provide future guidance. [ABSTRACT FROM AUTHOR]

Due to the rapid development of industrial internet technology, the traditional manufacturing industry is in urgent need of digital transformation, and one of the key technologies to achieve this is multi-source data fusion. For this problem, this paper proposes an improved entropy-weighted topsis method for a multi-source data fusion evaluation system. It adds a fusion evaluation system based on the decision-level fusion algorithm and proposes a dynamic fusion strategy. The fusion evaluation system effectively solves the problem of data scale inconsistency among multi-source data, which leads to difficulties in fusing models and low fusion accuracy, and obtains optimal fusion results. The paper then verifies the effectiveness of the fusion evaluation system through experiments on the multilayer feature fusion of single-source data and the decision-level fusion of multi-source data, respectively. The results of this paper can be used in intelligent production and assembly plants in the discrete industry and provide the corresponding management and decision support with a certain practical value. [ABSTRACT FROM AUTHOR]

The application of metal oxide gas sensors in Internet of Things (IoT) devices and mobile platforms like wearables and mobile phones offers new opportunities for sensing applications. Metal-oxide (MOx) sensors are promising candidates for such applications, thanks to the scientific progresses achieved in recent years. For the widespread application of MOx sensors, viable commercial offerings are required. In this publication, the authors show that with the new Sensirion Gas Platform (SGP) a milestone in the commercial application of MOx technology has been reached. The architecture of the new platform and its performance in selected applications are presented. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear intelligent control of a two link robot arm by considering human voluntary components. In general, human arm viscoelastic properties are regulated in different manners according to various task requirements. The viscoelasticity consists of joint stiffness and viscosity. The research of the viscoelasticity can improve the development of industrial robots, rehabilitation and sports etc. So far, some results have been shown using filtered human arm viscoelasticity measurements. That is, human motor command is removed. As a result, the dynamics of human voluntary component during movements is omitted. In this paper, based on the feedforward characteristics of human multi joint arm, a model is obtained by considering human voluntary components using a support vector regression technique. By employing the learned model, a nonlinear intelligent control of two link robot arm is proposed. Experimental results confirm the effectiveness of this proposal. [ABSTRACT FROM AUTHOR]

The development of industrial robotics requires the use of increasingly sophisticated control algorithms. In modern tasks posed by industry, it is not sufficient for the manipulator to move along a programmed path, reaching individual points with the greatest accuracy. There is a need for solutions that can allow detection and avoidance of obstacles appearing on the robot's path and that can compensate the path for low-repetitive workpieces, adjust the strength of the impact of manipulator tools on the workpiece or enable safe cooperation of manipulators with people. To support this development, this work proposes an interface for controlling industrial robots in the Simulink environment. With its use, we can easily test our control algorithms using an external controller without the need to write an extensive program in the RAPID language. The robot controller's task is to control the drives to achieve the set trajectory. [ABSTRACT FROM AUTHOR]

The world is living the fourth industrial revolution, marked by the increasing intelligence and automation of manufacturing systems. Nevertheless, there are types of tasks that are too complex or too expensive to be fully automated, it would be more efficient if the machines were able to work with the human, not only by sharing the same workspace but also as useful collaborators. A possible solution to that problem is on human–robot interaction systems, understanding the applications where they can be helpful to implement and what are the challenges they face. This work proposes the development of an industrial prototype of a human–machine interaction system through Augmented Reality, in which the objective is to enable an industrial operator without any programming experience to program a robot. The system itself is divided into two different parts: the tracking system, which records the operator's hand movement, and the translator system, which writes the program to be sent to the robot that will execute the task. To demonstrate the concept, the user drew geometric figures, and the robot was able to replicate the operator's path recorded. [ABSTRACT FROM AUTHOR]

Water is one of the most precious resources. However, industrial development has made water pollution a critical problem today and thus water quality monitoring and surface cleaning are essential for water resource protection. In this study, we have used the sensor fusion technology as a basis to develop a multi-function unmanned surface vehicle (MF-USV) for obstacle avoidance, water-quality monitoring, and water surface cleaning. The MF-USV comprises a USV control unit, a locomotion module, a positioning module, an obstacle avoidance module, a water quality monitoring system, a water surface cleaning system, a communication module, a power module, and a remote human–machine interface. We equip the MF-USV with the following functions: (1) autonomous obstacle detection, avoidance, and navigation positioning, (2) water quality monitoring, sampling, and positioning, (3) water surface detection and cleaning, and (4) remote navigation control and real-time information display. The experimental results verified that when the floating garbage located in the visual angle ranged from −30° to 30° on the front of the MF-USV and the distances between the floating garbage and the MF-USV were 40 and 70 cm, the success rates of floating garbage detection are all 100%. When the distance between the floating garbage and the MF-USV was 130 cm and the floating garbage was located on the left side (15°~30°), left front side (0°~15°), front side (0°), right front side (0°~15°), and the right side (15°~30°), the success rates of the floating garbage collection were 70%, 92%, 95%, 95%, and 75%, respectively. Finally, the experimental results also verified that the applications of the MF-USV and relevant algorithms to obstacle avoidance, water quality monitoring, and water surface cleaning were effective. [ABSTRACT FROM AUTHOR]

The industrial development of the last few decades has prompted an increase in the number of vehicles by multiple folds. With the increased number of vehicles on the road, safety has become one of the primary concerns. Inter vehicular communication, specially Vehicle to Everything (V2X) communication can address these pressing issues including autonomous traffic systems and autonomous driving. The reliability and effectiveness of V2X communication greatly depends on communication architecture and the associated wireless technology. Addressing this challenge, a device-to-device (D2D)-based reliable, robust, and energy-efficient V2X communication architecture is proposed with LoRa wireless technology. The proposed system takes a D2D communication approach to reduce the latency by offering direct vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, rather than routing the data via the LoRa WAN server. Additionally, the proposed architecture offers modularity and compact design, making it ideal for legacy systems without requiring any additional hardware. Testing and analysis suggest the proposed system can communicate reliably with roadside infrastructures and other vehicles at speeds ranging from 15–50 km per hour (kmph). The data packet consists of 12 bytes of metadata and 28 bytes of payload. At 15 kmph, a vehicle sends one data packet every 25.9 m, and at 50 kmph, it sends the same data packet every 53.34 m with reliable transitions. [ABSTRACT FROM AUTHOR]

Continuous development of the Industrial Internet of Things (IIoT) has opened up enormous opportunities for the engineers to enhance the efficiency of the machines. Despite the development, many industry administrators still fear to use Internet for operating their machines due to untrusted nature of the communication channel. The utilization of internet for managing industrial operations can be widespread adopted if the authentication of the entities are performed and trust is ensured. The traditional schemes with their inherent security issues and other complexities, cannot be directly deployed to resource constrained network devices. Therefore, we have proposed a strong mutual authentication and secret key exchange protocol to address the vulnerabilities of the existing schemes. We have used various cryptography operations such as hashing, ciphering, and so forth, for providing secure mutual authentication and secret key exchange between different entities to restrict unauthorized access. Performance and security analysis clearly demonstrates that the proposed work is energy efficient (computation and communication inexpensive) and more robust against the attacks in comparison to the traditional schemes. [ABSTRACT FROM AUTHOR]

The structural integrity assessment of thermoplastic pipes has become an interesting area of research due to its elevated usage in the liquid/gas transportation industry. Ultrasonic guided wave testing has gained higher attention from industry for the inspection of elongated structures due to the reduced inspection time and cost associated with conventional non-destructive testing techniques, e.g., ultrasonic testing, radiography, and visual inspection. Current research addresses the inspection of thermoplastic pipes using ultrasonic guided waves as a low cost and permanently installed structural health-monitoring tool. Laboratory and numerical investigations were conducted to study the potential of using ultrasonic guided waves to assess the structural health of thermoplastic pipe structures in order to define optimum frequency range for inspection, array design, and length of inspection. In order to achieve a better surface contact, flexible Macro-Fiber Composite transducers were used in this investigation, and the Teletest® Focus+ system was used as the pulser/receiver. Optimum frequency range of inspection was at 15−25 kHz due to the level of attenuation at higher frequencies and the larger dead zone at lower frequencies due to the pulse length. A minimum of 14 transducers around the circumference of a 3 inch pipe were required to suppress higher order flexural modes at 16 kHz. According to the studied condition, 1.84 m of inspection coverage could be achieved at a single direction for pulse-echo, which could be improved by using a higher number of transducers for excitation and using pitch-catch configuration. [ABSTRACT FROM AUTHOR]

With the rapid development of industrial internet of thing (IIoT), the distributed topology of IIoT and resource constraints of edge computing conduct new challenges to traditional data storage, transmission, and security protection. A distributed trust and allocated ledger of blockchain technology are suitable for the distributed IIoT, which also becomes an effective method for edge computing applications. This paper proposes a resource constrained Layered Lightweight Blockchain Framework (LLBF) and implementation mechanism. The framework consists of a resource constrained layer (RCL) and a resource extended layer (REL) blockchain used in IIoT. We redesign the block structure and size to suit to IIoT edge computing devices. A lightweight consensus algorithm and a dynamic trust right algorithm is developed to improve the throughput of blockchain and reduce the number of transactions validated in new blocks respectively. Through a high throughput management to guarantee the transaction load balance of blockchain. Finally, we conducted kinds of blockchain simulation and performance experiments, the outcome indicated that the method have a good performance in IIoT edge application. [ABSTRACT FROM AUTHOR]

The development of humidity sensors with simple transduction principles attracts considerable interest by both scientific researchers and industrial companies. Capacitive humidity sensors, based on polyimide sensing material with different thickness and surface morphologies, are prepared. The surface morphology of the sensing layer is varied from flat to rough and then to nanostructure called nanograss by using an oxygen plasma etch process. The relative humidity (RH) sensor selectively responds to the presence of water vapor by a capacitance change. The interaction between polyimide and water molecules is studied by FTIR spectroscopy. The complete characterization of the prepared capacitive humidity sensor performance is realized using a gas mixing setup and an evaluation kit. A linear correlation is found between the measured capacitance and the RH level in the range of 5 to 85%. The morphology of the humidity sensing layer is revealed as an important parameter influencing the sensor performance. It is proved that a nanograss-like structure is the most effective for detecting RH, due to its rapid response and recovery times, which are comparable to or even better than the ones of commercial polymer-based sensors. This work demonstrates the readiness of the developed RH sensor technology for industrialization. [ABSTRACT FROM AUTHOR]