Intelligent Industrial Systems Research Articles

Predictive-Adaptive Maintenance Applied for Optimizing the Performance of Industrial Electrical Systems and Equipment

Abstract In the Industry 4.0 era, predictive maintenance became a crucial element in ensuring the efficiency and reliability of intelligent industrial systems. This paper proposes a critical study on the role and benefits of predictive maintenance in the context of optimizing and enhancing the performance of industrial electrical systems, more specific the on the asynchronous machine, highlighting emerging perspectives and challenges associated with the implementation of this advanced technology. Additionally, it brings to the forefront the latest concepts and solutions in predictive maintenance to provide a more comprehensive and conclusive view at the time of conducting this case study.

Robust condition identification against label noise in industrial processes based on trusted connection dictionary learning

In the era of big data, the pervasive use of artificial intelligence (AI) technology has revolutionized various industries. AI-powered systems, particularly those utilizing data-driven interpretable and reliable methods for accurate condition identification, have emerged as a crucial factor in achieving intelligent industrial systems. However, in real industrial sites, the labels of offline data often contain incorrect labels, i.e. label noise, due to human error, sensor failure, and other sources, which reduces the accuracy of the model and affects the performance and safety of the industrial system. To tackle the challenge of precarious performance caused by label noise, this paper proposes a robust method called Trusted Connection Dictionary Learning (TCDL) which effectively mitigates the adverse effects of label noise through interpretable constraints and guarantees the trustworthiness of the model for industrial applications. Specifically, to address the issue of data structure and label inconsistency arising from label noise, this paper presents a dynamic graph model that jointly optimizes data features and label information to obtain consistent representations of them. Subsequently, to effectively fit the labeled data, TCDL leverages the label information from the nearest neighbors through the optimized graph structure and facilitates the self-correction of label noise, leading to robust representations of process features and a reliable condition classifier. Finally, a novel iterative optimization algorithm that combines the Block Coordinate Descent (BCD) method and the Alternating Direction Multiplier Method (ADMM) is also developed to ensure the convergence of the algorithm. Due to simple constraints, it can obtain accurately optimized variables with few iterations. Numerical simulations and industrial experiments demonstrate the superiority of the proposed method compared to some state-of-the-art methods.

A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES

A general Boolean semantic modelling approach for complex and intelligent industrial systems in the framework of DES

Towards intelligent industrial systems: A comprehensive survey of sensor fusion techniques in IIoT

Industrial Internet of Things (IIoT) is systems aim to facilitate human monitoring and the direction of efficient production of goods in industrial settings by linking a wide variety of intelligent devices such as sensors, actuators, and controllers. This is achieved by utilizing Internet of Things (IoT) to diagnose a problem with a specific IIoT part is to employ a basic diagnostic technique that's based on models and data. Physical models, signal patterns, and machine-learning strategies must be adequately built to account for system challenges. Another factor that could lead to an exponential rise in complexity is the ever-increasing interconnections between different electronic hardware. The knowledge-based defect diagnosis methods boost interoperability in the operation. Users don't need to be experts in the field to benefit from the system's high-level thinking and response to their queries. So, in advanced IIoT systems, a knowledge-based fault diagnostic approach is favored over traditional model-based and data-driven diagnosis methods. The goal of this study is to evaluate recent improvements in the design of knowledge-based defect detection in the context of IIoT systems, deductive and inductive reasoning, and many other forms of logical reasoning. IIoT-based systems have revolutionized industrial settings by connecting intelligent devices such as sensors, actuators, and controllers to enable efficient production and human monitoring. In this survey paper, we explore machine learning-based sensor fusion techniques within the realm of Industrial Internet of Things (IIoT), addressing critical challenges in fault detection and diagnosis.

Towards Industry 4.0: Color-Based Object Sorting Using a Robot Arm and Real-Time Object Detection

With the introduction of Industry 4.0, automation and robotics have made great strides, enabling 
 enterprises to improve their manufacturing processes for increased productivity and efficiency. This project 
 introduces a novel method for implementing Industry 4.0 concepts through color-based object sorting employing 
 a robot arm with real-time object identification capabilities. Creating a reliable and effective system that can 
 automatically categorize items based on their color properties is the main goal of this project. To enable seamless 
 object recognition and manipulation in real time, the suggested system integrates robotic manipulation with 
 computer vision algorithms. The system makes use of a convolutional neural network (CNN) for precise object 
 detection, using recent advancements in deep learning and image processing, allowing the robot arm to interact 
 with a variety of items effectively. The training phase and the sorting phase are the two key phases of the 
 approach. The CNN model is trained on a sizable dataset of labeled objects during the training phase to recognize 
 various colors and forms. In order for the robotic arm to recognize things as they go along the conveyor belt and 
 sort them into predetermined bins according to their respective colors, the trained model must be integrated with 
 the robotic arm during the sorting phase. Several experiments are carried out with various lighting setups and 
 object arrangements to evaluate the performance of the suggested system. The outcomes show how well the 
 system performs in terms of exact object detection and reliable sorting. The system's capacity to effectively 
 handle a variety of objects and adapt to changing environmental conditions further emphasizes its suitability for 
 use in actual industrial scenarios. This project has important ramifications for the manufacturing sector, enabling 
 improved automation capabilities and cost-efficiency. An important step towards implementing Industry 4.0 
 principles is the seamless integration of color-based object sorting and real-time object detection using a robotic 
 arm. This will allow industries to optimize their production processes, minimize human intervention, and 
 increase overall productivity. Further developments in robotics and computer vision are anticipated to push the 
 limits of automation and open the door for more advanced and intelligent industrial systems as technology 
 develops.

Ensemble-Based Deep Learning Models for Enhancing IoT Intrusion Detection

Cybersecurity finds widespread applications across diverse domains, encompassing intelligent industrial systems, residential environments, personal gadgets, and automobiles. This has spurred groundbreaking advancements while concurrently posing persistent challenges in addressing security concerns tied to IoT devices. IoT intrusion detection involves using sophisticated techniques, including deep learning models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and anomaly detection algorithms, to identify unauthorized or malicious activities within IoT ecosystems. These systems continuously monitor and analyze network traffic and device behavior, seeking patterns that deviate from established norms. When anomalies are detected, security measures are triggered to thwart potential threats. IoT intrusion detection is vital for safeguarding data integrity, ensuring users’ privacy, and maintaining critical systems’ reliability and safety. As the IoT landscape evolves, effective intrusion detection mechanisms become increasingly essential to mitigate the ever-growing spectrum of cyber threats. Practical security approaches, notably deep learning-based intrusion detection, have been introduced to tackle these issues. This study utilizes deep learning models, including convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs), while introducing an ensemble deep learning architectural framework that integrates a voting policy within the model’s structure, thereby facilitating the computation and learning of hierarchical patterns. In our analysis, we compared the performance of ensemble deep learning classifiers with traditional deep learning techniques. The standout models were CNN-LSTM and CNN-GRU, achieving impressive accuracies of 99.7% and 99.6%, along with exceptional F1-scores of 0.998 and 0.997, respectively.

Novel Intelligent System Based on Automated Machine Learning for Multiobjective Prediction and Early Warning Guidance of Biogas Performance in Industrial-Scale Garage Dry Fermentation

Industrial-scale garage dry fermentation systems are extremely nonlinear, and traditional machine learning algorithms have low prediction accuracy. Therefore, this study presents a novel intelligent system that employs two automated machine learning (AutoML) algorithms (AutoGluon and H2O) for biogas performance prediction and Shapley additive explanation (SHAP) for interpretable analysis, along with multiobjective particle swarm optimization (MOPSO) for early warning guidance of industrial-scale garage dry fermentation. The stacked ensemble models generated by AutoGluon have the highest prediction accuracy for digester and percolate tank biogas performances. Based on the interpretable analysis, the optimal parameter combinations for the digester and percolate tank were determined in order to maximize biogas production and CH4 content. The optimal conditions for the digester involve maintaining a temperature range of 35–38 °C, implementing a daily spray time of approximately 10 min and a pressure of 1000 Pa, and utilizing a feedstock with high total solids content. Additionally, the percolate tank should be maintained at a temperature range of 35–38 °C, with a liquid level of 1500 mm, a pH range of 8.0–8.1, and a total inorganic carbon concentration greater than 13.8 g/L. The software developed based on the intelligent system was successfully validated in production for prediction and early warning, and MOPSO-recommended guidance was provided. In conclusion, the novel intelligent system described in this study could accurately predict biogas performance in industrial-scale garage dry fermentation and guide operating condition optimization, paving the way for the next generation of intelligent industrial systems.

Analysis of Networks and Digital Systems by Using the Novel Technique Based on Complex Fuzzy Soft Information

In the last few years, intelligent industrial systems and consumer electronics are being developed extensively and broadly. Fuzzy logic and fuzzy techniques that integrate these approaches are recognized as effective ways to realize such intelligent functions. These intelligent systems rely on fuzzy control techniques i.e., common sense rules to describe system behavior instead of complex mathematical models. This work provides solutions to such uncertainties and aids in the relative decision-making processes. To this end, an innovative concept of complex fuzzy soft relation (CFSR) is defined based on the Cartesian product (CP) between two complex fuzzy soft sets (CFSSs). The main goal of this analysis is to develop some new and valuable concepts which will be suitable for managing awkward and unreliable information in real-life problems. The proposed idea is more dominant and superior to the prevailing ideas, where the presented idea is the modified technique of two different theories, called fuzzy set (FS) and soft set (SS). In addition, some reliable results have also been proved. Moreover, an application of the proposed techniques is proposed which demonstrates the process of choosing a good network. Finally, the advantages of the proposed framework are explained by the comparison with pre-existing frameworks.

Learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low- cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments.

Preface

14th International conference on Electron Beam Technologies - EBT 2022 26 June – 1 July 2022, St. Konstantin & Elena, Estreya Hotels, Varna, Bulgaria The current 14-th EBT 2022 conference was held in a hybrid form in the period 26 June – 1 July 2022, traditionally in Varna, Bulgaria at St. Konstantin & Elena, Estreya Hotels. The conference program included 43 presentations from above 130 authors from 17 countries from 3 continents: Germany, Bulgaria, India, France, Belarus, Slovakia, Poland, United Kingdom, Japan, Latvia, Pakistan, Czech Republic, Algeria, China, Hungary, Russian Federation, Romania. The published in this issue EBT 2022 papers are devoted to the science, investigation, application and/or the equipment for additive manufacturing, electron beam lithography, electron beam welding, electron beam melting and refining, building nanowires and coatings, the new advanced environmental and biotechnological applications, building intelligent industrial control systems, based on the development of cyber-physical systems, management operation systems and model-based software applications.

HMACCE: Establishing Authenticated and Confidential Channel From Historical Data for Industrial Internet of Things

Industrial Internet of Things (IIoT) is a new paradigm for building intelligent industrial control systems, and how to establish a secure channel in IIoT for machine-to-machine (M2M) communication is a critical problem because the devices in IIoT suffer from various attacks and may leak confidential information. Traditional authenticated and confidential channel establishment (ACCE) protocols neither apply for resource-constrained IIoT devices nor satisfy leakage resilience. In this paper, we introduce a new security notion: historical data based multi-factor ACCE (HMACCE) to address this issue and propose two HMACCE protocols. Our HMACCE protocols use three authentication factors, i.e., a symmetric secret key, historical data, and a set of secret tags associated with the historical data, to establish a secure communication channel between the client and the server. The key idea is to use the secret key managed by an IIoT edge device to quickly verify the relationship between the historical data and its associated tags stored on the server. Our HMACCE has the following remarkable features. First, it is lightweight and tailored for resource-constrained IIoT devices. Second, it is bounded historical tag leakage resilience, which means that if a small portion of the secret tags is leaked to an adversary, it will not affect its security with an overwhelming probability. Moreover, as a security enhancement service, our HMACCE can be easily integrated with legacy IIoT devices by running simple authenticated key exchange protocols.

Intrusion Detection in IoT Using Deep Learning.

Cybersecurity has been widely used in various applications, such as intelligent industrial systems, homes, personal devices, and cars, and has led to innovative developments that continue to face challenges in solving problems related to security methods for IoT devices. Effective security methods, such as deep learning for intrusion detection, have been introduced. Recent research has focused on improving deep learning algorithms for improved security in IoT. This research explores intrusion detection methods implemented using deep learning, compares the performance of different deep learning methods, and identifies the best method for implementing intrusion detection in IoT. This research is conducted using deep learning models based on convolutional neural networks (CNNs), long short-term memory (LSTM), and gated recurrent units (GRUs). A standard dataset for intrusion detection in IoT is considered to evaluate the proposed model. Finally, the empirical results are analyzed and compared with the existing approaches for intrusion detection in IoT. The proposed method seemed to have the highest accuracy compared to the existing methods.

Visuomotor Reinforcement Learning for Multirobot Cooperative Navigation

This article investigates the multirobot cooperative navigation problem based on raw visual observations. A fully end-to-end learning framework is presented, which leverages graph neural networks to learn local motion coordination and utilizes deep reinforcement learning to generate visuomotor policy that enables each robot to move to its goal without the need of environment map and global positioning information. Experimental results show that, with a few tens of robots, our approach achieves comparable performance with the state-of-the-art imitation learning-based approaches with bird-view state inputs. We also illustrate our generalizability to crowded and large environments and our scalability to ten times number of the training robots. In addition, we demonstrate that our model trained for multirobot case can also improve the success rate in the single-robot navigation task in unseen environments. Note to Practitioners—With the development of intelligent industrial and logistic systems, robotic transportation systems are widely implemented. However, existing multirobot path coordination and navigation approaches are basically under some unreasonable assumptions, which are very hard to be implemented in practical scenarios. This article aims to greatly promote the real application of learning-based multirobot cooperative navigation approach, in order to achieve the following. First, we introduce an end-to-end reinforcement learning framework instead of the commonly used imitation learning strategy, as the latter one needs exhaustive training data to cover all the scenarios and does not have the required generalizability. Second, we directly use the raw sensor data instead of the commonly used bird-eye-view semantic observations, as the latter one is generally not representative of practical application scenario from the robot perspective and cannot solve the occlusion issue. Third, we interpret our learned model to illustrate which parts of the input and shared observations contribute most to the robots’ final actions. The above interpretability ensures predictability (thus safety) of our visuomotor policy in practical applications. Our learned visuomotor policy has the ability to coordinate dozens of robots by only using raw visual observations in unknown environments without map nor global localization information, this is the first time in the literature. Our future work includes solving the sim-to-real issue and conducting physical experiments.

Decentralized Self-Organizing Networks for Poor Reconfigurability of Loom Networks

The intelligent weaving of looms requires networking and intelligent industrial production systems that use industrial equipment with network communication and data computing, creating a “smart factory”. However, limited by the development of industrial technology, existing industrial loom equipment does not have sufficient communication or computing capabilities: communications primarily rely on bus and Ethernet communication, which is limited to wired connections; and computations primarily rely on the host computers and cloud computers that only provide weak real-time computing. Currently, the remote management system used in the loom industry can be constructed using a cloud platform, but machine-machine interconnections between the loom control terminals are ignored. To solve the problem of poor reconfigurability in loom networks that rely on cloud servers, this study analyzes the networking requirements of a loom network and uses mesh network technology to build a wireless self-organizing loom network. A mesh network can be formed with clouds, loom equipment, and other equipment. By constructing an experimental platform with a decentralized self-organizing network to build a remote loom monitoring system, an anti-destruction experiment of the developed self-organizing loom network is performed. After the self-organizing network is constructed, key nodes in the network are disconnected, and the network is automatically reconfigured to resume normal work. The reconfigurability and stability of the network are demonstrated via destructive experiments. Results show that the self-organizing network construction method developed in this study can build a loom self-organizing network, which is anti-destructive and mitigate the poor reconfigurability of existing loom networks. Compared with the wireless communication network of the previous loom remote monitoring system, the loom interconnection self-organizing network is more reliable and anti-interference, which can better meet the network demand of intelligent manufacturing for loom equipment. This study provides an effective example of loom networking technology for the industry and is important when promoting the construction of smart loom factories.

MATRIX FACTORIZATION OF BIG DATA IN THE INDUSTRIAL SYSTEMS

The creation of new technologies and their implementation in various fields necessitated Big Data processing and storage. In industrial systems, modernization means the use of a large number of smart devices that perform specialized functions. Data from such devices are used to control the system and automate production processes. A change in the parameters of individual components of the manufacturing system may indicate the need to adjust the global management strategy. The intelligent industrial systems main characteristics were defined in the paper. The Industrial Internet of Things concept and the relevance of the modernization problem for manufacturing were analyzed. The problems of processing Big Data in Industrial Internet of Things systems were examined in the paper. The use of recommendation systems for quickly finding relationships between users and production services was considered. The advantages of Big Data analysis by recommendation systems, which have a favourable effect on industrial enterprise efficiency were given. The use of SVD and FunkSVD matrix factorization algorithms for processing sparse data matrices was analyzed. The possibility of optimizing arrays of information, choosing the most important, and rejecting redundancy with the help of the above algorithms was determined. The proposed algorithms were simulated. The advantages of FunkSVD for working with sparse data were assigned. It was found that the FunkSVD algorithm processes the data in a shorter time than SVD, but this does not affect the accuracy of the result. The SVD is also more difficult to implement and it requires more computing resources was established. It has been shown that FunkSVD uses a lot of data to determine the relationships between it and make recommendations about the products most likely to be of interest to users. To increase the efficiency of processing large sets of information the FunkSVD algorithm was improved in such a way that it uses fewer data to generate recommendations. Based on the results of the research, the modified method works faster than the non-modified one but retains high calculation accuracy, which is important for work in recommender systems. The possibility of providing recommendations to users of industrial systems in a shorter period, thus improving their relevance, was revealed. It was proposed to continue research for finding the optimal parameters of the FunkSVD algorithm for Big Data processing.

Towards decision support systems for energy management in the smart industry and Internet of Things

Decision Support Systems (DSS) have widely provided on intelligent human–machine interactions like the Internet of Things (IoT). As the dimensions and complexity of the IoT communications between intelligent devices, industrial equipment, sensors, and mobile applications are continuously increasing, supporting Service Level Agreements (SLA) for a large amount of cloud data centers and a huge number of user requests is going to be more challenging. This challenge would be more complicated when the energy consumption of the industrial IoT ecosystems increases exponentially as well. Therefore, DSS models are required for automated decision-making in critical IoT environments like intelligent industrial systems and smart cities. This paper provides a comprehensive review of the DSS strategies for energy-efficient IoT applications in intelligent urban computing. The main objective of this review is (1) presenting a technical taxonomy to categorize existing energy-efficient DSS models for industrial and smart environments in IoT ecosystems; (2) recognizing significant research trends in the field of energy-aware DSS strategies for intelligent urban computing in IoT and (3) presenting a technical and statistical analysis of reviewed studies and evaluation factors. Finally, innovations as forthcoming issues and new challenges of energy-based DSS strategies for intelligent urban computing in IoT are presented.

Industrial autonomous systems: a survey on definitions, characteristics and abilities

Abstract Autonomous systems are increasingly discussed in the domain of industrial automation. However, there is no consensus in the literature about the definition of industrial autonomous systems, what characteristics do they possess and how to distinguish them from intelligent industrial automation systems. Another important aspect is the comparison between the degree of automation and autonomy. Addressing this confusion, this paper starts with an analysis of the historical development of the term autonomy. Based on the similarities in the definitions, that were identified in a literature review, a comprehensive definition of industrial autonomous systems is presented, and four main characteristics of an industrial autonomous system are derived. Consequently, the characteristics for the realization of an industrial autonomous system (1) systematic process execution, (2) adaptability, (3) self-governance and (4) self-containedness are described in detail. Another objective of this contribution is to assist the developers of industrial autonomous systems to take the above-mentioned characteristics into account. Furthermore, the developer is advised which abilities can be used to achieve the corresponding essential characteristics of industrial autonomous systems. To illustrate the characteristics and abilities of an industrial autonomous system, this paper gives a detailed description of three realized cases of application in industry.

Adaptive neuro-fuzzy modeling of a soft finger-like actuator for cyber-physical industrial systems

Soft robotics is a trending area of research that can revolutionize the use of robotics in industry 4.0 and cyber-physical systems including intelligent industrial systems and their interactions with the human. These robots have notable adaptability to objects and can facilitate many tasks in everyday life. One potential use of these robots is in medical applications. Due to the soft body of these robots, they are a suitable replacement for applications like rehabilitation and exoskeletons. In this paper, we present the neuro-fuzzy modeling of a soft pneumatic finger-like actuator. This actuator is a fiber-reinforced soft robot with the shape and dimensions of a real finger and moves in planar motion. A bending sensor is used as a feedback for curvature motion of this actuator. In order to model this actuator, an adaptive neuro-fuzzy inference system is utilized to overcome the hardship in the modeling of the nonlinear performance of the soft materials. An experimental setup is designed to obtain suitable input–output data needed for modeling. The results show the applicability of the utilized method in the modeling of the soft actuator.

Evaluation and Build to honeypot System about SCADA Security for Large-Scale IoT Devices

Under the trend of intelligent industrial control systems, it is very important to prevent network hackers from invading the system to attack devices. In particular, today’s hackers have multiple attacks and the number of attacks has increased year-by-year. How to stop hackers from attacking the system has become the main goal of this research. In this study, we explore how to build and evaluate Arduino and Raspberry Pi were used as real-world programmable logic controllers and camouflaged honeypots to simulate the honeymap of the industrial control system. And simulate the common sensors of industrial control systems, such as temperature, humidity, pressure sensors, to send the same type of signal as the real sensors to deceive the hacker, so the hacker will attack the simulated sensor with a high probability. After receiving the hacker’s attack signal, returns the spoofed attack result, which makes the hacker mistakenly think that the attack is successful, and no longer causes further damage to the system, and achieves the purpose of protecting the system. At the same time, record the attack means by honeypot to prevention other similar attacks.

Decision analytics mobilized with digital coaching

SummaryThe context to be addressed is the digitalization of industry and industrial processes. Digitalization brings enhanced customer relationships and value‐chain integration, which are effective instruments to meet increasing competition and slimmer margins for productivity and profitability. Digitalization also brings more pronounced requirements for effective planning, problem solving and decision making in an increasingly complex and fast‐changing environment. Decision analytics will meet the challenges from the growing global competition that major industrial corporations face and will help solve the problems of big data/fast data that digitalization is generating as a by‐product. A mantra is appearing in business magazines – that powerful, intelligent systems will be effective tools for the digitalization of industrial processes – but much less attention appears to be paid to the fact that users need advanced knowledge and skills to benefit from the intelligent systems. First, an effective transfer of knowledge from developers, experts and researchers to users (including management) will be needed; second, the daily use and operations of the systems need to be supported, as automated, intelligent industrial systems are complex to operate. We look at this transfer as knowledge mobilization and will work out how the mobilization can be supported with coaching; this coaching needs to be digital, as human coaches are both scarce and too expensive to employ in large numbers.