Journal of Industrial Information Integration最新文献

As one of the world's largest energy consumers, China has paid much attention to the development of non-fossil energy sources. The nuclear power is regarded as the top priority for development due to its remarkable ecological and economic advantages. Given the large investment, long lifecycle, and rigorous quality control, the conceptual design plays a critical role in the pressurized water reactor development. Multitudinous design alternatives are presented at this stage and it is essential to develop an advanced evaluation approach. Hence, this work proposes a multi-criteria decision-making approach for pressurized water reactor based on hesitant fuzzy-improved cumulative prospect theory and 2-additive fuzzy measure. Firstly, considering the inherent uncertainty and cognitive biases of nuclear power experts, cumulative prospect values are calculated for design alternatives by adopting dual prospect reference points and two-tuple entropy measure under a hesitant fuzzy environment. Secondly, a linear programming model based on bidirectional projection measures is constructed to eliminate the discordance between the independent criteria assumption and interdependent evaluation information. This model helps to identify optimal 2-additive fuzzy measures, which serve as the basis for determining the Shapley importance and interaction indices of evaluation criteria. Then, taking Shapley interaction indices modification into account, a quadratic programming model based on the global criterion method is built. Finally, 2-additive Choquet integral-based TOPSIS method is proposed to select the optimal design alternative. A case study on the essential service water system is implemented to demonstrate the reliability and superiority of the proposed approach.

Industrial device scanners allow anyone to scan devices on private networks and the Internet. They were intended as network security tools, but they are commonly exploited as attack tools, as scanning can reveal vulnerable devices. However, from a defensive perspective, this vulnerability disclosure could be used to secure devices if characteristics such as type, model, manufacturer, and firmware could be identified. Automated scanning reports can help to apply security measures before an attacker finds a vulnerability. A complete device recognition procedure can then be seen as the basis for auditing networks and identifying vulnerabilities to mitigate cyber-attacks, especially among Industrial Internet of Things (IIoT) devices that are part of critical systems. In this survey, considering SCADA (Supervisory Control and Data Acquisition) systems as monitoring and control components of essential infrastructure, we focus on analyzing the architectures, specifications, and constraints of several industrial device scanners. In addition, we examine the information revealed by the scanners to identify the threats posed by them on industrial systems and networks. We analyze monthly and yearly statistics of cyber-attack incidents to investigate the role of these scanners in accelerating attacks. By presenting the findings of an experimentation, we highlight how easily anyone could identify hundreds of Internet-connected industrial devices in Sweden, which could lead to a major service interruption in industrial environments designed for minimal human involvement. We also discuss several methods to avoid scanners or reduce their identifying capabilities to conceal industrial devices from unauthorized access.

Considering increasingly serious environmental issues, sustainable development and green manufacturing have received much attention. Meanwhile, with the development of economic globalization and requirement of customization production, distributed hybrid flowshop scheduling problem (DHFSP) and assembly shop problem (ASP) have widely existed in realistic manufacturing systems. In addition to machine resources, worker resources are a key element affecting production efficiency. However, previous studies have not considered the integration mode of DHFSP, ASP, and worker resources in green manufacturing systems. Therefore, this paper focuses on an energy-efficient distributed assembly hybrid flowshop scheduling problem considering worker resources (EDAHFSPW) for the first time. To solve this problem, a mixed-integer linear programming (MILP) model and a multi-objective memetic algorithm (MOMA) are proposed with minimization the total tardiness ($TTD$) and total energy consumption ($TEC$) objectives. In MOMA, a speed-related decoding method is developed to improve the quality of solutions. To generate excellent initial solutions, an initialization strategy is proposed based on problem characteristics. A local search strategy is presented to improve the exploitation capability. An energy-saving strategy is designed to further optimize $TEC$. Additionally, to validate the proposed MILP model, we implement CPLEX to solve it on 12 small-sized instances. To verify the effectiveness of the proposed MOMA, extensive experiments are conducted to compare with other 5 comparison algorithms on 90 large-sized instances. Experimental results illustrate that MOMA is superior to its competitors.

Kernel principal component analysis (KPCA) is a well-recognized nonlinear dimensionality reduction method that has been widely used in nonlinear fault detection tasks. As a kernel trick-based method, KPCA inherits two major problems. First, the form and the parameters of the kernel function are usually selected blindly, depending seriously on trial-and-error. As a result, there may be serious performance degradation in case of inappropriate selections. Second, at the online monitoring stage, KPCA has much computational burden and poor real-time performance, because the kernel method requires to leverage all the offline training data. In this work, to deal with the two drawbacks, a learnable faster realization of the conventional KPCA is proposed. The core idea is to parameterize all feasible kernel functions using the novel nonlinear DAE-FE (deep autoencoder based feature extraction) framework and propose DAE-PCA (deep autoencoder based principal component analysis) approach in detail. The proposed DAE-PCA method is proved to be equivalent to KPCA but has more advantage in terms of automatic searching of the most suitable nonlinear high-dimensional space according to the inputs, which helps to improve the accuracy of fault detection. Furthermore, the online computational efficiency improves by many times compared with the conventional KPCA. Finally, the Tennessee Eastman (TE) process benchmark and wastewater treatment plant (WWTP) benchmark are employed to illustrate the effectiveness of the proposed method, where the average fault detection rates of DAE-PCA are at least 0.27% and 4.69% higher than those of other methods, and its online computational efficiency is faster 90.48% and 24.57% times than that of KPCA respectively.

Real-time monitoring solutions have gained popularity across industries due to the advent of Industry 4.0, AI, and big data enhancing the efficiency of industrial production and equipment decisions. Machine learning models that possess computing intelligence and interpretability provide superior predictive capabilities compared to manual adjustments, resulting in cost savings and manufacturing high-quality products. This study proposes a zero-defect manufacturing decision support system based on computational intelligence feature selection combined with interpretable machine learning. The decision support system integrates Particle Swarm Optimization (PSO) and the C4.5 decision tree method, abbreviated as PSO+C4.5, to enable the continuous monitoring of the injection molding process in real-time, considering production parameter information and collected data quality, guiding the decision-making process for implementing zero-defect manufacturing (ZDM). In contrast to existing research, our innovative methodology relies on computational intelligence techniques for extracting features and employs interpretable machine learning prediction models. In terms of quality prediction, our empirical findings show that the suggested method accomplishes the optimal balance between interpretability and predictive performance (Accuracy: 0.9889, Sensitivity: 0.9869, and Specificity: 0.9935). These characteristics can directly support maintenance personnel and operators in optimizing the processing quality process.

Industrial information integration engineering (IIIE) is an interdisciplinary field to facilitate the industrial information integration process. In the age of complex and large-scale systems, model-based systems engineering (MBSE) is widely adopted in industry to support IIIE. Traceability management is considered the foundation of information management in MBSE. However, a lack of integration between stakeholders, development processes, and models can decrease the effectiveness and efficiency of the system development. A modified MBSE toolchain prototype has been developed to implement traceability management; however, a lack of formal and structured specifications makes it difficult to describe the complex topology in traceability management scenarios using this MBSE toolchain, such as creating traceability between heterogeneous models, which leads to poor reusability of this MBSE toolchain in other traceability management scenarios. To formalize traceability management scenarios using the MBSE toolchain, a cognitive thread (CT) ontology is developed in this study. The CT ontology is a specification expressing the information of stakeholders, models, and development processes for traceability management, providing the cognition capability to analyze the interrelationships between them. Based on the implementation of the modified MBSE toolchain, the concepts and interrelationships in the CT ontology are identified. The CT ontology is designed to develop the MBSE toolchain prototype for building, managing, and analyzing traceability in various traceability management scenarios. A case study of an adaptive cruise control system design is used to evaluate the completeness of the CT ontology through qualitative and quantitative analyses. The results demonstrate that the proposed CT ontology formalizes the information related to traceability management while using the proposed MBSE toolchain and can also be used in common traceability management scenarios to design other complex engineered systems.

Industrial information integration engineering (IIIE) is an innovative research subject for analyzing complicated and large-scale systems. Autonomous and efficient path coverage of unmanned systems formations is an important subject of intelligent industrial agriculture. As one typical kind of complicated systems, agricultural unmanned systems formations are urgently required to optimize their operating trajectories. In this paper, an IIIE design for coverage path planning of the agricultural unmanned systems formations is presented as an IIIE application to verify the entire performances with considering the couplings between the formations and the working environment. In this design, one key concept of field-state iteration for information coupling integration is inherited and introduced in detail. Furthermore, its simulation models were developed based on structure (unmanned system agent structure and formation structure), geometry (map model and graph theory), dynamics (unmanned system agent model and formation model), and control (formation coverage path planning, formation control and trajectory recurrence) in the practice environments. The practice results were analyzed to validate the effectiveness of the proposed information integration design. Further, this paper puts forward a coverage path planning scheme for unmanned systems formations based on the rotating beam and improved probability roadmap algorithms, which can maintain 99.8% coverage rate, 0.08% repetition rate, and 0.007% redundant coverage rate while ensuring the optimal time. Then, two types of three-dimensional practice platform software including CarSim and Gazebo, are selected to graft the proposed algorithm into agricultural tractors formation and plant protection UAVs formation respectively, and the feasibility of the algorithm is verified under the condition closest to the real environment. Multiple experimentalresults demonstrate that the algorithm proposed in this paper has superior feasibility for engineering practice.

The resilient food supply chain (RFSC) has been identified as an effective model for mitigating food supply chain (FSC) risks. However, there exist many barriers impacting the implementation of the RFSC. Further, previous studies seldom utilize integrated decision models for identifying and ranking the barriers to implementing RFSC within uncertain environments. Thus, the study establishes an interval-valued Fermatean fuzzy (IVFF) decision framework to identify and rank these barriers. The framework is classified into four stages. First, to model the interaction between preference information, we introduce the IVFF-prioritized weighted average (PWA) operator to collect this information. Then, an integrated IVFF-CRITIC method is proposed to calculate the barrier weights considering their inter-correlation relationships. Next, the IVFF-PWA operator and IVFF-CRITIC method are incorporated into the PROMETHEE-II model to rank the barrier levels of alternative participation in the FSC. Further, a case study about analyzing implementation barriers to RFSC is employed to test the effectiveness and practicality of the presented framework. The result shows that the participation food processing company (priority: 0.161) has the highest barrier level. The findings of this article may offer decision support to stakeholders for mitigating the barriers to implementing a resilient supply chain in the food industry.

Sourcing and identification of new manufacturing partners is crucial for manufacturing system integrators to enhance agility and reduce risk through supply chain diversification in the global economy. The advent of advanced large language models has captured significant interest, due to their ability to generate comprehensive and articulate responses across a wide range of knowledge domains. However, the system often falls short in accuracy and completeness when responding to domain-specific inquiries, particularly in areas like manufacturing service discovery. This research explores the potential of leveraging Knowledge Graphs in conjunction with ChatGPT to streamline the process for prospective clients in identifying small manufacturing enterprises. In this study, we propose a method that integrates bottom-up ontology with advanced machine learning models to develop a Manufacturing Service Knowledge Graph from an array of structured and unstructured data sources, including the digital footprints of small-scale manufacturers throughout North America. The Knowledge Graph and the learned graph embedding vectors are leveraged to tackle intricate queries within the digital supply chain network, responding with enhanced reliability and greater interpretability. The approach highlighted is scalable to millions of entities that can be distributed to form a global Manufacturing Service Knowledge Network Graph that can potentially interconnect multiple types of Knowledge Graphs that span industry sectors, geopolitical boundaries, and business domains. The dataset developed for this study, now publicly accessible, encompasses more than 13,000 manufacturers’ weblinks, manufacturing services, certifications, and location entity types.

Structural defects account for a large proportion of defects, and acquiring large batches of high-quality labels is labor-intensive and time-consuming for industrial visual defect inspection tasks. This paper addresses the above problem by exploiting sufficient unlabeled samples, and aims to achieve superior model performance with some labeled data by using self-training methods that incorporate positional information. Specifically, this paper proposes a novel self-training architecture, MixSiam, which uses a Multi-Position-based Mix strategy (MPMix) and Siamese network structure for defect classification. Furthermore, considering the prediction noise problem in unlabeled data during training, we propose a progressive MPMix (MPMix+) strategy to reduce the negative impacts of noise on model training. Finally, we validate the effectiveness of our architecture on industrial datasets. For example, our method achieves 71.40% and 87.01% accuracy on the SMT (Surface Mounting Technology) dataset and MBH (Motor Brush Holder) dataset with only 100 labeled samples, which are 2.40% and 5.86% higher than the state-of-the-art FixMatch method, respectively. Compared with the supervised algorithm with 3,600 labels, our method achieves comparable accuracy on the SMT and MBH datasets, respectively, while saving 2/3 the amount of labeled data. In conclusion, MixSiam effectively utilizes unlabeled industrial data and improves model accuracy with fewer labeled samples, thus reducing the burden of data annotation in industrial production.