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"Industrial equipment"
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Robotic nondestructive testing technology
\"This book introduces a variety of non-destructive testing (NDT) methods, including testing and application cases. New ultrasonic testing technology for complex workpieces is proposed\"-- Provided by publisher.
BOOK
Research on the Application of Artificial Intelligence Technology in Intelligent Operation and Maintenance of Industrial Equipment and System
With the rapid development of network communication technology, artificial intelligence (hereinafter referred to as AI) technology has been deeply optimized, which has been applied to all walks of life, especially industrial equipment system. At present, industrial automation equipment is reasonable judgment through algorithm, which is inseparable from AI technology. Through AI technology, modern industry has undergone earth shaking changes, which constantly promote the traditional industrial structure adjustment and optimization and upgrading. This is the continuous upgrading of the industrial structure and the promotion of AI. According to the architecture of reliability, availability, maintainability and safety, the security and availability of the system are guaranteed by reliability and maintainability, which requires us to improve the reliability of operation and maintenance of the system. This paper first analyzes the importance of AI technology in the industrial field. Then, this paper analyzes the Metro signal equipment system architecture. Finally, some key technologies are proposed.
Journal Article
A Non-Intrusive Load Decomposition Model Based on Multiple Electrical Parameters to Point
The sliding window method is commonly used for non-intrusive load disaggregation. However, it is difficult to choose the appropriate window size, and the disaggregation effect is poor in low-frequency industrial environments. To better handle low-frequency industrial load data, in this paper, we propose a vertical non-intrusive load disaggregation model that is different from the sliding window method. By training multiple electrical parameters at a single point on the bus end with the corresponding load data at the branch end, the proposed method, called multiple electrical parameters to point (Mep2point), takes the electrical parameter data sampled at a single point on the bus end as its input and outputs the load data of the target device sampled at the corresponding point. First, the electrical parameters of the bus end are processed, and each item is normalized to the range from 0–1. Then, the electrical parameters are vertically arranged by their time point, and a convolutional neural network (CNN) is used to train the model. The proposed method is analyzed on low-frequency industrial user data sampled at a frequency of 1/120 Hz in the real world. We compare our method with three advanced sliding window methods, achieving an average improvement ranging from 9.23% to 22.51% in evaluation metrics, while showing substantial superiority in the actual decomposed images. Compared with three classical machine learning algorithms, our model, using the same amount of data, significantly outperforms these methods. Finally, we also compared our method with the multi-channel low window sequence-to-point (MLSP) method, which also selects multiple electrical parameters. Our model’s complexity is much less than that of the MLSP model, and its performance remains high. The superiority of our model, as presented in this paper, is fully verified by experimental analysis, which can produce better actual load decomposition results from each branch and contribute to the analysis and monitoring of loads in industrial environments.
Journal Article
The rise of industry (1700-1800)
In only a few decades, new materials, new machines, new sources of power, and new methods of transportation changed the face of the world. Mines, furnaces, and mills formed the basis of towns where the routines of the natural world were subject to the rhythms of the factory. This book explores the innovations of the 18th century and how they changed the world forever.
Book
Vintage industrial : living with machine age design
\"An exquisitely illustrated celebration of this influential style that is now at the forefront of interior design. Vintage Industrial covers the period from 1900 to 1950, which produced the raw, functional aesthetic that has become a cornerstone of modern design. The advent of the second industrial revolution created the need for a new kind of furniture to satisfy the demands of a rapidly growing workforce. Chairs, tables, lamps, and modular storage were designed from new materials to be mass-produced, stackable, and adjustable to the developing needs of brand-new industries that in turn were manufacturing the products that would define a changing society. These pieces, that inform a reclaimed style, are now highly popular among collectors and interior designers. This volume celebrates the engineers who shaped the industrial aesthetic as the unsung heroes of modern design and showcases their creations. By discovering ways to work iron and steel into functional forms, luminaries such as Bernard-Albin Gras, George Carwardine, Jean Prouvâe, and âEdouard-Wilfred Buquet sparked a revolution in the way we think about our built environment. Five chapters--on lighting, seating, tables, storage, and curiosities--describe the major innovations and designs from the period and include stunning photography depicting these objects in homes, workshops, factories, and warehouses. Meticulously curated, this elegant book is an informative style guide and source of inspiration for how to live with industrial design.\"-- Provided by publisher.
Book
Implementation of Digital Twin in Actual Production: Intelligent Assembly Paradigm for Large-Scale Industrial Equipment
The assembly process of large-scale and non-standard industrial equipment poses significant challenges due to its inherent scale-related complexity and proneness to errors, making it difficult to ensure process cost, production cycle, and assembly accuracy. In response to the limitations of traditional ineffective production models, this paper aims to explore and propose a digital twin (DT)-based technology paradigm for the intelligent assembly of large-scale and non-standard industrial equipment, focusing on both the equipment structure and assembly process levels. The paradigm incorporates key technologies that facilitate the integration of virtual and physical information, including the establishment and updating of DT models for assembly structures using actual data, the assessment of structural assemblability based on DT models, the planning and simulation of assembly processes, and the implementation of virtual commissioning technology tailored to the actual assembly process. The effectiveness of the proposed paradigm is demonstrated through a case study involving the actual assembly of a large-scale aerodynamic experimental equipment. The results confirm its ability to provide valuable technical support for the design, evaluation, and optimization of industrial equipment assembly processes. By leveraging the DT-based methodological system proposed in this paper, significant improvements in the transparency and intelligence of industrial equipment production processes can be achieved.
Journal Article