相关论文: Using Big Data to Enhance the Bosch Production Lin…
In this work, we study the use of logistic regression in manufacturing failures detection. As a data set for the analysis, we used the data from Kaggle competition Bosch Production Line Performance. We considered the use of machine…
Nowadays all industrial sectors are increasingly faced with the explosion in the amount of data. Therefore, it raises the question of the efficient use of this large amount of data. In this research work, we are concerned with process and…
While manufacturers have been generating highly distributed data from various systems, devices and applications, a number of challenges in both data management and data analysis require new approaches to support the big data era. These…
At the heart of smart manufacturing is real-time semi-automatic decision-making. Such decisions are vital for optimizing production lines, e.g., reducing resource consumption, improving the quality of discrete manufacturing operations, and…
Batch processes show several sources of variability, from raw materials' properties to initial and evolving conditions that change during the different events in the manufacturing process. In this chapter, we will illustrate with an…
In the current competitive world, industrial companies seek to manufacture products of higher quality which can be achieved by increasing reliability, maintainability and thus the availability of products. On the other hand, improvement in…
The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the…
Fault detection in industrial plants is a hot research area as more and more sensor data are being collected throughout the industrial process. Automatic data-driven approaches are widely needed and seen as a promising area of investment.…
Currently, data collection on the shop floor is based on individual resources such as machines, robots, and Autonomous Guided Vehicles (AGVs). There is a gap between this approach and manufacturing orchestration software that supervises the…
The increasing capabilities of machine learning models, such as vision-language and multimodal language models, are placing growing demands on data in automotive systems engineering, making the quality and relevance of collected data…
The prompt and accurate detection of faults and abnormalities in electric transmission lines is a critical challenge in smart grid systems. Existing methods mostly rely on model-based approaches, which may not capture all the aspects of…
Industry 4.0 offers opportunities to combine multiple sensor data sources using IoT technologies for better utilization of raw material in production lines. A common belief that data is readily available (the big data phenomenon), is…
Database indexes facilitate data retrieval and benefit broad applications in real-world systems. Recently, a new family of index, named learned index, is proposed to learn hidden yet useful data distribution and incorporate such information…
Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A…
Design and operation of complex engineering systems rely on reliability optimization. Such optimization requires us to account for uncertainties expressed in terms of compli-cated, high-dimensional probability distributions, for which only…
Digitization and data-driven manufacturing process is needed for today's industry. The term Industry 4.0 stands for today industrial digitization which is defined as a new level of organization and control over the entire value chain of the…
Ensuring consistent product quality in modern manufacturing is crucial, particularly in safety-critical applications. Conventional quality control approaches, reliant on manually defined thresholds and features, lack adaptability to the…
In today's rapidly evolving landscape of automation and manufacturing systems, the efficient resolution of productivity losses is paramount. This study introduces a data-driven ensemble approach, utilizing the cyclic multivariate time…
Big Data is reforming many industrial domains by providing decision support through analyzing large data volumes. Big Data testing aims to ensure that Big Data systems run smoothly and error-free while maintaining the performance and…
Systems tend to become more and more complex. This has a direct impact on system engineering processes. Two of the most important phases in these processes are requirements engineering and quality assurance. Two significant complexity…