With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment monitoring, process optimisation, and efficiency enhancement. Industrial data exhibit characteristics such as multi-source heterogeneity, nonlinearity, strong coupling, and temporal interactions, while also being affected by noise interference. These complexities make it challenging for traditional anomaly detection methods to extract key features, impacting detection accuracy and stability. Traditional machine learning approaches often struggle with such complex data due to limitations in processing capacity and generalisation ability, making them inadequate for practical applications. While deep learning feature extraction modules have demonstrated remarkable performance in image and text processing, they remain ineffective when applied to multi-source heterogeneous industrial data lacking explicit correlations. Moreover, existing multi-source heterogeneous data processing techniques still rely on dimensionality reduction and feature selection, which can lead to information loss and difficulty in capturing high-order interactions. To address these challenges, this study applies the EAPCR and Time-EAPCR models proposed in previous research and introduces a new model, Time-EAPCR-T, where Transformer replaces the LSTM module in the time-series processing component of Time-EAPCR. This modification effectively addresses multi-source data heterogeneity, facilitates efficient multi-source feature fusion, and enhances the temporal feature extraction capabilities of multi-source industrial data.Experimental results demonstrate that the proposed method outperforms existing approaches across four industrial datasets, highlighting its broad application potential.

Navigating densely vegetated environments poses significant challenges for autonomous ground vehicles. Learning-based systems typically use prior and in-situ data to predict terrain traversability but often degrade in performance when encountering out-of-distribution elements caused by rapid environmental changes or novel conditions. This paper presents a novel, lidar-only, online adaptive traversability estimation (TE) method that trains a model directly on the robot using self-supervised data collected through robot-environment interaction. The proposed approach utilises a probabilistic 3D voxel representation to integrate lidar measurements and robot experience, creating a salient environmental model. To ensure computational efficiency, a sparse graph-based representation is employed to update temporarily evolving voxel distributions. Extensive experiments with an unmanned ground vehicle in natural terrain demonstrate that the system adapts to complex environments with as little as 8 minutes of operational data, achieving a Matthews Correlation Coefficient (MCC) score of 0.63 and enabling safe navigation in densely vegetated environments. This work examines different training strategies for voxel-based TE methods and offers recommendations for training strategies to improve adaptability. The proposed method is validated on a robotic platform with limited computational resources (25W GPU), achieving accuracy comparable to offline-trained models while maintaining reliable performance across varied environments.

With the development of intelligent manufacturing and the increasing complexity of industrial production, root cause diagnosis has gradually become an important research direction in the field of industrial fault diagnosis. However, existing research methods struggle to effectively combine domain knowledge and industrial data, failing to provide accurate, online, and reliable root cause diagnosis results for industrial processes. To address these issues, a novel fault root cause diagnosis framework based on knowledge graph and industrial data, called Root-KGD, is proposed. Root-KGD uses the knowledge graph to represent domain knowledge and employs data-driven modeling to extract fault features from industrial data. It then combines the knowledge graph and data features to perform knowledge graph reasoning for root cause identification. The performance of the proposed method is validated using two industrial process cases, Tennessee Eastman Process (TEP) and Multiphase Flow Facility (MFF). Compared to existing methods, Root-KGD not only gives more accurate root cause variable diagnosis results but also provides interpretable fault-related information by locating faults to corresponding physical entities in knowledge graph (such as devices and streams). In addition, combined with its lightweight nature, Root-KGD is more effective in online industrial applications.

The recent emergence of large language models (LLMs) shows the potential for artificial general intelligence, revealing new opportunities in industry 4.0 and smart manufacturing. However, a notable gap exists in applying these LLMs in industry, primarily due to their training on general knowledge rather than domain-specific knowledge. Such specialized domain knowledge is vital for effectively addressing the complex needs of industrial applications. To bridge this gap, this paper proposes an Industrial Large Knowledge Model (ILKM) framework emphasizing their potential to revolutionize the industry in smart manufacturing. In addition, ILKMs and LLMs are compared from eight perspectives. Finally, "6S Principle" is proposed as the guideline for the development of ILKMs in smart manufacturing.

All industries have one thing in common, data and lots of it. The amount of data is related to the volume of'things' now connected to the internet, from personal devices, the office printer, all the way to the sensor on a pump that is helping generate the electricity necessary to keep the power on. They say data is the new oil; however, far too many industrial companies are finding little to no use or benefit from all the data they are generating. In fact, the Mining and Resources sector is reported to use less than 1 percent of the data collected from their equipment. So how do companies ensure they are getting the most value from the data generated, and how do we ensure that the project is a success and doesn't become another statistic in the 70 percent of all digital transformations that fail?

What is your industrial AI readiness? That's a question that's top-of-mind for many industrial executives lately -- and simultaneously one that has not taken on enough importance for many others. While AI, machine learning and other means of automation have swept through industries, including the industrial sector, in recent years, AI still too often gets treated as an add-on technology. But AI isn't something to be tacked onto an existing framework; it has to be treated as the strategy itself. This is especially true for industrial AI.

Domain-specific solutions, a lowered barrier to AI adoption, and an emphasis on industrial data value are all benefits industrial AI brings to the table. The years-long trend toward Industry 4.0, coupled with the pressures of 2020's economic climate, have made one thing clear: the need to adopt artificial intelligence (AI) isn't just accelerating; it's become critical. And while many industrial organizations still lack expertise in and experience with industrial AI, the ability to adopt and implement it will very quickly become a matter of survival. So much so that 2021 is poised to be a defining year, literally, for a new breed of AI: Industrial AI. While both startups and legacy industrial companies alike have made their own plays around industrial AI, the new year will mark the first time that industrial AI as a new industry category begins to take on a mainstream role.

The latest Industrial revolution has helped industries in achieving very high rates of productivity and efficiency. It has introduced data aggregation and cyber-physical systems to optimize planning and scheduling. Although, uncertainty in the environment and the imprecise nature of human operators are not accurately considered for into the decision making process. This leads to delays in consignments and imprecise budget estimations. This widespread practice in the industrial models is flawed and requires rectification. Various other articles have approached to solve this problem through stochastic or fuzzy set model methods. This paper presents a comprehensive method to logically and realistically quantify the non-deterministic uncertainty through probabilistic uncertainty modelling. This method is applicable on virtually all Industrial data sets, as the model is self adjusting and uses epsilon-contamination to cater to limited or incomplete data sets. The results are numerically validated through an Industrial data set in Flanders, Belgium. The data driven results achieved through this robust scheduling method illustrate the improvement in performance.

The European Commission's new digital plan proposes the creation of so-called data spaces, where industrial data can be shared across sectors. While this would be an important step towards a single market for good quality, interoperable data, it remains to be seen whether the EU member states will be able to reach a consensus on the plan and unlock the required funding. The Commission sees the limited access to data held by both public and private actors as one of the main obstacles to the development of AI technologies in Europe. As such, the idea of data spaces is an important step towards strengthening the EU's position in AI. The more data that is shared, especially among businesses, the more sophisticated algorithms can become.

--In this paper, a method of prediction on continuous time series variables from the production or flow - an LSTM algorithm based on multivariate tuning - is proposed. The algorithm improves the traditional LSTM algorithm and converts the time series data into supervised learning sequences regarding industrial data's features. The main innovation of this paper consists in introducing the concepts of periodic measurement and time window in the industrial prediction problem, especially considering industrial data with time series characteristics. Experiments using real-world datasets show that the prediction accuracy is improved, 54.05% higher than that of traditional LSTM algorithm. In industry, with the high-speed functioning of the enterprise product line, data are generated continuously. Malfunctions and abnormality often take place, which incur a great deal of money and resources, and even advanced equipment cannot avoid these problems[1]. Industrial companies have to pay a lot to maintain and ensure the normal operation of the manufacturing process. According to the statistics, the maintenance costs of all kinds of industrial enterprises account for about 15%-70% of total production costs[2]. Flow prediction is motivated by such industrial conundrums faced by many factories. Implementing flow prediction to forecast the output of the machine and to detect the problems in time via prediction, not only can production increase, but also a large number of workforce and resources for troubleshooting can be saved.