The increasing use of smart devices has emphasized the critical role of maintenance in production activities. Interactive Electronic Technical Manuals (IETMs) are vital tools that support the maintenance of smart equipment. However, traditional IETMs face challenges such as transitioning from Graphical User Interfaces (GUIs) to natural Language User Interfaces (LUIs) and managing complex logical relationships. Additionally, they must meet the current demands for higher intelligence. This paper proposes a Maintenance Scheme Generation Method based on Large Language Models (LLM-R). The proposed method includes several key innovations: We propose the Low Rank Adaptation-Knowledge Retention (LORA-KR) loss technology to proportionally adjust mixed maintenance data for fine-tuning the LLM. This method prevents knowledge conflicts caused by mixed data, improving the model's adaptability and reasoning ability in specific maintenance domains, Besides, Hierarchical Task-Based Agent and Instruction-level Retrieval-Augmented Generation (RAG) technologies are adopted to optimize the generation steps and mitigate the phenomenon of hallucination caused by the model's Inability to access contextual information. This enhancement improves the model's flexibility and accuracy in handling known or unknown maintenance objects and maintenance scheme scenarios. To validate the proposed method's effectiveness in maintenance tasks, a maintenance scheme dataset was constructed using objects from different fields. The experimental results show that the accuracy of the maintenance schemes generated by the proposed method reached 91.59%, indicating which improvement enhances the intelligence of maintenance schemes and introduces novel technical approaches for equipment maintenance.

Deep Neural networks (DNNs), extensively applied across diverse disciplines, are characterized by their integrated and monolithic architectures, setting them apart from conventional software systems. This architectural difference introduces particular challenges to maintenance tasks, such as model restructuring (e.g., model compression), re-adaptation (e.g., fitting new samples), and incremental development (e.g., continual knowledge accumulation). Prior research addresses these challenges by identifying task-critical neuron layers, and dividing neural networks into semantically-similar sequential modules. However, such layer-level approaches fail to precisely identify and manipulate neuron-level semantic components, restricting their applicability to finer-grained model maintenance tasks. In this work, we implement NeuSemSlice, a novel framework that introduces the semantic slicing technique to effectively identify critical neuron-level semantic components in DNN models for semantic-aware model maintenance tasks. Specifically, semantic slicing identifies, categorizes and merges critical neurons across different categories and layers according to their semantic similarity, enabling their flexibility and effectiveness in the subsequent tasks. For semantic-aware model maintenance tasks, we provide a series of novel strategies based on semantic slicing to enhance NeuSemSlice. They include semantic components (i.e., critical neurons) preservation for model restructuring, critical neuron tuning for model re-adaptation, and non-critical neuron training for model incremental development. A thorough evaluation has demonstrated that NeuSemSlice significantly outperforms baselines in all three tasks.

We developed and compared Constraint Programming (CP) and Quantum Annealing (QA) approaches for rolling stock optimisation considering necessary maintenance tasks. To deal with such problems in CP we investigated specialised pruning rules and implemented them in a global constraint. For the QA approach, we developed quadratic unconstrained binary optimisation (QUBO) models. For testing, we use data sets based on real data from Deutsche Bahn and run the QA approach on real quantum computers from D-Wave. Classical computers are used to run the CP approach as well as tabu search for the QUBO models. We find that both approaches tend at the current development stage of the physical quantum annealers to produce comparable results, with the caveat that QUBO does not always guarantee that the maintenance constraints hold, which we fix by adjusting the QUBO model in preprocessing, based on how close the trains are to a maintenance threshold distance.

Daniel Faggella: So, Lorena, I want to be able to dive into these various use cases of how artificial intelligence can start to unlock the value of data in the oil and gas space, and make this really tangible. I know the first category we wanted to talk about was really around the value of subsurface data, that there's a lot of subsurface data, obviously in the oil and oil and gas domain. Lorena Pelegrín: And we see that AI or our ML can help these teams find the data and process the data much, much faster. Yeah, and I imagine a good deal of this has to do with, tell me if I'm wrong here, Lorena, but having an understanding of your company from working with you guys for a little while, I would imagine that the digitization of these myriad, somewhat chunky paper forms is one part of the process here, using some kind of optical character recognition stuff and working with historical records and maybe congealing and digitizing that. Daniel Faggella: But you let me know, Lorena, where does M&A, where does this data come in, in terms of the real value for potential M&A? Daniel Faggella: So Drone Deploy, for example, was on talking about what they do in the energy space with drones and video data to look at and inspect assets.

Scientists are building autonomous repair robots that will use AI to identify and fix potholes in UK roads. The electric, self-driving bots – which are being built by a spin-out company from the University of Liverpool called Robotiz3d – can find small cracks in the road and cover them with asphalt. Researchers say the machines, which look like a cross between a tank and a road roller, will transform road maintenance when they hit the roads in 2021, and finally offer a cost effective fix for the UK's pothole problem. Currently, no autonomous technology solutions exist to tackle potholes, which are estimated to have cost UK taxpayers more than £1 billion to fix over the last decade. Artist's impression of the autonomous road repair system, which looks part-tank, part road roller.

Generating a working (value-generating) machine learning model is not an easy task. It usually involves advanced modelling techniques and teams with scarce skills. However, this is only the first step on an even more complex task: deploying the model into production and preventing its degradation. Even being alleviated by the cloud shift, at least two-thirds of IT spent is still concentrated on maintenance-mode tasks. There is still little research about where this split holds for ML related projects or not, but my take is that this percentage will even increase significantly due to the fact that an ML workload has more "liquid" inputs and fewer control levers as shown below: In essence, maintenance is mainly driven by the level of variability and control we may have over the different components on the system.

While it may be a bit premature to expect collaborative humanoid robots to be doing anything useful in a warehouse environment, the only way we're going to make it happen is by encouraging the difficult transition between research labs and industry. The European Union is doing a pretty good job of providing support for things like this through its Horizon 2020 program, and one of the projects it's supporting is called SecondHands, intended to "design a robot that can offer help to a maintenance technician in a pro-active manner… as a second pair of hands that can assist the technician when he/she is in need of help." SecondHands is a collaboration between Ocado (a U.K. company that operates highly automated warehouses), Karlsruhe Institute of Technology (which has a bunch of experience building capable humanoid robots), and other research institutions including EPFL, UCL, and Sapienza University of Rome. Together, they're using the first prototype of the SecondHands collaborative robot, which also happens to be the sixth version of ARMAR, and one that's ready (we hope) to do something practical. ARMAR was created by Professor Tamim Asfour and his team at the High Performance Humanoid Technologies Lab (H²T) at KIT's Institute for Anthropomatics and Robotics.

The International Space Station sits at an altitude of approximately 220 miles above the Earth in this photo taken by Expedition 27 crew member Paolo Nespoli from the Soyuz TMA-20 following its undocking. Inside NASA's Ames Research Center in Silicon Valley is a test environment that simulates the International Space Station's pressurized capsules. Here, aerospace engineers test the new Astrobee intra vehicular activity (IVA) robots, which will be heading to the ISS in the spring. These robots are 1-by-1-foot cubes, with an array of LED communication lights. They can function autonomously or be remotely controlled from Houston.