Telecommunication Networks (TNs) have become the most important infrastructure for data communications over the last century. Operations and maintenance (O&M) is extremely important to ensure the availability, effectiveness, and efficiency of TN communications. Different from the popular O&M technique for IT systems (e.g., the cloud), artificial intelligence for IT Operations (AIOps), O&M for TNs meets the following three fundamental challenges: topological dependence of network components, highly heterogeneous software, and restricted failure data. This article presents TelOps, the first AI-driven O&M framework for TNs, systematically enhanced with mechanism, data, and empirical knowledge. We provide a comprehensive comparison between TelOps and AIOps, and conduct a proof-of-concept case study on a typical O&M task (failure diagnosis) for a real industrial TN. As the first systematic AI-driven O&M framework for TNs, TelOps opens a new door to applying AI techniques to TN automation.

With the ever-increasing demands on Question Answering (QA) systems for IT operations and maintenance, an efficient and supervised fine-tunable framework is necessary to ensure the data security, private deployment and continuous upgrading. Although Large Language Models (LLMs) have notably improved the open-domain QA's performance, how to efficiently handle enterprise-exclusive corpora and build domain-specific QA systems are still less-studied for industrial applications. In this paper, we propose a general and comprehensive framework based on Retrieval Augmented Generation (RAG) and facilitate the whole business process of establishing QA systems for IT operations and maintenance. In accordance with the prevailing RAG method, our proposed framework, named with RAG4ITOps, composes of two major stages: (1) Models Fine-tuning \& Data Vectorization, and (2) Online QA System Process. At the Stage 1, we leverage a contrastive learning method with two negative sampling strategies to fine-tune the embedding model, and design the instruction templates to fine-tune the LLM with a Retrieval Augmented Fine-Tuning method. At the Stage 2, an efficient process of QA system is built for serving. We collect enterprise-exclusive corpora from the domain of cloud computing, and the extensive experiments show that our method achieves superior results than counterparts on two kinds of QA tasks. Our experiment also provide a case for applying the RAG4ITOps to real-world enterprise-level applications.

In various industrial fields of human social development, people have been exploring methods aimed at freeing human labor. Constructing LLM-based agents is considered to be one of the most effective tools to achieve this goal. Agent, as a kind of human-like intelligent entity with the ability of perception, planning, decision-making, and action, has created great production value in many fields. However, the bridge O\&M field shows a relatively low level of intelligence compared to other industries. Nevertheless, the bridge O\&M field has developed numerous intelligent inspection devices, machine learning algorithms, and autonomous evaluation and decision-making methods, which provide a feasible basis for breakthroughs in artificial intelligence in this field. The aim of this study is to explore the impact of AI bodies based on large-scale language models on the field of bridge O\&M and to analyze the potential challenges and opportunities it brings to the core tasks of bridge O\&M. Through in-depth research and analysis, this paper expects to provide a more comprehensive perspective for understanding the application of intelligentsia in this field.

With the rapid development of IT operations, it has become increasingly crucial to efficiently manage and analyze large volumes of data for practical applications. The techniques of Natural Language Processing (NLP) have shown remarkable capabilities for various tasks, including named entity recognition, machine translation and dialogue systems. Recently, Large Language Models (LLMs) have achieved significant improvements across various NLP downstream tasks. However, there is a lack of specialized LLMs for IT operations. In this paper, we introduce the Owl, a large language model trained on our collected Owl-Instruct dataset with a wide range of IT-related information, where the mixture-of-adapter strategy is proposed to improve the parameter-efficient tuning across different domains or tasks. Furthermore, we evaluate the performance of our Owl on the Owl-Bench established by us and open IT-related benchmarks. Owl demonstrates superior performance results on IT tasks, which outperforms existing models by significant margins. Moreover, we hope that the findings of our work will provide more insights to revolutionize the techniques of IT operations with specialized LLMs.

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.

In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind powe has been developing in the direction of digitization and intelligence. It is of great significance to carry ou research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit fo the reduction of the operation and maintenance costs, the improvement of the power generation efficiency improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of powe equipment, and digital platforms.

As AI in manufacturing changes how machine health is monitored and managed, companies will need machine ops specialists to bridge the gap between operations and maintenance. AI in manufacturing can enable the digital transformation of the industry into a more effective, skillful, and productive version of itself. It can help enhance record-keeping, inventory management, and supply chain flow. Through machine data analysis, it can also significantly improve machine health. AI can diagnose existing problems and provide predictive insights to save manufacturers time and money on maintenance and repairs.

In the last few years, many asset-intensive organizations, particularly in the mining, power and utilities, oil and gas, and chemicals industries, have turned to industrial Internet of Things (IIoT) and cognitive technologies to help improve a critical area of their business: equipment reliability.1 Asset performance management (APM) programs, which connect data and trigger actions via systems across the business, can play a major part in driving these improvements. According to a 2018 Deloitte survey, oil and gas leaders rated the big data derived from programs such as APM as the most likely to provide the greatest business value.2 However, when asked about how digital technology can be used most effectively within their companies, those same executives ranked APM below both cost reduction in maintenance and operations as well as improvements in safety.3 This seems to reveal a pervasive and narrow view of APM that may miss the connection between asset performance, broader maintenance and operations improvements, and safety. Merely implementing APM software and digitizing existing processes is not likely to improve core operations and obtain the financial results that executive leaders desire (and investors demand).

The University of Manchester is leading a consortium to investigate advanced technologies, including robotics and artificial intelligence, for the operation and maintenance of offshore windfarms. The remote inspection and asset management of offshore wind farms and their connection to the shore is an industry which will be worth up to £2 billion annually by 2025 in the UK alone. Eighty to ninety percent of the cost of offshore operation and maintenance according to the Crown Estate is generated by the need to get site access - in essence get engineers and technicians to remote sites to evaluate a problem and decide what action to undertake. Such inspection takes place in a remote and hazardous environment and requires highly trained personnel of which there is likely to be a shortage in coming years. The £5m project will investigate the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort.

The University of Manchester is leading a consortium to investigate advanced technologies, including robotics and artificial intelligence, for the operation and maintenance of offshore windfarms. The remote inspection and asset management of offshore wind farms and their connection to the shore is an industry which will be worth up to £2 billion annually by 2025 in the UK alone. Eighty to ninety percent of the cost of offshore operation and maintenance according to the Crown Estate is generated by the need to get site access - in essence get engineers and technicians to remote sites to evaluate a problem and decide what action to undertake. Such inspection takes place in a remote and hazardous environment and requires highly trained personnel of which there is likely to be a shortage in coming years. The £5m project will investigate the use of advanced sensing, robotics, virtual reality models and artificial intelligence to reduce maintenance cost and effort.