The automation and intellectualization of the manufacturing processes in the iron and steel industry needs the strong support of inspection technologies, which play an important role in the field of quality control. At present, visual inspection technology based on image processing has an absolute advantage because of its intuitive nature, convenience, and efficiency. A major breakthrough in this field can be achieved if sufficient research regarding visual inspection technologies is undertaken. Therefore, the purpose of this article is to study the latest developments in steel inspection relating to the detected object, system hardware, and system software, existing problems of current inspection technologies, and future research directions. The paper mainly focuses on the research status and trends of inspection technology. The network framework based on deep learning provides space for the development of end-to-end mode inspection technology, which would greatly promote the implementation of intelligent manufacturing.

The goal of this work is to improve the generalization of remaining useful life (RUL) prognostics for wheel hub bearings. The traditional life prognostics methods assume that the data used in RUL prognostics is composed of one specific fatigue damage type, the data used in RUL prognostics is accurate, and the RUL prognostics are conducted in the short term. Due to which, a generalizing RUL prognostics method is designed based on fault signal data. Firstly, the fault signal model is designed with the signal in a complex and mutative environment. Then, the generalizing RUL prognostics method is designed based on the fault signal model. Lastly, the simplified solution of the generalizing RUL prognostics method is deduced. The experimental results show that the proposed method gained good accuracies for RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. The proposed method can process inaccurate fault signals with different kinds of noise in the actual working environment, and it can be conducted in the long term. Therefore, the RUL prognostics method has a good generalization.

The central focus of this study revolves around constructing a comprehensive load model to estimate the elasticity of electricity demand predicated on customers’ risk-averse tendencies. This model serves as the foundation for designing a proficient demand response program. Moreover, a welfare function is employed to ascertain the most optimal incentives to motivate customers to curtail their electricity consumption. Given Iran’s recent socioeconomic challenges marked by a high inflation rate and inadequate investment in power plants, data from the Iranian power system is employed to validate the efficacy of the proposed model in the numerical analysis. The findings reveal that customers exhibiting higher levels of risk aversion tend to derive greater satisfaction from consuming electricity and consequently necessitate more substantial incentives to prompt a reduction in their demand. Additionally, the results highlight that variations in electricity prices can counterbalance the impact of income increases on consumption patterns.

With growing concerns about the environment and the rising risks and costs associated with fossil fuels, today’s power systems are rapidly transitioning toward renewable energy sources. While subsidies and incentives often drove early adoption, renewable energy producers now face the challenge of competing in open electricity markets while ensuring profitability. This paper presents an AI-enhanced bidding strategy for a virtual power plant (VPP) that integrates wind and solar generation with demand response (DR) capabilities. At the core of the proposed hybrid approach is a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Long Short-Term Memory (LSTM) neural network that forecasts wind power output, PV generation, market prices, and demand profiles for the day-ahead horizon. These forecasts feed into a multi-objective optimization framework that strategically allocates DR resources and determines optimal market bids. Technically, this framework enhances grid reliability by minimizing Expected Energy Not Supplied (EENS), reducing active power losses, and lowering outage-related consumer costs. Commercially, the strategy enhances profitability by anticipating market conditions and minimizing imbalance settlement costs through optimal DR deployment. The method is validated on the IEEE 24-bus test system, demonstrating strong performance in both market outcomes and system reliability.