The manufacturing industry is rapidly changing, creating a growing demand for more intelligent and adaptive systems. With recent developments in artificial intelligence, especially with the onset of large language models (LLMs)Large Language Models (LLMs) such as ChatGPT, new opportunities have emerged for companies to increase their productivity and maximize revenue. In a competitive environment, businesses must constantly innovate to stay ahead. To support innovative and competitive organizations, LLMs can analyze large amounts of data to identify trends and optimize processes. In addition, the industry faces a labor shortage, particularly in roles that require specialized skills. LLMs can fill this gap by providing real-time assistance and training. This knowledge transfer could help less experienced workers perform their tasks more effectively. Regulatory compliance is increasingly imperative in manufacturing, and LLMs can help ensure adherence to safetySafety in manufacturing standards and regulatory requirements. LLMs can address these and other challenges by using their capabilities in data processing, natural language understanding, and predictive analytics. In this chapter, we explain the fundamental concepts behind LLM techniques and how to use them in a smart manufacturing environment such as Industry X.0Industry X.0. We discuss the challenges and future trends of LLMs in different industrial fields. We also highlight the need for LLM frameworks that can guarantee data privacy, security, and ethical usage.

Prognostics and health management is an engineering discipline that aims to support system operation while ensuring maximum safety and performance. Prognostics is a key step of this framework, focusing on developing effective maintenance policies based on predictive methods. Traditionally, prognostics models forecast the degradation process using regression techniques that approximate a mapping function from input to continuous remaining useful life estimates. These models are typically of high complexity and low interpretability. Classification approaches are an alternative solution to these types of models. We propose a predictive classification model that translates the input into discrete output variables instead of mapping the input to a single remaining useful life estimate. Each discrete output variable corresponds to a range of remaining useful life values. In other words, each output class variable represents the likelihood or risk of failure within a specific time range. We apply this model to a real-world case study involving the unscheduled and scheduled removals of a set of engine bleed valves from a fleet of Boeing 737 aircraft. The model can reach an area under the (micro-average) receiver operating characteristic curve of 72%. Our results suggest that the proposed multiclass gated recurrent unit network can provide valuable information about the different fault stages (corresponding to intervals of residual lives) of the studied valves.

Traditionally, prognostics approaches to predictive maintenance have focused on estimating the remaining useful life of the equipment. However, from an industrial point of view, the goal is often not to predict the residual life but to determine the need for a maintenance action at a given time window. This approach allows us to frame the data-driven prognostics problem as a binary classification task rather than a regression one. To address this problem, we propose in this paper to explore the relative strengths and limitations of a set of classifier approaches such as random forests, support vector machines, nearest neighbors, and deep learning techniques. We evaluate the models using metrics such as sensitivity, specificity, accuracy, receiver operating characteristic curve, and F-score. This work's novelty lies in adopting a modeling approach with a natural probabilistic interpretation of the prognostics exercise. The comparison of an extensive range of classifier models is performed on two real-world datasets from the aeronautics sector. Results indicate that deep learning classifier methods are well suited for this kind of prognostics and can outperform by a significant margin the traditional classification techniques. Importantly, the proposed modeling approach aims to generate an alternative prognostics representation that goes in line with the expectations of aeronautical engineers.