Generating synthetic data is a complex task that necessitates accurately replicating the statistical and mathematical properties of the original data elements. In sectors such as finance, utilizing and disseminating real data for research or model development can pose substantial privacy risks owing to the inclusion of sensitive information. Additionally, authentic data may be scarce, particularly in specialized domains where acquiring ample, varied, and high-quality data is difficult or costly. This scarcity or limited data availability can limit the training and testing of machine-learning models. In this paper, we address this challenge. In particular, our task is to synthesize a dataset with similar properties to an input dataset about the stock market. The input dataset is anonymized and consists of very few columns and rows, contains many inconsistencies, such as missing rows and duplicates, and its values are not normalized, scaled, or balanced. We explore the utilization of generative adversarial networks, a deep-learning technique, to generate synthetic data and evaluate its quality compared to the input stock dataset. Our innovation involves generating artificial datasets that mimic the statistical properties of the input elements without revealing complete information. For example, synthetic datasets can capture the distribution of stock prices, trading volumes, and market trends observed in the original dataset. The generated datasets cover a wider range of scenarios and variations, enabling researchers and practitioners to explore different market conditions and investment strategies. This diversity can enhance the robustness and generalization of machine-learning models. We evaluate our synthetic data in terms of the mean, similarities, and correlations.

Predictive maintenance (PdM) leverages artificial intelligence (AI) and data analytics to forecast equipment failures in smart manufacturing, enabling timely interventions that minimize downtime and operational costs. This literature review examines recent advancements in PdM, focusing on three interrelated dimensions: (1) data challenges and limitations, (2) role of advanced AI models, and (3) actionable decision-making with human-AI collaboration. Unlike previous studies that often address these aspects in isolation, our review synthesizes them to provide a comprehensive understanding of current capabilities and limitations. We highlight how emerging AI technologies such as generative models, large language models (LLMs), and hybrid frameworks enhance predictive accuracy, enable synthetic data generation, and support interpretable, human-centered maintenance strategies. By evaluating both strengths and gaps across existing approaches, this work offers a comprehensive foundation for developing more scalable, reliable, and adaptable PdM systems aligned with Industry 5.0 principles through the integrative data–model–human (DMH) framework.