Cognitive machine learning techniques for predictive maintenance in industrial systems: A data-driven analysis

Abstract

This paper delves into the intricate relationship between machine learning (ML) and data analysis, spotlighting the recent advancements, prevailing challenges, and emerging opportunities that underscore their integration. By conducting an extensive review of scholarly literature and real-world case studies, this article uncovers the synergistic potential of ML and data analysis, emphasizing their combined influence across diverse industries and domains. The exploration is framed around pivotal themes including algorithmic innovations, which are at the heart of ML's ability to transform vast and complex datasets into actionable insights. Moreover, the discussion extends to predictive modeling techniques, a cornerstone of data analysis that leverages historical data to forecast future trends, behaviors, and outcomes. Practical applications are scrutinized to demonstrate how the confluence of ML and data analysis is pioneering solutions in fields as varied as healthcare, where predictive analytics can save lives, to finance, where it is used to navigate market uncertainties. This paper also addresses the barriers to effective integration, such as data privacy concerns and the need for robust data governance frameworks. Through this comprehensive examination, the article sheds light on the rapidly evolving landscape of ML-driven data analysis, offering insights into how these technological advancements are reshaping research methodologies, industry practices, and societal norms.