Predictive Maintenance -- Bridging Artificial Intelligence and IoT

TL;DR

This paper reviews recent trends in predictive maintenance using machine learning and IoT, highlighting dominant industries, AI models, and sensor types to optimize industrial processes.

Contribution

It provides a comprehensive analysis of the integration of AI and IoT in predictive maintenance, identifying key sectors, models, and sensors used in recent research.

Findings

Production sector dominates with 54.54% of publications.

Artificial Neural Networks are the most prevalent AI model at 27.84%.

Temperature and vibration sensors are most widely used, at 60.71% and 46.42%.

Abstract

This paper highlights the trends in the field of predictive maintenance with the use of machine learning. With the continuous development of the Fourth Industrial Revolution, through IoT, the technologies that use artificial intelligence are evolving. As a result, industries have been using these technologies to optimize their production. Through scientific research conducted for this paper, conclusions were drawn about the trends in Predictive Maintenance applications with the use of machine learning bridging Artificial Intelligence and IoT. These trends are related to the types of industries in which Predictive Maintenance was applied, the models of artificial intelligence were implemented, mainly of machine learning and the types of sensors that are applied through the IoT to the applications. Six sectors were presented and the production sector was dominant as it accounted for 54.54% of total publications. In terms of artificial intelligence models, the most prevalent among ten were the Artificial Neural Networks, Support Vector Machine and Random Forest with 27.84%, 17.72% and 13.92% respectively. Finally, twelve categories of sensors emerged, of which the most widely used were the sensors of temperature and vibration with percentages of 60.71% and 46.42% correspondingly.