AI-Powered Predictive Maintenance for Industrial IoT Systems

Abstract

Artificial Intelligence (AI) and the rise of Industrial Internet of Things (IIoT) systems have transformed the way maintenance is performed, particularly in the fields of manufacturing and energy. The study discussed here works to identify differences between predictive maintenance through artificial intelligence (AI) solutions and traditional maintenance strategies in terms of downtime reduction, cost-efficiency improvement, and equipment lifetime increase. The study developed models to predict the probability of equipment failure based on historical failure data combined with real-time sensor data from manufacturing plants and power stations in Pakistan, using machine learning algorithms including decision trees, support vector machines, and neural networks. The findings reveal that AI-based predictive maintenance can help in significantly reducing downtime – by 30%, from 120 hours down to 84 hours – vs. traditional methods. Additionally, this will result in 25% cost saving on maintenance, saving around 375,000 PKR every month. AI neural networks outperform failure prediction models in: accuracy (92%); Mean error (8%) However, it may also serve as a valuable guide in refining the design of AI-driven maintenance systems within the context of a growing body of literature on this topic. In conclusion, the paper highlights that AI-based predictive maintenance can get significant advantages compared with traditional maintenance strategies in industry.