Description: Artificial Intelligence for Predictive Maintenance refers to the use of algorithms and machine learning models to anticipate failures in machinery and industrial equipment before they occur. This technology is based on the analysis of historical and real-time data, allowing companies to optimize their maintenance processes. By collecting data from sensors, maintenance logs, and operating conditions, AI systems can identify patterns and trends that indicate a potential failure. This not only helps reduce unplanned downtime but also minimizes costs associated with reactive maintenance. In the context of Industry 4.0, where digitalization and connectivity are fundamental, artificial intelligence becomes a key tool for improving operational efficiency and extending asset lifespan. The implementation of these solutions enables companies to adopt a more proactive approach to maintenance management, transforming the way resources are managed and strategic decisions are made in the industrial sector.
History: Artificial intelligence applied to predictive maintenance began to take shape in the 1980s when companies started using expert systems for maintenance decision-making. However, it was from the 2010s, with the rise of Big Data and machine learning, that this technology became established. The evolution of IoT (Internet of Things) sensors has enabled massive real-time data collection, driving the development of more accurate and efficient predictive models.
Uses: Artificial intelligence for predictive maintenance is used in various industries, including manufacturing, energy, transportation, and healthcare. Its applications include monitoring machinery conditions, predicting failures in critical equipment, optimizing maintenance schedules, and improving resource planning. Additionally, it enables companies to conduct risk analysis and prioritize maintenance interventions based on data.
Examples: An example of artificial intelligence for predictive maintenance is GE’s Predix system, which uses data analytics to predict failures in gas turbines. Another case is Siemens, which implements AI solutions in its factories to optimize machinery maintenance. In the railway sector, Hitachi uses machine learning algorithms to anticipate issues in trains and improve operational safety and efficiency.
