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What is Predictive Maintenance

Empowering Sustainable Industrial 4.0 Systems With Machine Intelligence
It refers to a type of maintenance that is able to predict the failure of a component of a machine, in such a way that said component can be replaced, based on a well-determined plan, just before it fails. It allows to minimize the dead time of the equipment, while maximizing the lifetime of the component.
Published in Chapter:
How Artificial Intelligence Can Enhance Predictive Maintenance in Smart Factories
María A. Pérez-Juárez (University of Valladolid, Spain), Javier M. Aguiar-Pérez (University of Valladolid, Spain), Javier Del-Pozo-Velázquez (University of Valladolid, Spain), Miguel Alonso-Felipe (University of Valladolid, Spain), Saúl Rozada-Raneros (University of Valladolid, Spain), and Mikel Barrio-Conde (University of Valladolid, Spain)
DOI: 10.4018/978-1-7998-9201-4.ch004
The Fourth Industrial Revolution, under the name of Industry 4.0, focuses on obtaining and using data to facilitate decision-making and thus achieve a competitive advantage. Industry 4.0 is about smart factories. For this, a series of technologies have emerged that communicate the physical and the virtual world, including Internet of Things, Big Data, and Artificial Intelligence. These technologies can be applied in many areas of the industry such as production, manufacturing, quality, logistics, maintenance, or security to improve the optimization of the production capacity or the control and monitoring of the production process. An important area of application is maintenance. Predictive maintenance is focused on monitoring the performance and condition of equipment during normal operation to reduce the likelihood of failures with the help of data-driven techniques. This chapter aims to explore the possibilities of using artificial intelligence to optimize the maintenance of the machinery and equipment components so that product costs are reduced.
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More Results
Outage Analysis and Maintenance Strategies in Hydroelectric Production
A technique utilized to forecast equipment degradation to perform as-needed maintenance actions after an event occurred.
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Prediction of Remaining Useful Life of Batteries Using Machine Learning Models
It is a proactive maintenance approach that uses data analysis and predictive modelling techniques to anticipate and prevent equipment or system failures. It involves monitoring and analysing real-time or historical data from sensors, machinery, or other sources to identify patterns, trends, and early indicators of potential issues. By predicting when equipment is likely to fail, maintenance activities can be scheduled in advance, optimizing resources, and minimizing unplanned downtime. Predictive maintenance aims to maximize the operational efficiency and reliability of assets while minimizing maintenance costs and disruptions.
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Infrared Thermography for Intelligent Robotic Systems in Research Industry Inspections: Thermography in Industry Processes
Data mining techniques designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed.
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Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0
Using artificial intelligence algorithms on collected asset data to predict the next failure of a component/machine/system.
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Implication of Predictive Maintenance for Industrial Marketing: A Case Study From the Air Compressor Industry
The concept and practice of managing equipment maintenance with the help of real-time data captured through physical devices like chip sets, sensors and processors.
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Data-Driven Decision Making to Select Condition-Based Maintenance Technology
Also known as condition-based maintenance (CBM), this is based on the control of physical parameters (such as vibrations, temperature, water content of used lubricant, etc.) of a working machine that can be recorded, periodically or continuously, by a set of sensors to detect an abnormal situation, allowing necessary maintenance activities to be carried out before any failure occurs. The data obtained from the control of the parameters are analyzed to find a possible trend over time, which allows a prediction to be made about when the threshold values will be reached.
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