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Predictive maintenance (PdM) uses data analysis to identify operational anomalies and potential equipment defects, enabling timely repairs before failures occur. It aims to minimize maintenance frequency, avoiding unplanned outages and unnecessary preventive maintenance (opens in new tab) costs.
In this article, we explore 30 different examples of how predictive maintenance is making a significant impact across a variety of sectors. Predictive maintenance is the monitoring of equipment and assets in real-time to identify signs of wear and tear that could potentially lead to failures.
Predictive maintenance is changing how factories work by keeping machines running better and stopping surprise breakdowns. Special sensors that detect shaking can tell when motors aren’t working right, warning workers early.
Predictive maintenance is conducted and executed by AI to improve productivity and efficiency. For example, since the AI can predict when a machine will break down, it can plan maintenance efforts where they are needed, effectively reducing long-term repair costs.
Predictive maintenance is essential in many industries, including the manufacturing, transportation, healthcare, energy, oil and gas, and aviation sectors. In this article, we will explore the benefits and applications of predictive maintenance and their usage in various industries.
Predictive maintenance builds on condition-based monitoring to optimize the performance and lifespan of equipment by continually assessing its health in real time. By collecting data from sensors and applying advanced analytical tools and processes such as machine learning (ML).
A predictive maintenance strategy can save maintenance teams time and money, letting them fix potential problems before they interfere with your operation and helping them avoid unplanned downtime. This blog post explains what predictive maintenance is, how it works, and five examples of predictive maintenance in the real world.