The Role of AI-Based Predictive Maintenance Solutions in U.S. Manufacturing: Techniques and Real-World Applications
Keywords:
Predictive Maintenance, ManufacturingAbstract
Manufacturing plays a pivotal role in the U.S. economy, contributing approximately $2.08 trillion to gross domestic product (GDP) in 2021 and employing 8.9 percent of the total U.S. workforce. However, unplanned equipment downtime is a pervasive and costly challenge for manufacturers, resulting in an annual loss of $50 billion. Predictive maintenance has emerged as a solution to this widespread issue. As a data-driven approach to maintenance, it leverages sensor data and machine learning to identify potential equipment failures before they occur [1]. Unlike reactive maintenance, which addresses issues after they occur, and preventive maintenance, which performs upkeep on a schedule without regard for an asset’s condition, predictive maintenance relies on condition monitoring. This involves gathering data from equipment sensors and using it to produce actionable insights [2]. Predictive maintenance has gained traction in various enterprises, from energy and electric utilities to transportation and logistics systems. However, despite its promise, the adoption of predictive maintenance solutions in manufacturing lags behind other industries. This is due to challenges such as low data availability, difficulty instrumenting machinery with sensors, organizational resistance to technological change, lack of employee expertise, and supply chain investment hurdles. To accelerate the adoption of predictive maintenance solutions across the manufacturing sector, this research seeks to identify representative predictive maintenance techniques, along with well-documented U.S. manufacturing use cases that highlight their real-world application and effectiveness.
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