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BMW Group Plant Regensburg is setting new standards in assembly line efficiency with its groundbreaking smart analysis system, aimed at preventing unplanned stoppages and optimizing vehicle production flow.  The cutting-edge predictive maintenance solution employs artificial intelligence (AI) to proactively identify and address potential equipment faults, resulting in significant improvements in production uptime and cost savings. The smart monitoring system at BMW Group Plant Regensburg focuses on the assembly process, where vehicles are attached to mobile load carriers or skid systems. These carriers traverse production halls in a chain, and any technical fault in the conveyor systems can disrupt the assembly line, leading to increased maintenance efforts and costs.  To circumvent these issues, BMW’s innovation team developed a system capable of early fault detection, ensuring uninterrupted production. Remarkably, this monitoring system harnesses existing data from installed components and conveyor element control, eliminating the need for additional sensors or hardware. It actively evaluates various data points, including power consumption fluctuations, conveyor movement irregularities, and barcode legibility, to identify anomalies.  When such anomalies are detected, an alert is immediately sent to the maintenance control center, enabling swift action to address the issue. Project manager Oliver Mrasek emphasizes the system’s continuous operation: “The surveillance monitors at our control center run 24/7, enabling us to respond quickly to any kind of fault report and take the affected vehicle out of the cycle.” Implementation: AI-supported, standardized, and cost-effective Predictive maintenance is not just a stand-alone solution; it’s a collaborative effort. The system’s standardization, in cooperation with BMW Group’s central shopfloor management and other plant sites, facilitates its rapid deployment to BMW Group locations worldwide.  A notable advantage is its cost-effectiveness, as it doesn’t require additional sensors, with expenses limited to storage and computing power. In-house machine learning models are integrated into the system, employing heatmaps with…

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Filed under: News - @ November 28, 2023 12:10 am