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Maintenance processes require complex planning and dispatching efforts and are a major lifecycle cost. However, the trend is shifting toward carrying out diagnostic activities that are more time- and cost-efficient—that is, implementing mathematical modeling tools that not only predict when an issue is likely to occur but can also help align the maintenance needs with the operation services. The key difference between traditional preventive and predictive maintenance is that preventive maintenance is scheduled regularly, but predictive maintenance occurs on an as-needed basis.
For this reason, maintenance practices are shifting toward this new approach. Rather than conventional reactive or preventive maintenance, which in both cases cost more time and money, and in some cases, occur after the damage has occurred, predictive maintenance minimizes the cost of maintenance and operation downtime. AI’s machine learning leverages vast amounts of data to identify trends and draw inferences. For this reason, the field equipment is fitted with sensors that provide numerous amounts of data on system performance.
By continuously monitoring the devices and collecting the data, machine learning algorithms predict system performance and degradation. These predictions serve as early indicators of potential system failures, enabling proactive repairs. This transformative practice, known as predictive maintenance, is reshaping the manufacturing sector.
Siemens Easy Share enables technicians in the field, who traditionally relied on service logs and manuals for maintenance, to point a tablet at the area of concern so that AI can identify what part they are looking at and provide all the contextual information they need, such as manuals, the circuit diagrams, or a catalog of spare parts, which they can order with one click—for instance, in “Siemens Easy Spares.”
Full Article: IEEE Vehicular Technology Magazine, Volume 20, Number 3, September 2025
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